The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text
Matthieu Meeus, Lukas Wutschitz, Santiago Zanella-Béguelin, Reza Shokri, Shruti Tople
February 2025
Privacy Preserving Machine Learning Innovation
Publications
Publications by Year
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Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Models
Zinan Lin, Tadas Baltrusaitis, Sergey Yekhanin
February 2025
Inferentially-Private Private Information
Shuaiqi Wang, Shuran Zheng, Zinan Lin, Giulia Fanti, Zhiwei Steven Wu
WWW 2025 | February 2025
MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models
Chejian Xu, Jiawei Zhang, Zhaorun Chen, Chulin Xie, Mintong Kang, Zhuowen Yuan, Zidi Xiong, Chenhui Zhang, Lingzhi Yuan, Yi Zeng, Peiyang Xu, Chengquan Guo, Andy Zhou, Jeffrey Ziwei Tan, Zhun Wang, Alexander Xiong, Xuandong Zhao, Yu Gai, Francesco Pinto, Yujin Potter, Zhen Xiang, Zinan Lin, Dan Hendrycks, Dawn Song, Bo Li
ICLR 2025 | February 2025
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Closed-Form Bounds for DP-SGD against Record-level Inference
Giovanni Cherubin, Boris Köpf, Andrew Paverd, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
USENIX Security Symposium | August 2024
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
ICML 2024 | July 2024
Spotlight
Statistic Maximal Leakage
Shuaiqi Wang, Zinan Lin, Giulia Fanti
IEEE International Symposium on Information Theory 2024 | July 2024
Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition
Edoardo Debenedetti, Javier Rando, Daniel Paleka, Fineas Silaghi, Dragos Albastroiu, Niv Cohen, Yuval Lemberg, Reshmi Ghosh, Ahmed Salem, Rui Wen, Giovanni Cherubin, Santiago Zanella-Béguelin, Robin Schmid, Victor Klemm, Takahiro Miki, Chenhao Li, Stefan Kraft, Mario Fritz, Florian Tramer, Sahar Abdelnabi, Lea Schönherr
NeurIPS 2024 | June 2024
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
ICLR 2024 | May 2024
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
Xinyu Tang, Richard Shin, Huseyin Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Robert Sim
ICLR 2024 | May 2024
Invariant Aggregator for Defending against Federated Backdoor Attacks
Xiaoya Wang, Dimitrios Dimitriadis, Oluwasanmi Koyejo, Shruti Tople
International Conference on Artificial Intelligence and Statistics, AISTATS | May 2024
Editor(s): Sanjoy Dasgupta, Stephan Mandt, Yingzhen Li
Selective Pre-training for Private Fine-tuning
Da Yu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, Huishuai Zhang
TMLR | May 2024
Summary Statistic Privacy in Data Sharing
Zinan Lin, Shuaiqi Wang, Vyas Sekar, Giulia Fanti
IEEE Journal on Selected Areas in Information Theory | March 2024
Advancing differential privacy: where we are now and future directions for real-world deployment
Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Yangsibo Huang, Matthew Jagielski, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Da Yu, Sergey Yekhanin, Huanyu Zhang, Wanrong Zhang
Harvard Data Science Review | January 2024, Vol 6(1)
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Training Private and Efficient Language Models with Synthetic Data from LLMs
Da Yu, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Janardhan (Jana) Kulkarni, Zinan Lin, Chulin Xie, Huishuai Zhang, Wanrong Zhang
NeurIPS 2023 SoLaR Workshop | December 2023
Secure Floating-Point Training
Deevashwer Rathee, Anwesh Bhattacharya, Divya Gupta, Rahul Sharma, Dawn Song
Usenix Security Symposium 2023 | August 2023
Project Florida: Federated Learning Made Easy
Daniel Madrigal, Andre Manoel, Jialei Chen, Nalin Singal, Robert Sim
| July 2023
Published by Microsoft
Confidential Computing within an AI Accelerator
Kapil Vaswani, Stavros Volos, Cédric Fournet, Antonio Nino Diaz, Ken Gordon, Balaji Vembu, Sam Webster, David Chisnall, Saurabh Kulkarni, Graham Cunningham , Richard Osborne , Daniel Wilkinson
2023 USENIX Annual Technical Conference | July 2023
Bayesian Estimation of Differential Privacy
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Ahmed Salem, Victor Ruehle, Andrew Paverd, Mohammad Naseri, Boris Köpf, Daniel Jones
2023 International Conference on Machine Learning | July 2023
Editor(s): Barbara Engelhardt, Emma Brunskill, Kyunghyun Cho
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, Bo Li
NeurIPS 2023 | June 2023
Outstanding Paper Award
Analyzing Leakage of Personally Identifiable Information in Language Models
Nils Lukas, Ahmed Salem, Robert Sim, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
2023 IEEE Symposium on Security and Privacy | May 2023
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan (Jana) Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian
International Conference on Learning Representations (ICLR) 2023 | May 2023
SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning
Ahmed Salem, Giovanni Cherubin, David Evans, Boris Köpf, Andrew Paverd, Anshuman Suri, Shruti Tople, Santiago Zanella-Béguelin
2023 IEEE Symposium on Security and Privacy | May 2023
Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
COLT 2023 | February 2023
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Differentially Private Model Compression
Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin Inan, Lukas Wutschitz, Janardhan (Jana) Kulkarni
NeurIPS 2022 | November 2022
When does differentially private learning not suffer in high dimensions?
Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin Inan, Janardhan (Jana) Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta
NeurIPS’22 | November 2022
Membership Inference Attacks and Generalization: A Causal Perspective
Teodora Baluta, Shiqi Shen, S. Hitarth, Shruti Tople, Prateek Saxena
Computer and Communications Security (CCS) | November 2022
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning
Yae Jee Cho, Andre Manoel, Gauri Joshi, Robert Sim, Dimitrios Dimitriadis
IJCAI-ECAI’22 | August 2022
SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost
Nishanth Chandran, Divya Gupta, Sai Lakshmi Bhavana Obbattu, Akash Shah
USENIX Security | August 2022
USENIX Security 2022
dp-transformers: Training transformer models with differential privacy
Huseyin Inan, Andre Manoel, Lukas Wutschitz
July 2022
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis
Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, Dimitrios Dimitriadis
2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL’22) | July 2022
Private Convex Optimization in General Norms
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
SODA 2023 | July 2022
Circuit-PSI with Linear Complexity via Relaxed Batch OPPRF
Nishanth Chandran, Divya Gupta, Akash Shah
22nd Privacy Enhancing Technologies Symposium (PETS 2022) | June 2022
Personalized Federated Learning with Adaptive Batchnorm for Healthcare
Wang Lu, Jindong Wang, Yiqiang Chen, Xin Qin, Renjun Xu, Dimitrios Dimitriadis, Tao Qin
IEEE Transactions on Big Data | May 2022
SECFLOAT: Accurate Floating-Point meets Secure 2-Party Computation
Deevashwer Rathee, Anwesh Bhattacharya, Rahul Sharma, Divya Gupta, Nishanth Chandran, Aseem Rastogi
43rd IEEE Symposium on Security and Privacy (IEEE S&P 2022) | May 2022
Differentially private fine-tuning of language models
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Gautam Kamath, Janardhan (Jana) Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang
ICLR 2022 | April 2022
FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations
Dimitrios Dimitriadis, Mirian Hipolito Garcia, Daniel Madrigal, Andre Manoel, Robert Sim
March 2022
Private Convex Optimization via Exponential Mechanism
Sivakanth Gopi, Yin Tat Lee, Daogao Liu
COLT 2022 | March 2022
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Differentially private n-gram extraction
Kunho Kim, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Sergey Yekhanin
NeurIPS 2021 | December 2021
Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal
Tae Jin Park, Kenichi Kumatani, Dimitrios Dimitriadis
October 2021
Secure Featurization and Applications to Secure Phishing Detection
Akash Shah, Nishanth Chandran, Divya Gupta, Mesfin Dema, Arun Gururajan, Huan Yu
CCSW 2021: The ACM Cloud Computing Security Workshop | October 2021
Membership Inference Attacks Against NLP Classification Models
Virat Shejwalkar, Huseyin Inan, Amir Houmansadr, Robert Sim
NeurIPS 2021 Workshop Privacy in Machine Learning | September 2021
Leakage of Dataset Properties in Multi-Party Machine Learning
Wanrong Zhang, Shruti Tople, Olga Ohrimenko
Usenix Security | August 2021
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan (Jana) Kulkarni
2021 International Conference on Machine Learning | July 2021
Domain Generalization using Causal Matching
Divyat Mahajan, Shruti Tople, Amit Sharma
International Conference on Machine Learning (ICML) | July 2021
Grey-box Extraction of Natural Language Models
Santiago Zanella-Béguelin, Shruti Tople, Andrew Paverd, Boris Köpf
International Conference on Machine Learning | July 2021
Editor(s): Marina Meila and Tong Zhang
Membership Inference on Word Embedding and Beyond
Saeed Mahloujifar, Huseyin A. Inan, Melissa Chase, Esha Ghosh, Marcello Hasegawa
June 2021
Numerical Composition of Differential Privacy
Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
NeurIPS 2021 | June 2021
Spotlight at NeurIPS 2021.
