Research Focus: Week of October 7, 2024

NEW RESEARCH

Securely Training Decision Trees Efficiently

In a recent paper: Securely Training Decision Trees Efficiently that will appear at ACM CCS 2024, researchers from Microsoft significantly reduce the communication complexity of secure decision tree training. Decision trees are an important class of supervised learning algorithms. In this approach, a classification or regression tree is built based on a set of features or attributes present in the training dataset. As with many learning algorithms, the accuracy of decision trees can be greatly improved with larger volumes of data. However, this can be a challenge, since data may come from multiple independent sources and require attention to data privacy concerns. In this case, the use of a privacy-enhancing technology, such as secure multi-party computation (MPC), can help protect the underlying training data.  

When the number of elements in the dataset is 𝑁, the number of attributes is 𝑚 and the height of the tree to be built is ℎ, the researchers construct a protocol with communication complexity O(𝑚𝑁 log 𝑁 + ℎ𝑚𝑁 + ℎ𝑁 log 𝑁 ), thereby achieving an improvement of ≈ min(ℎ, 𝑚, log 𝑁 ) over the previous state of the art. The essential feature is an improved protocol to regroup sorted private elements further into additional groups (according to a flag vector) while maintaining their relative ordering. Implementing this protocol in the MP-SPDZ framework shows that it requires 10× lesser communication and is 9× faster than existing approaches.

NEW RESEARCH

Multi-label audio classification with a noisy zero-shot teacher

Improving the real-world accuracy of audio content detection (ACD) is an important problem for streaming platforms, operating systems and playback devices. It’s similar to audio tagging, i.e., labeling sounds present in a given audio segment of several seconds length or longer. However, ACD may consist of a small number of higher-level labels or super-classes, e.g. speech, music, traffic, machines, animals, etc., where each label can include a multitude of specific sounds.

In a recent paper: Multi-label audio classification with a noisy zero-shot teacher, researchers from Microsoft propose a novel training scheme using self-label correction and data augmentation methods to deal with noisy labels and improve real-world accuracy on a polyphonic audio content detection task. The augmentation method reduces label noise by mixing multiple audio clips and joining their labels, while being compatible with multiple active labels. The researchers show that performance can be improved by a self-label correction method using the same pretrained model. They also show that it is feasible to use a strong zero-shot model such as CLAP to generate labels for unlabeled data and improve the results using the proposed training and label enhancement methods. The resulting model performs similar to CLAP while providing an efficient mobile device friendly architecture which can be quickly adapted to unlabeled sound classes. 

NEW RESEARCH

Tabularis Revilio: Converting Text to Tables

Tables are commonly used to store and present data. These tables are often moved as free-form text when copied from documents and applications without proper tabular support like PDF documents, web pages, or images. Users are dependent on manual effort or programming abilities to parse this free-form text back into structured tables.

In a recent paper: Tabularis Revilio: Converting Text to Tables, researchers from Microsoft present a novel neurosymbolic system for reconstructing tables when their column boundaries have been lost. Revilio addresses this task by detecting headers, generating an initial table sketch using a large language model (LLM), and using that sketch as a guiding representation during an enumerate-and-test strategy that evaluates syntactic and semantic table structures. Revilio was evaluated on a diverse set of datasets, demonstrating significant improvements over existing table parsing methods. Revilio outperforms traditional techniques in both accuracy and scalability, handling large tables with over 100,000 rows. The researchers’ experiments using publicly available datasets show an increase in reconstruction accuracy by 5.8–11.3% over both neural and symbolic baseline state-of-the-art systems. 

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NEW RESEARCH

Confidential Container Groups: Implementing Confidential Computing on Azure Container Instances

Container-based technologies empower cloud tenants to develop highly portable software and deploy services in the cloud at a rapid pace. Cloud privacy, meanwhile, is important as a large number of container deployments operate on privacy-sensitive data, but challenging due to the increasing frequency and sophistication of attacks. State-of-the-art confidential container-based designs leverage process-based trusted execution environments (TEEs), but face security and compatibility issues that limit their practical deployment.

In a recent article in Communications of the ACM: Confidential Container Groups: Implementing Confidential Computing on Azure Container Instances (opens in new tab), researchers from Microsoft with external colleagues present the Parma architecture, which provides lift-and-shift deployment of unmodified containers while providing strong security protection against a powerful attacker who controls the untrusted host and hypervisor. Parma leverages VM-level isolation to execute a container group within a unique VM-based TEE. Besides container integrity and user data confidentiality and integrity, Parma also offers container attestation and execution integrity based on an attested execution policy. This policy, which is specified by the customer, delimits the actions that the cloud service provider is allowed to take on their behalf when managing the container group. 

The result is that customers receive the security protections of TEEs for their container workloads with minimal costs to perfromance. To learn more, check out Confidential Containers on Azure Container Instances (opens in new tab), which is based on Microsoft’s Parma architecture. 

NEW VIDEO SERIES

AI for Business Transformation with Peter Lee and Vijay Mital

Generative AI is changing how businesses operate and how stakeholders talk to each other. The building blocks for large scale AI transformation are now in place, but we are only beginning to imagine how it will unfold. Learn what Microsoft research leaders discovered from some early AI innovation in healthcare, and how businesses can prepare for what’s ahead.

In this new three-part video series, Microsoft Research President Peter Lee and Corporate Vice President Vijay Mital discuss how Microsoft is helping businesses navigate this transformation, along with the critical role of data and how emerging multimodal AI models could turbocharge business innovation.