@inproceedings{laine2017private,
author = {Laine, Kim and Lauter, Kristin and Chen, Hao and Rindal, Peter and Çetin, Gizem S. and Xia, Yuhou},
title = {Private queries on encrypted genomic data},
booktitle = {iDASH Privacy and Security Workshop 2016},
year = {2017},
month = {July},
abstract = {Background
One of the tasks in the iDASH Secure Genome Analysis Competition in 2016 was to demonstrate the feasibility of privacy-preserving queries on homomorphically encrypted genomic data. More precisely, given a list of up to 100,000 mutations, the task was to encrypt the data using homomorphic encryption in a way that allows it to be stored securely in the cloud, and enables the data owner to query the dataset for the presence of specific mutations, without revealing any information about the dataset or the queries to the cloud.



Methods
We devise a novel string matching protocol to enable privacy-preserving queries on homomorphically encrypted data. Our protocol combines state-of-the-art techniques from homomorphic encryption and private set intersection protocols to minimize the computational and communication cost.



Results
We implemented our protocol using the homomorphic encryption library SEAL v2.1, and applied it to obtain an efficient solution to the iDASH competition task. For example, using 8 threads, our protocol achieves a running time of only 4 s, and a communication cost of 2 MB, when querying for the presence of 5 mutations from an encrypted dataset of 100,000 mutations.



Conclusions
We demonstrate that homomorphic encryption can be used to enable an efficient privacy-preserving mechanism for querying the presence of particular mutations in realistic size datasets. Beyond its applications to genomics, our protocol can just as well be applied to any kind of data, and is therefore of independent interest to the homomorphic encryption community.},
publisher = {BMC Medical Genomics},
url = {http://approjects.co.za/?big=en-us/research/publication/private-queries-encrypted-genomic-data/},
volume = {10 (Suppl 2)},
edition = {iDASH Privacy and Security Workshop 2016},
}