Privacy Regularization: Joint Privacy-Utility Optimization in Language Models
Fatemehsadat Mireshghallah, Huseyin Inan, Marcello Hasegawa, Victor Rühle, Taylor Berg-Kirkpatrick, Robert Sim
2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021) | June 2021
Causally Constrained Data Synthesis for Private Data Release
Varun Chandrasekaran, Darren Edge, Somesh Jha, Amit Sharma, Cheng Zhang, Shruti Tople
Distributed and Private Machine Learning (DPML) Workshop at ICLR’21 | May 2021
On Privacy and Confidentiality of Communications in Organizational Graphs
Masoumeh Shafieinejad, Huseyin A. Inan (huinan), Marcello Hasegawa, Robert Sim
May 2021
Efficient Linear Multiparty PSI and Extensions to Circuit/Quorum PSI
Nishanth Chandran, Nishka Dasgupta, Divya Gupta, Sai Lakshmi Bhavana Obbattu, Sruthi Sekar, Akash Shah
28th Annual Conference on Computer and Communications Security (ACM CCS 2021) | May 2021
Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps
Janardhan (Jana) Kulkarni, Yin Tat Lee, Daogao Liu
NeurIPS 2021 | March 2021
Training Data Leakage Analysis in Language Models
Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim
February 2021
Differentially Private Correlation Clustering
Mark Bun, Marek Eliás, Janardhan (Jana) Kulkarni
2021 International Conference on Machine Learning | February 2021
Fast and Memory Efficient Differentially Private-SGD via JL Projections
Zhiqi Bu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat
NeurIPS 2021 | February 2021
SIRNN: A Math Library for Secure RNN Inference
Deevashwer Rathee, Mayank Rathee, Rahul Kranti Kiran Goli, Divya Gupta, Rahul Sharma, Nishanth Chandran, Aseem Rastogi
42nd IEEE Symposium on Security and Privacy (IEEE S&P 2021) | February 2021
Property Inference from Poisoning
Melissa Chase, Esha Ghosh, Saeed Mahloujifar
January 2021
Function Secret Sharing for Mixed-Mode and Fixed-Point Secure Computation
Elette Boyle, Nishanth Chandran, Niv Gilboa, Divya Gupta, Yuval Ishai, Nishant Kumar, Mayank Rathee
EUROCRYPT 2021 | January 2021
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Secure Medical Image Analysis with CrypTFlow
Javier Alvarez-Valle, Pratik Bhatu, Nishanth Chandran, Divya Gupta, Aditya Nori, Aseem Rastogi, Mayank Rathee, Rahul Sharma, Shubham Ugare
NeurIPS PPML Workshop | December 2020
The Connection between Out-of-Distribution Generalization and Privacy of ML Models
Divyat Mahajan, Shruti Tople, Amit Sharma
Privacy Preserving Machine Learning (PriML/PPML) Workshop at NeurIPS’20 | December 2020
Analyzing Information Leakage of Updates to Natural Language Models
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Victor Ruehle, Andrew Paverd, Olga Ohrimenko, Boris Köpf, Marc Brockschmidt
ACM Conference on Computer and Communication Security (CCS) | November 2020
An Empirical Study on the Intrinsic Privacy of SGD
Stephanie Hyland, Shruti Tople
Theory and Practice of Differential Privacy (CCS Worshop) | November 2020
CrypTFlow2: Practical 2-Party Secure Inference
Deevashwer Rathee, Mayank Rathee, Nishant Kumar, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma
27th Annual Conference on Computer and Communications Security (ACM CCS 2020) | August 2020
Visor: Privacy-Preserving Video Analytics as a Cloud Service
Rishabh Poddar, Ganesh Ananthanarayanan, Srinath Setty, Stavros Volos, Raluca Ada Popa
29th USENIX Security Symposium | August 2020
CSAW 2020 Applied Research Competition Award (Runner-up)
Alleviating Privacy Attacks via Causal Learning
Shruti Tople, Amit Sharma, Aditya V. Nori
International Conference on Machine Learning (ICML) | July 2020
Differentially Private Set Union
Sivakanth Gopi, Pankaj Gulhane, Janardhan (Jana) Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin
International Conference on Machine Learning 2020 | July 2020
Contributed talk at TPDP 2020. Also appeared in the Journal of Privacy and Confidentiality (JPC).
Locally Private Hypothesis Selection
Sivakanth Gopi, Gautam Kamath, Janardhan (Jana) Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
Conference on Learning Theory (COLT) 2020 | July 2020
Privately Learning Markov Random Fields
Huanyu Zhang, Gautam Kamath, Janardhan (Jana) Kulkarni, Zhiwei Steven Wu
ICML 2020 | July 2020
CrypTFlow: Secure TensorFlow Inference
Nishant Kumar, Mayank Rathee, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma
IEEE Symposium on Security and Privacy | May 2020
Differentially private release of synthetic graphs
Marek Eliáš, Michael Kapralov, Janardhan (Jana) Kulkarni, Yin Tat Lee
2020 Symposium on Discrete Algorithms | January 2020
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Locally Private Gaussian Estimation
Matthew Joseph, Janardhan (Jana) Kulkarni, Jieming Mao, Zhiwei Steven Wu
November 2019
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors
Joshua Allen, Bolin Ding, Janardhan (Jana) Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin
NeurIPS 2019 | August 2019
EzPC: Programmable, Efficient, and Scalable Secure Two-Party Computation for Machine Learning
Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma, Shardul Tripathi
IEEE European Symposium on Security and Privacy | February 2019
SecureNN: Efficient and Private Neural Network Training
Sameer Wagh, Divya Gupta, Nishanth Chandran
Privacy Enhancing Technologies Symposium | February 2019
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Graviton: Trusted Execution Environments on GPUs
Stavros Volos, Kapil Vaswani, Rordigo Bruno
13th USENIX Symposium on Operating Systems Design and Implementation | October 2018
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Collecting Telemetry Data Privately
Bolin Ding, Janardhan (Jana) Kulkarni, Sergey Yekhanin
Advances in Neural Information Processing Systems 30 | December 2017
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Oblivious Multi-Party Machine Learning on Trusted Processors
Olya Ohrimenko, Felix Schuster, Cédric Fournet, Aastha Mehta, Sebastian Nowozin, Kapil Vaswani, Manuel Costa
The 25th USENIX Security Symposium 2016 | July 2016
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VC3: Trustworthy Data Analytics in the Cloud using SGX
Felix Schuster, Manuel Costa, Cédric Fournet, Christos Gkantsidis, Marcus Peinado, Gloria Mainar-Ruiz, Mark Russinovich
36th IEEE Symposium on Security and Privacy | May 2015
Publications by Research Area
-
The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text
Matthieu Meeus, Lukas Wutschitz, Santiago Zanella-Béguelin, Reza Shokri, Shruti Tople
February 2025
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Models
Zinan Lin, Tadas Baltrusaitis, Sergey Yekhanin
February 2025
Inferentially-Private Private Information
Shuaiqi Wang, Shuran Zheng, Zinan Lin, Giulia Fanti, Zhiwei Steven Wu
WWW 2025 | February 2025
MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models
Chejian Xu, Jiawei Zhang, Zhaorun Chen, Chulin Xie, Mintong Kang, Zhuowen Yuan, Zidi Xiong, Chenhui Zhang, Lingzhi Yuan, Yi Zeng, Peiyang Xu, Chengquan Guo, Andy Zhou, Jeffrey Ziwei Tan, Zhun Wang, Alexander Xiong, Xuandong Zhao, Yu Gai, Francesco Pinto, Yujin Potter, Zhen Xiang, Zinan Lin, Dan Hendrycks, Dawn Song, Bo Li
ICLR 2025 | February 2025
Closed-Form Bounds for DP-SGD against Record-level Inference
Giovanni Cherubin, Boris Köpf, Andrew Paverd, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
USENIX Security Symposium | August 2024
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
ICML 2024 | July 2024
Spotlight
Statistic Maximal Leakage
Shuaiqi Wang, Zinan Lin, Giulia Fanti
IEEE International Symposium on Information Theory 2024 | July 2024
Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition
Edoardo Debenedetti, Javier Rando, Daniel Paleka, Fineas Silaghi, Dragos Albastroiu, Niv Cohen, Yuval Lemberg, Reshmi Ghosh, Ahmed Salem, Rui Wen, Giovanni Cherubin, Santiago Zanella-Béguelin, Robin Schmid, Victor Klemm, Takahiro Miki, Chenhao Li, Stefan Kraft, Mario Fritz, Florian Tramer, Sahar Abdelnabi, Lea Schönherr
NeurIPS 2024 | June 2024
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
Xinyu Tang, Richard Shin, Huseyin Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Robert Sim
ICLR 2024 | May 2024
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
ICLR 2024 | May 2024
Invariant Aggregator for Defending against Federated Backdoor Attacks
Xiaoya Wang, Dimitrios Dimitriadis, Oluwasanmi Koyejo, Shruti Tople
International Conference on Artificial Intelligence and Statistics, AISTATS | May 2024
Editor(s): Sanjoy Dasgupta, Stephan Mandt, Yingzhen Li
Summary Statistic Privacy in Data Sharing
Zinan Lin, Shuaiqi Wang, Vyas Sekar, Giulia Fanti
IEEE Journal on Selected Areas in Information Theory | March 2024
Training Private and Efficient Language Models with Synthetic Data from LLMs
Da Yu, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Janardhan (Jana) Kulkarni, Zinan Lin, Chulin Xie, Huishuai Zhang, Wanrong Zhang
NeurIPS 2023 SoLaR Workshop | December 2023
Project Florida: Federated Learning Made Easy
Daniel Madrigal, Andre Manoel, Jialei Chen, Nalin Singal, Robert Sim
| July 2023
Published by Microsoft
Bayesian Estimation of Differential Privacy
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Ahmed Salem, Victor Ruehle, Andrew Paverd, Mohammad Naseri, Boris Köpf, Daniel Jones
2023 International Conference on Machine Learning | July 2023
Editor(s): Barbara Engelhardt, Emma Brunskill, Kyunghyun Cho
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, Bo Li
NeurIPS 2023 | June 2023
Outstanding Paper Award
Analyzing Leakage of Personally Identifiable Information in Language Models
Nils Lukas, Ahmed Salem, Robert Sim, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
2023 IEEE Symposium on Security and Privacy | May 2023
SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning
Ahmed Salem, Giovanni Cherubin, David Evans, Boris Köpf, Andrew Paverd, Anshuman Suri, Shruti Tople, Santiago Zanella-Béguelin
2023 IEEE Symposium on Security and Privacy | May 2023
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan (Jana) Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian
International Conference on Learning Representations (ICLR) 2023 | May 2023
Differentially Private Model Compression
Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin Inan, Lukas Wutschitz, Janardhan (Jana) Kulkarni
NeurIPS 2022 | November 2022
Membership Inference Attacks and Generalization: A Causal Perspective
Teodora Baluta, Shiqi Shen, S. Hitarth, Shruti Tople, Prateek Saxena
Computer and Communications Security (CCS) | November 2022
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning
Yae Jee Cho, Andre Manoel, Gauri Joshi, Robert Sim, Dimitrios Dimitriadis
IJCAI-ECAI’22 | August 2022
dp-transformers: Training transformer models with differential privacy
Huseyin Inan, Andre Manoel, Lukas Wutschitz
July 2022
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis
Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, Dimitrios Dimitriadis
2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL’22) | July 2022
Personalized Federated Learning with Adaptive Batchnorm for Healthcare
Wang Lu, Jindong Wang, Yiqiang Chen, Xin Qin, Renjun Xu, Dimitrios Dimitriadis, Tao Qin
IEEE Transactions on Big Data | May 2022
FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations
Dimitrios Dimitriadis, Mirian Hipolito Garcia, Daniel Madrigal, Andre Manoel, Robert Sim
March 2022
Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal
Tae Jin Park, Kenichi Kumatani, Dimitrios Dimitriadis
October 2021
Membership Inference Attacks Against NLP Classification Models
Virat Shejwalkar, Huseyin Inan, Amir Houmansadr, Robert Sim
NeurIPS 2021 Workshop Privacy in Machine Learning | September 2021
Leakage of Dataset Properties in Multi-Party Machine Learning
Wanrong Zhang, Shruti Tople, Olga Ohrimenko
Usenix Security | August 2021
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan (Jana) Kulkarni
2021 International Conference on Machine Learning | July 2021
Domain Generalization using Causal Matching
Divyat Mahajan, Shruti Tople, Amit Sharma
International Conference on Machine Learning (ICML) | July 2021
Grey-box Extraction of Natural Language Models
Santiago Zanella-Béguelin, Shruti Tople, Andrew Paverd, Boris Köpf
International Conference on Machine Learning | July 2021
Editor(s): Marina Meila and Tong Zhang
Membership Inference on Word Embedding and Beyond
Saeed Mahloujifar, Huseyin A. Inan, Melissa Chase, Esha Ghosh, Marcello Hasegawa
June 2021
Privacy Regularization: Joint Privacy-Utility Optimization in Language Models
Fatemehsadat Mireshghallah, Huseyin Inan, Marcello Hasegawa, Victor Rühle, Taylor Berg-Kirkpatrick, Robert Sim
2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021) | June 2021
On Privacy and Confidentiality of Communications in Organizational Graphs
Masoumeh Shafieinejad, Huseyin A. Inan (huinan), Marcello Hasegawa, Robert Sim
May 2021
Causally Constrained Data Synthesis for Private Data Release
Varun Chandrasekaran, Darren Edge, Somesh Jha, Amit Sharma, Cheng Zhang, Shruti Tople
Distributed and Private Machine Learning (DPML) Workshop at ICLR’21 | May 2021
Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps
Janardhan (Jana) Kulkarni, Yin Tat Lee, Daogao Liu
NeurIPS 2021 | March 2021
Training Data Leakage Analysis in Language Models
Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim
February 2021
Property Inference from Poisoning
Melissa Chase, Esha Ghosh, Saeed Mahloujifar
January 2021
The Connection between Out-of-Distribution Generalization and Privacy of ML Models
Divyat Mahajan, Shruti Tople, Amit Sharma
Privacy Preserving Machine Learning (PriML/PPML) Workshop at NeurIPS’20 | December 2020
Secure Medical Image Analysis with CrypTFlow
Javier Alvarez-Valle, Pratik Bhatu, Nishanth Chandran, Divya Gupta, Aditya Nori, Aseem Rastogi, Mayank Rathee, Rahul Sharma, Shubham Ugare
NeurIPS PPML Workshop | December 2020
Analyzing Information Leakage of Updates to Natural Language Models
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Victor Ruehle, Andrew Paverd, Olga Ohrimenko, Boris Köpf, Marc Brockschmidt
ACM Conference on Computer and Communication Security (CCS) | November 2020
Privately Learning Markov Random Fields
Huanyu Zhang, Gautam Kamath, Janardhan (Jana) Kulkarni, Zhiwei Steven Wu
ICML 2020 | July 2020
Alleviating Privacy Attacks via Causal Learning
Shruti Tople, Amit Sharma, Aditya V. Nori
International Conference on Machine Learning (ICML) | July 2020
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The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text
Matthieu Meeus, Lukas Wutschitz, Santiago Zanella-Béguelin, Reza Shokri, Shruti Tople
February 2025
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Models
Zinan Lin, Tadas Baltrusaitis, Sergey Yekhanin
February 2025
Inferentially-Private Private Information
Shuaiqi Wang, Shuran Zheng, Zinan Lin, Giulia Fanti, Zhiwei Steven Wu
WWW 2025 | February 2025
MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models
Chejian Xu, Jiawei Zhang, Zhaorun Chen, Chulin Xie, Mintong Kang, Zhuowen Yuan, Zidi Xiong, Chenhui Zhang, Lingzhi Yuan, Yi Zeng, Peiyang Xu, Chengquan Guo, Andy Zhou, Jeffrey Ziwei Tan, Zhun Wang, Alexander Xiong, Xuandong Zhao, Yu Gai, Francesco Pinto, Yujin Potter, Zhen Xiang, Zinan Lin, Dan Hendrycks, Dawn Song, Bo Li
ICLR 2025 | February 2025
Closed-Form Bounds for DP-SGD against Record-level Inference
Giovanni Cherubin, Boris Köpf, Andrew Paverd, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
USENIX Security Symposium | August 2024
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
ICML 2024 | July 2024
Spotlight
Statistic Maximal Leakage
Shuaiqi Wang, Zinan Lin, Giulia Fanti
IEEE International Symposium on Information Theory 2024 | July 2024
Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition
Edoardo Debenedetti, Javier Rando, Daniel Paleka, Fineas Silaghi, Dragos Albastroiu, Niv Cohen, Yuval Lemberg, Reshmi Ghosh, Ahmed Salem, Rui Wen, Giovanni Cherubin, Santiago Zanella-Béguelin, Robin Schmid, Victor Klemm, Takahiro Miki, Chenhao Li, Stefan Kraft, Mario Fritz, Florian Tramer, Sahar Abdelnabi, Lea Schönherr
NeurIPS 2024 | June 2024
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
Xinyu Tang, Richard Shin, Huseyin Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Robert Sim
ICLR 2024 | May 2024
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
ICLR 2024 | May 2024
Invariant Aggregator for Defending against Federated Backdoor Attacks
Xiaoya Wang, Dimitrios Dimitriadis, Oluwasanmi Koyejo, Shruti Tople
International Conference on Artificial Intelligence and Statistics, AISTATS | May 2024
Editor(s): Sanjoy Dasgupta, Stephan Mandt, Yingzhen Li
Selective Pre-training for Private Fine-tuning
Da Yu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, Huishuai Zhang
TMLR | May 2024
Summary Statistic Privacy in Data Sharing
Zinan Lin, Shuaiqi Wang, Vyas Sekar, Giulia Fanti
IEEE Journal on Selected Areas in Information Theory | March 2024
Advancing differential privacy: where we are now and future directions for real-world deployment
Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Yangsibo Huang, Matthew Jagielski, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Da Yu, Sergey Yekhanin, Huanyu Zhang, Wanrong Zhang
Harvard Data Science Review | January 2024, Vol 6(1)
Secure Floating-Point Training
Deevashwer Rathee, Anwesh Bhattacharya, Divya Gupta, Rahul Sharma, Dawn Song
Usenix Security Symposium 2023 | August 2023
Confidential Computing within an AI Accelerator
Kapil Vaswani, Stavros Volos, Cédric Fournet, Antonio Nino Diaz, Ken Gordon, Balaji Vembu, Sam Webster, David Chisnall, Saurabh Kulkarni, Graham Cunningham , Richard Osborne , Daniel Wilkinson
2023 USENIX Annual Technical Conference | July 2023
Bayesian Estimation of Differential Privacy
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Ahmed Salem, Victor Ruehle, Andrew Paverd, Mohammad Naseri, Boris Köpf, Daniel Jones
2023 International Conference on Machine Learning | July 2023
Editor(s): Barbara Engelhardt, Emma Brunskill, Kyunghyun Cho
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, Bo Li
NeurIPS 2023 | June 2023
Outstanding Paper Award
Analyzing Leakage of Personally Identifiable Information in Language Models
Nils Lukas, Ahmed Salem, Robert Sim, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
2023 IEEE Symposium on Security and Privacy | May 2023
SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning
Ahmed Salem, Giovanni Cherubin, David Evans, Boris Köpf, Andrew Paverd, Anshuman Suri, Shruti Tople, Santiago Zanella-Béguelin
2023 IEEE Symposium on Security and Privacy | May 2023
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan (Jana) Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian
International Conference on Learning Representations (ICLR) 2023 | May 2023
Membership Inference Attacks and Generalization: A Causal Perspective
Teodora Baluta, Shiqi Shen, S. Hitarth, Shruti Tople, Prateek Saxena
Computer and Communications Security (CCS) | November 2022
SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost
Nishanth Chandran, Divya Gupta, Sai Lakshmi Bhavana Obbattu, Akash Shah
USENIX Security | August 2022
USENIX Security 2022
dp-transformers: Training transformer models with differential privacy
Huseyin Inan, Andre Manoel, Lukas Wutschitz
July 2022
Private Convex Optimization in General Norms
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
SODA 2023 | July 2022
Circuit-PSI with Linear Complexity via Relaxed Batch OPPRF
Nishanth Chandran, Divya Gupta, Akash Shah
22nd Privacy Enhancing Technologies Symposium (PETS 2022) | June 2022
SECFLOAT: Accurate Floating-Point meets Secure 2-Party Computation
Deevashwer Rathee, Anwesh Bhattacharya, Rahul Sharma, Divya Gupta, Nishanth Chandran, Aseem Rastogi
43rd IEEE Symposium on Security and Privacy (IEEE S&P 2022) | May 2022
FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations
Dimitrios Dimitriadis, Mirian Hipolito Garcia, Daniel Madrigal, Andre Manoel, Robert Sim
March 2022
Private Convex Optimization via Exponential Mechanism
Sivakanth Gopi, Yin Tat Lee, Daogao Liu
COLT 2022 | March 2022
Secure Featurization and Applications to Secure Phishing Detection
Akash Shah, Nishanth Chandran, Divya Gupta, Mesfin Dema, Arun Gururajan, Huan Yu
CCSW 2021: The ACM Cloud Computing Security Workshop | October 2021
Leakage of Dataset Properties in Multi-Party Machine Learning
Wanrong Zhang, Shruti Tople, Olga Ohrimenko
Usenix Security | August 2021
Grey-box Extraction of Natural Language Models
Santiago Zanella-Béguelin, Shruti Tople, Andrew Paverd, Boris Köpf
International Conference on Machine Learning | July 2021
Editor(s): Marina Meila and Tong Zhang
Membership Inference on Word Embedding and Beyond
Saeed Mahloujifar, Huseyin A. Inan, Melissa Chase, Esha Ghosh, Marcello Hasegawa
June 2021
Numerical Composition of Differential Privacy
Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
NeurIPS 2021 | June 2021
Spotlight at NeurIPS 2021.
Privacy Regularization: Joint Privacy-Utility Optimization in Language Models
Fatemehsadat Mireshghallah, Huseyin Inan, Marcello Hasegawa, Victor Rühle, Taylor Berg-Kirkpatrick, Robert Sim
2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021) | June 2021
On Privacy and Confidentiality of Communications in Organizational Graphs
Masoumeh Shafieinejad, Huseyin A. Inan (huinan), Marcello Hasegawa, Robert Sim
May 2021
Causally Constrained Data Synthesis for Private Data Release
Varun Chandrasekaran, Darren Edge, Somesh Jha, Amit Sharma, Cheng Zhang, Shruti Tople
Distributed and Private Machine Learning (DPML) Workshop at ICLR’21 | May 2021
Efficient Linear Multiparty PSI and Extensions to Circuit/Quorum PSI
Nishanth Chandran, Nishka Dasgupta, Divya Gupta, Sai Lakshmi Bhavana Obbattu, Sruthi Sekar, Akash Shah
28th Annual Conference on Computer and Communications Security (ACM CCS 2021) | May 2021
Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps
Janardhan (Jana) Kulkarni, Yin Tat Lee, Daogao Liu
NeurIPS 2021 | March 2021
Training Data Leakage Analysis in Language Models
Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim
February 2021
SIRNN: A Math Library for Secure RNN Inference
Deevashwer Rathee, Mayank Rathee, Rahul Kranti Kiran Goli, Divya Gupta, Rahul Sharma, Nishanth Chandran, Aseem Rastogi
42nd IEEE Symposium on Security and Privacy (IEEE S&P 2021) | February 2021
Fast and Memory Efficient Differentially Private-SGD via JL Projections
Zhiqi Bu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat
NeurIPS 2021 | February 2021
Property Inference from Poisoning
Melissa Chase, Esha Ghosh, Saeed Mahloujifar
January 2021
Function Secret Sharing for Mixed-Mode and Fixed-Point Secure Computation
Elette Boyle, Nishanth Chandran, Niv Gilboa, Divya Gupta, Yuval Ishai, Nishant Kumar, Mayank Rathee
EUROCRYPT 2021 | January 2021
The Connection between Out-of-Distribution Generalization and Privacy of ML Models
Divyat Mahajan, Shruti Tople, Amit Sharma
Privacy Preserving Machine Learning (PriML/PPML) Workshop at NeurIPS’20 | December 2020
Secure Medical Image Analysis with CrypTFlow
Javier Alvarez-Valle, Pratik Bhatu, Nishanth Chandran, Divya Gupta, Aditya Nori, Aseem Rastogi, Mayank Rathee, Rahul Sharma, Shubham Ugare
NeurIPS PPML Workshop | December 2020
Analyzing Information Leakage of Updates to Natural Language Models
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Victor Ruehle, Andrew Paverd, Olga Ohrimenko, Boris Köpf, Marc Brockschmidt
ACM Conference on Computer and Communication Security (CCS) | November 2020
An Empirical Study on the Intrinsic Privacy of SGD
Stephanie Hyland, Shruti Tople
Theory and Practice of Differential Privacy (CCS Worshop) | November 2020
CrypTFlow2: Practical 2-Party Secure Inference
Deevashwer Rathee, Mayank Rathee, Nishant Kumar, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma
27th Annual Conference on Computer and Communications Security (ACM CCS 2020) | August 2020
Visor: Privacy-Preserving Video Analytics as a Cloud Service
Rishabh Poddar, Ganesh Ananthanarayanan, Srinath Setty, Stavros Volos, Raluca Ada Popa
29th USENIX Security Symposium | August 2020
CSAW 2020 Applied Research Competition Award (Runner-up)
Privately Learning Markov Random Fields
Huanyu Zhang, Gautam Kamath, Janardhan (Jana) Kulkarni, Zhiwei Steven Wu
ICML 2020 | July 2020
Alleviating Privacy Attacks via Causal Learning
Shruti Tople, Amit Sharma, Aditya V. Nori
International Conference on Machine Learning (ICML) | July 2020
Locally Private Hypothesis Selection
Sivakanth Gopi, Gautam Kamath, Janardhan (Jana) Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
Conference on Learning Theory (COLT) 2020 | July 2020
Differentially Private Set Union
Sivakanth Gopi, Pankaj Gulhane, Janardhan (Jana) Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin
International Conference on Machine Learning 2020 | July 2020
Contributed talk at TPDP 2020. Also appeared in the Journal of Privacy and Confidentiality (JPC).
CrypTFlow: Secure TensorFlow Inference
Nishant Kumar, Mayank Rathee, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma
IEEE Symposium on Security and Privacy | May 2020
Locally Private Gaussian Estimation
Matthew Joseph, Janardhan (Jana) Kulkarni, Jieming Mao, Zhiwei Steven Wu
November 2019
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors
Joshua Allen, Bolin Ding, Janardhan (Jana) Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin
NeurIPS 2019 | August 2019
EzPC: Programmable, Efficient, and Scalable Secure Two-Party Computation for Machine Learning
Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma, Shardul Tripathi
IEEE European Symposium on Security and Privacy | February 2019
SecureNN: Efficient and Private Neural Network Training
Sameer Wagh, Divya Gupta, Nishanth Chandran
Privacy Enhancing Technologies Symposium | February 2019
Graviton: Trusted Execution Environments on GPUs
Stavros Volos, Kapil Vaswani, Rordigo Bruno
13th USENIX Symposium on Operating Systems Design and Implementation | October 2018
Collecting Telemetry Data Privately
Bolin Ding, Janardhan (Jana) Kulkarni, Sergey Yekhanin
Advances in Neural Information Processing Systems 30 | December 2017
Oblivious Multi-Party Machine Learning on Trusted Processors
Olya Ohrimenko, Felix Schuster, Cédric Fournet, Aastha Mehta, Sebastian Nowozin, Kapil Vaswani, Manuel Costa
The 25th USENIX Security Symposium 2016 | July 2016
VC3: Trustworthy Data Analytics in the Cloud using SGX
Felix Schuster, Manuel Costa, Cédric Fournet, Christos Gkantsidis, Marcus Peinado, Gloria Mainar-Ruiz, Mark Russinovich
36th IEEE Symposium on Security and Privacy | May 2015
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MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models
Chejian Xu, Jiawei Zhang, Zhaorun Chen, Chulin Xie, Mintong Kang, Zhuowen Yuan, Zidi Xiong, Chenhui Zhang, Lingzhi Yuan, Yi Zeng, Peiyang Xu, Chengquan Guo, Andy Zhou, Jeffrey Ziwei Tan, Zhun Wang, Alexander Xiong, Xuandong Zhao, Yu Gai, Francesco Pinto, Yujin Potter, Zhen Xiang, Zinan Lin, Dan Hendrycks, Dawn Song, Bo Li
ICLR 2025 | February 2025
Visor: Privacy-Preserving Video Analytics as a Cloud Service
Rishabh Poddar, Ganesh Ananthanarayanan, Srinath Setty, Stavros Volos, Raluca Ada Popa
29th USENIX Security Symposium | August 2020
CSAW 2020 Applied Research Competition Award (Runner-up)
-
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
ICML 2024 | July 2024
Spotlight
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
ICLR 2024 | May 2024
Selective Pre-training for Private Fine-tuning
Da Yu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, Huishuai Zhang
TMLR | May 2024
Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
COLT 2023 | February 2023
Differentially Private Model Compression
Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin Inan, Lukas Wutschitz, Janardhan (Jana) Kulkarni
NeurIPS 2022 | November 2022
When does differentially private learning not suffer in high dimensions?
Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin Inan, Janardhan (Jana) Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta
NeurIPS’22 | November 2022
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning
Yae Jee Cho, Andre Manoel, Gauri Joshi, Robert Sim, Dimitrios Dimitriadis
IJCAI-ECAI’22 | August 2022
Private Convex Optimization in General Norms
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
SODA 2023 | July 2022
Differentially private fine-tuning of language models
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Gautam Kamath, Janardhan (Jana) Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang
ICLR 2022 | April 2022
Private Convex Optimization via Exponential Mechanism
Sivakanth Gopi, Yin Tat Lee, Daogao Liu
COLT 2022 | March 2022
Differentially private n-gram extraction
Kunho Kim, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Sergey Yekhanin
NeurIPS 2021 | December 2021
Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal
Tae Jin Park, Kenichi Kumatani, Dimitrios Dimitriadis
October 2021
Domain Generalization using Causal Matching
Divyat Mahajan, Shruti Tople, Amit Sharma
International Conference on Machine Learning (ICML) | July 2021
Numerical Composition of Differential Privacy
Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
NeurIPS 2021 | June 2021
Spotlight at NeurIPS 2021.
Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps
Janardhan (Jana) Kulkarni, Yin Tat Lee, Daogao Liu
NeurIPS 2021 | March 2021
Differentially Private Correlation Clustering
Mark Bun, Marek Eliás, Janardhan (Jana) Kulkarni
2021 International Conference on Machine Learning | February 2021
Fast and Memory Efficient Differentially Private-SGD via JL Projections
Zhiqi Bu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat
NeurIPS 2021 | February 2021
Privately Learning Markov Random Fields
Huanyu Zhang, Gautam Kamath, Janardhan (Jana) Kulkarni, Zhiwei Steven Wu
ICML 2020 | July 2020
Locally Private Hypothesis Selection
Sivakanth Gopi, Gautam Kamath, Janardhan (Jana) Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
Conference on Learning Theory (COLT) 2020 | July 2020
Differentially Private Set Union
Sivakanth Gopi, Pankaj Gulhane, Janardhan (Jana) Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin
International Conference on Machine Learning 2020 | July 2020
Contributed talk at TPDP 2020. Also appeared in the Journal of Privacy and Confidentiality (JPC).
Differentially private release of synthetic graphs
Marek Eliáš, Michael Kapralov, Janardhan (Jana) Kulkarni, Yin Tat Lee
2020 Symposium on Discrete Algorithms | January 2020
Collecting Telemetry Data Privately
Bolin Ding, Janardhan (Jana) Kulkarni, Sergey Yekhanin
Advances in Neural Information Processing Systems 30 | December 2017
-
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
ICML 2024 | July 2024
Spotlight
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
ICLR 2024 | May 2024
Advancing differential privacy: where we are now and future directions for real-world deployment
Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Yangsibo Huang, Matthew Jagielski, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Da Yu, Sergey Yekhanin, Huanyu Zhang, Wanrong Zhang
Harvard Data Science Review | January 2024, Vol 6(1)
Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
COLT 2023 | February 2023
Private Convex Optimization in General Norms
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
SODA 2023 | July 2022
Private Convex Optimization via Exponential Mechanism
Sivakanth Gopi, Yin Tat Lee, Daogao Liu
COLT 2022 | March 2022
Numerical Composition of Differential Privacy
Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
NeurIPS 2021 | June 2021
Spotlight at NeurIPS 2021.
Fast and Memory Efficient Differentially Private-SGD via JL Projections
Zhiqi Bu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat
NeurIPS 2021 | February 2021
Locally Private Hypothesis Selection
Sivakanth Gopi, Gautam Kamath, Janardhan (Jana) Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
Conference on Learning Theory (COLT) 2020 | July 2020
Differentially Private Set Union
Sivakanth Gopi, Pankaj Gulhane, Janardhan (Jana) Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin
International Conference on Machine Learning 2020 | July 2020
Contributed talk at TPDP 2020. Also appeared in the Journal of Privacy and Confidentiality (JPC).
-
Confidential Computing within an AI Accelerator
Kapil Vaswani, Stavros Volos, Cédric Fournet, Antonio Nino Diaz, Ken Gordon, Balaji Vembu, Sam Webster, David Chisnall, Saurabh Kulkarni, Graham Cunningham , Richard Osborne , Daniel Wilkinson
2023 USENIX Annual Technical Conference | July 2023
Graviton: Trusted Execution Environments on GPUs
Stavros Volos, Kapil Vaswani, Rordigo Bruno
13th USENIX Symposium on Operating Systems Design and Implementation | October 2018
-
Confidential Computing within an AI Accelerator
Kapil Vaswani, Stavros Volos, Cédric Fournet, Antonio Nino Diaz, Ken Gordon, Balaji Vembu, Sam Webster, David Chisnall, Saurabh Kulkarni, Graham Cunningham , Richard Osborne , Daniel Wilkinson
2023 USENIX Annual Technical Conference | July 2023
Visor: Privacy-Preserving Video Analytics as a Cloud Service
Rishabh Poddar, Ganesh Ananthanarayanan, Srinath Setty, Stavros Volos, Raluca Ada Popa
29th USENIX Security Symposium | August 2020
CSAW 2020 Applied Research Competition Award (Runner-up)
Graviton: Trusted Execution Environments on GPUs
Stavros Volos, Kapil Vaswani, Rordigo Bruno
13th USENIX Symposium on Operating Systems Design and Implementation | October 2018
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UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis
Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, Dimitrios Dimitriadis
2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL’22) | July 2022
Membership Inference on Word Embedding and Beyond
Saeed Mahloujifar, Huseyin A. Inan, Melissa Chase, Esha Ghosh, Marcello Hasegawa
June 2021
Privacy Regularization: Joint Privacy-Utility Optimization in Language Models
Fatemehsadat Mireshghallah, Huseyin Inan, Marcello Hasegawa, Victor Rühle, Taylor Berg-Kirkpatrick, Robert Sim
2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021) | June 2021
On Privacy and Confidentiality of Communications in Organizational Graphs
Masoumeh Shafieinejad, Huseyin A. Inan (huinan), Marcello Hasegawa, Robert Sim
May 2021
Training Data Leakage Analysis in Language Models
Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim
February 2021
-
Secure Medical Image Analysis with CrypTFlow
Javier Alvarez-Valle, Pratik Bhatu, Nishanth Chandran, Divya Gupta, Aditya Nori, Aseem Rastogi, Mayank Rathee, Rahul Sharma, Shubham Ugare
NeurIPS PPML Workshop | December 2020
-
Secure Medical Image Analysis with CrypTFlow
Javier Alvarez-Valle, Pratik Bhatu, Nishanth Chandran, Divya Gupta, Aditya Nori, Aseem Rastogi, Mayank Rathee, Rahul Sharma, Shubham Ugare
NeurIPS PPML Workshop | December 2020
EzPC: Programmable, Efficient, and Scalable Secure Two-Party Computation for Machine Learning
Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma, Shardul Tripathi
IEEE European Symposium on Security and Privacy | February 2019
-
Collecting Telemetry Data Privately
Bolin Ding, Janardhan (Jana) Kulkarni, Sergey Yekhanin
Advances in Neural Information Processing Systems 30 | December 2017
Publications by Type
-
The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text
Matthieu Meeus, Lukas Wutschitz, Santiago Zanella-Béguelin, Reza Shokri, Shruti Tople
February 2025
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Models
Zinan Lin, Tadas Baltrusaitis, Sergey Yekhanin
February 2025
dp-transformers: Training transformer models with differential privacy
Huseyin Inan, Andre Manoel, Lukas Wutschitz
July 2022
Membership Inference on Word Embedding and Beyond
Saeed Mahloujifar, Huseyin A. Inan, Melissa Chase, Esha Ghosh, Marcello Hasegawa
June 2021
Training Data Leakage Analysis in Language Models
Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim
February 2021
Property Inference from Poisoning
Melissa Chase, Esha Ghosh, Saeed Mahloujifar
January 2021
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Inferentially-Private Private Information
Shuaiqi Wang, Shuran Zheng, Zinan Lin, Giulia Fanti, Zhiwei Steven Wu
WWW 2025 | February 2025
MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models
Chejian Xu, Jiawei Zhang, Zhaorun Chen, Chulin Xie, Mintong Kang, Zhuowen Yuan, Zidi Xiong, Chenhui Zhang, Lingzhi Yuan, Yi Zeng, Peiyang Xu, Chengquan Guo, Andy Zhou, Jeffrey Ziwei Tan, Zhun Wang, Alexander Xiong, Xuandong Zhao, Yu Gai, Francesco Pinto, Yujin Potter, Zhen Xiang, Zinan Lin, Dan Hendrycks, Dawn Song, Bo Li
ICLR 2025 | February 2025
Closed-Form Bounds for DP-SGD against Record-level Inference
Giovanni Cherubin, Boris Köpf, Andrew Paverd, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
USENIX Security Symposium | August 2024
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
ICML 2024 | July 2024
Spotlight
Statistic Maximal Leakage
Shuaiqi Wang, Zinan Lin, Giulia Fanti
IEEE International Symposium on Information Theory 2024 | July 2024
Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition
Edoardo Debenedetti, Javier Rando, Daniel Paleka, Fineas Silaghi, Dragos Albastroiu, Niv Cohen, Yuval Lemberg, Reshmi Ghosh, Ahmed Salem, Rui Wen, Giovanni Cherubin, Santiago Zanella-Béguelin, Robin Schmid, Victor Klemm, Takahiro Miki, Chenhao Li, Stefan Kraft, Mario Fritz, Florian Tramer, Sahar Abdelnabi, Lea Schönherr
NeurIPS 2024 | June 2024
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
Xinyu Tang, Richard Shin, Huseyin Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Robert Sim
ICLR 2024 | May 2024
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
ICLR 2024 | May 2024
Invariant Aggregator for Defending against Federated Backdoor Attacks
Xiaoya Wang, Dimitrios Dimitriadis, Oluwasanmi Koyejo, Shruti Tople
International Conference on Artificial Intelligence and Statistics, AISTATS | May 2024
Editor(s): Sanjoy Dasgupta, Stephan Mandt, Yingzhen Li
Training Private and Efficient Language Models with Synthetic Data from LLMs
Da Yu, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Janardhan (Jana) Kulkarni, Zinan Lin, Chulin Xie, Huishuai Zhang, Wanrong Zhang
NeurIPS 2023 SoLaR Workshop | December 2023
Secure Floating-Point Training
Deevashwer Rathee, Anwesh Bhattacharya, Divya Gupta, Rahul Sharma, Dawn Song
Usenix Security Symposium 2023 | August 2023
Confidential Computing within an AI Accelerator
Kapil Vaswani, Stavros Volos, Cédric Fournet, Antonio Nino Diaz, Ken Gordon, Balaji Vembu, Sam Webster, David Chisnall, Saurabh Kulkarni, Graham Cunningham , Richard Osborne , Daniel Wilkinson
2023 USENIX Annual Technical Conference | July 2023
Bayesian Estimation of Differential Privacy
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Ahmed Salem, Victor Ruehle, Andrew Paverd, Mohammad Naseri, Boris Köpf, Daniel Jones
2023 International Conference on Machine Learning | July 2023
Editor(s): Barbara Engelhardt, Emma Brunskill, Kyunghyun Cho
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, Bo Li
NeurIPS 2023 | June 2023
Outstanding Paper Award
Analyzing Leakage of Personally Identifiable Information in Language Models
Nils Lukas, Ahmed Salem, Robert Sim, Shruti Tople, Lukas Wutschitz, Santiago Zanella-Béguelin
2023 IEEE Symposium on Security and Privacy | May 2023
SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning
Ahmed Salem, Giovanni Cherubin, David Evans, Boris Köpf, Andrew Paverd, Anshuman Suri, Shruti Tople, Santiago Zanella-Béguelin
2023 IEEE Symposium on Security and Privacy | May 2023
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan (Jana) Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian
International Conference on Learning Representations (ICLR) 2023 | May 2023
Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
COLT 2023 | February 2023
Differentially Private Model Compression
Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin Inan, Lukas Wutschitz, Janardhan (Jana) Kulkarni
NeurIPS 2022 | November 2022
When does differentially private learning not suffer in high dimensions?
Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin Inan, Janardhan (Jana) Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta
NeurIPS’22 | November 2022
Membership Inference Attacks and Generalization: A Causal Perspective
Teodora Baluta, Shiqi Shen, S. Hitarth, Shruti Tople, Prateek Saxena
Computer and Communications Security (CCS) | November 2022
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning
Yae Jee Cho, Andre Manoel, Gauri Joshi, Robert Sim, Dimitrios Dimitriadis
IJCAI-ECAI’22 | August 2022
SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost
Nishanth Chandran, Divya Gupta, Sai Lakshmi Bhavana Obbattu, Akash Shah
USENIX Security | August 2022
USENIX Security 2022
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis
Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, Dimitrios Dimitriadis
2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL’22) | July 2022
Private Convex Optimization in General Norms
Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
SODA 2023 | July 2022
Circuit-PSI with Linear Complexity via Relaxed Batch OPPRF
Nishanth Chandran, Divya Gupta, Akash Shah
22nd Privacy Enhancing Technologies Symposium (PETS 2022) | June 2022
SECFLOAT: Accurate Floating-Point meets Secure 2-Party Computation
Deevashwer Rathee, Anwesh Bhattacharya, Rahul Sharma, Divya Gupta, Nishanth Chandran, Aseem Rastogi
43rd IEEE Symposium on Security and Privacy (IEEE S&P 2022) | May 2022
Differentially private fine-tuning of language models
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Gautam Kamath, Janardhan (Jana) Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang
ICLR 2022 | April 2022
Private Convex Optimization via Exponential Mechanism
Sivakanth Gopi, Yin Tat Lee, Daogao Liu
COLT 2022 | March 2022
Differentially private n-gram extraction
Kunho Kim, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Sergey Yekhanin
NeurIPS 2021 | December 2021
Secure Featurization and Applications to Secure Phishing Detection
Akash Shah, Nishanth Chandran, Divya Gupta, Mesfin Dema, Arun Gururajan, Huan Yu
CCSW 2021: The ACM Cloud Computing Security Workshop | October 2021
Membership Inference Attacks Against NLP Classification Models
Virat Shejwalkar, Huseyin Inan, Amir Houmansadr, Robert Sim
NeurIPS 2021 Workshop Privacy in Machine Learning | September 2021
Leakage of Dataset Properties in Multi-Party Machine Learning
Wanrong Zhang, Shruti Tople, Olga Ohrimenko
Usenix Security | August 2021
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan (Jana) Kulkarni
2021 International Conference on Machine Learning | July 2021
Domain Generalization using Causal Matching
Divyat Mahajan, Shruti Tople, Amit Sharma
International Conference on Machine Learning (ICML) | July 2021
Grey-box Extraction of Natural Language Models
Santiago Zanella-Béguelin, Shruti Tople, Andrew Paverd, Boris Köpf
International Conference on Machine Learning | July 2021
Editor(s): Marina Meila and Tong Zhang
Numerical Composition of Differential Privacy
Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
NeurIPS 2021 | June 2021
Spotlight at NeurIPS 2021.
Privacy Regularization: Joint Privacy-Utility Optimization in Language Models
Fatemehsadat Mireshghallah, Huseyin Inan, Marcello Hasegawa, Victor Rühle, Taylor Berg-Kirkpatrick, Robert Sim
2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021) | June 2021
Causally Constrained Data Synthesis for Private Data Release
Varun Chandrasekaran, Darren Edge, Somesh Jha, Amit Sharma, Cheng Zhang, Shruti Tople
Distributed and Private Machine Learning (DPML) Workshop at ICLR’21 | May 2021
Efficient Linear Multiparty PSI and Extensions to Circuit/Quorum PSI
Nishanth Chandran, Nishka Dasgupta, Divya Gupta, Sai Lakshmi Bhavana Obbattu, Sruthi Sekar, Akash Shah
28th Annual Conference on Computer and Communications Security (ACM CCS 2021) | May 2021
Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps
Janardhan (Jana) Kulkarni, Yin Tat Lee, Daogao Liu
NeurIPS 2021 | March 2021
Differentially Private Correlation Clustering
Mark Bun, Marek Eliás, Janardhan (Jana) Kulkarni
2021 International Conference on Machine Learning | February 2021
SIRNN: A Math Library for Secure RNN Inference
Deevashwer Rathee, Mayank Rathee, Rahul Kranti Kiran Goli, Divya Gupta, Rahul Sharma, Nishanth Chandran, Aseem Rastogi
42nd IEEE Symposium on Security and Privacy (IEEE S&P 2021) | February 2021
Fast and Memory Efficient Differentially Private-SGD via JL Projections
Zhiqi Bu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat
NeurIPS 2021 | February 2021
Function Secret Sharing for Mixed-Mode and Fixed-Point Secure Computation
Elette Boyle, Nishanth Chandran, Niv Gilboa, Divya Gupta, Yuval Ishai, Nishant Kumar, Mayank Rathee
EUROCRYPT 2021 | January 2021
The Connection between Out-of-Distribution Generalization and Privacy of ML Models
Divyat Mahajan, Shruti Tople, Amit Sharma
Privacy Preserving Machine Learning (PriML/PPML) Workshop at NeurIPS’20 | December 2020
Secure Medical Image Analysis with CrypTFlow
Javier Alvarez-Valle, Pratik Bhatu, Nishanth Chandran, Divya Gupta, Aditya Nori, Aseem Rastogi, Mayank Rathee, Rahul Sharma, Shubham Ugare
NeurIPS PPML Workshop | December 2020
Analyzing Information Leakage of Updates to Natural Language Models
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Victor Ruehle, Andrew Paverd, Olga Ohrimenko, Boris Köpf, Marc Brockschmidt
ACM Conference on Computer and Communication Security (CCS) | November 2020
An Empirical Study on the Intrinsic Privacy of SGD
Stephanie Hyland, Shruti Tople
Theory and Practice of Differential Privacy (CCS Worshop) | November 2020
CrypTFlow2: Practical 2-Party Secure Inference
Deevashwer Rathee, Mayank Rathee, Nishant Kumar, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma
27th Annual Conference on Computer and Communications Security (ACM CCS 2020) | August 2020
Visor: Privacy-Preserving Video Analytics as a Cloud Service
Rishabh Poddar, Ganesh Ananthanarayanan, Srinath Setty, Stavros Volos, Raluca Ada Popa
29th USENIX Security Symposium | August 2020
CSAW 2020 Applied Research Competition Award (Runner-up)
Privately Learning Markov Random Fields
Huanyu Zhang, Gautam Kamath, Janardhan (Jana) Kulkarni, Zhiwei Steven Wu
ICML 2020 | July 2020
Alleviating Privacy Attacks via Causal Learning
Shruti Tople, Amit Sharma, Aditya V. Nori
International Conference on Machine Learning (ICML) | July 2020
Locally Private Hypothesis Selection
Sivakanth Gopi, Gautam Kamath, Janardhan (Jana) Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
Conference on Learning Theory (COLT) 2020 | July 2020
Differentially Private Set Union
Sivakanth Gopi, Pankaj Gulhane, Janardhan (Jana) Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin
International Conference on Machine Learning 2020 | July 2020
Contributed talk at TPDP 2020. Also appeared in the Journal of Privacy and Confidentiality (JPC).
CrypTFlow: Secure TensorFlow Inference
Nishant Kumar, Mayank Rathee, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma
IEEE Symposium on Security and Privacy | May 2020
Differentially private release of synthetic graphs
Marek Eliáš, Michael Kapralov, Janardhan (Jana) Kulkarni, Yin Tat Lee
2020 Symposium on Discrete Algorithms | January 2020
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors
Joshua Allen, Bolin Ding, Janardhan (Jana) Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin
NeurIPS 2019 | August 2019
EzPC: Programmable, Efficient, and Scalable Secure Two-Party Computation for Machine Learning
Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma, Shardul Tripathi
IEEE European Symposium on Security and Privacy | February 2019
SecureNN: Efficient and Private Neural Network Training
Sameer Wagh, Divya Gupta, Nishanth Chandran
Privacy Enhancing Technologies Symposium | February 2019
Graviton: Trusted Execution Environments on GPUs
Stavros Volos, Kapil Vaswani, Rordigo Bruno
13th USENIX Symposium on Operating Systems Design and Implementation | October 2018
Collecting Telemetry Data Privately
Bolin Ding, Janardhan (Jana) Kulkarni, Sergey Yekhanin
Advances in Neural Information Processing Systems 30 | December 2017
Oblivious Multi-Party Machine Learning on Trusted Processors
Olya Ohrimenko, Felix Schuster, Cédric Fournet, Aastha Mehta, Sebastian Nowozin, Kapil Vaswani, Manuel Costa
The 25th USENIX Security Symposium 2016 | July 2016
VC3: Trustworthy Data Analytics in the Cloud using SGX
Felix Schuster, Manuel Costa, Cédric Fournet, Christos Gkantsidis, Marcus Peinado, Gloria Mainar-Ruiz, Mark Russinovich
36th IEEE Symposium on Security and Privacy | May 2015
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Project Florida: Federated Learning Made Easy
Daniel Madrigal, Andre Manoel, Jialei Chen, Nalin Singal, Robert Sim
| July 2023
Published by Microsoft
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Selective Pre-training for Private Fine-tuning
Da Yu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, Huishuai Zhang
TMLR | May 2024
Summary Statistic Privacy in Data Sharing
Zinan Lin, Shuaiqi Wang, Vyas Sekar, Giulia Fanti
IEEE Journal on Selected Areas in Information Theory | March 2024
Advancing differential privacy: where we are now and future directions for real-world deployment
Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Yangsibo Huang, Matthew Jagielski, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Da Yu, Sergey Yekhanin, Huanyu Zhang, Wanrong Zhang
Harvard Data Science Review | January 2024, Vol 6(1)
Personalized Federated Learning with Adaptive Batchnorm for Healthcare
Wang Lu, Jindong Wang, Yiqiang Chen, Xin Qin, Renjun Xu, Dimitrios Dimitriadis, Tao Qin
IEEE Transactions on Big Data | May 2022
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FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations
Dimitrios Dimitriadis, Mirian Hipolito Garcia, Daniel Madrigal, Andre Manoel, Robert Sim
March 2022
Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal
Tae Jin Park, Kenichi Kumatani, Dimitrios Dimitriadis
October 2021
On Privacy and Confidentiality of Communications in Organizational Graphs
Masoumeh Shafieinejad, Huseyin A. Inan (huinan), Marcello Hasegawa, Robert Sim
May 2021
Locally Private Gaussian Estimation
Matthew Joseph, Janardhan (Jana) Kulkarni, Jieming Mao, Zhiwei Steven Wu
November 2019