Preserving Privacy for Distributed Genome-Wide Analysis Against Identity Tracing Attacks
- Publisher:
- IEEE COMPUTER SOC
- Publication Type:
- Journal Article
- Citation:
- IEEE Transactions on Dependable and Secure Computing, 2023, 20, (4), pp. 3341-3357
- Issue Date:
- 2023-07-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 23‘ TDSC Preserving_Privacy_for_Distributed_Genome-Wide_Analysis_Against_Identity_Tracing_Attacks-2.pdf | Accepted version | 1.27 MB |
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Genome-wide analysis has demonstrated both health and social benefits. However, large scale sharing of such data may reveal sensitive information about individuals. One of the emerging challenges is identity tracing attack that exploits correlations among genomic data to reveal the identity of DNA samples. In this paper, we first demonstrate that the adversary can narrow down the sample's identity by detecting his/her genetic relatives and quantify such privacy threat by employing a Shannon entropy-based measurement. For example, we exemplify that when the dataset size reaches 30% of the population, for any target from that population, the uncertainty of the target's identity is reduced to merely 2.3 bits of entropy (i.e., the identity is pinned down within 5 people). Direct application of existing approaches such as differential privacy (DP), secure multiparty computation (MPC) and homomorphic encryption (HE) may not be applicable to this challenge in genome-wide analysis because of the compromise on utility (i.e., accuracy or efficiency). Towards addressing this challenge, this paper proposes a framework named υFrag to facilitate privacy-preserving data sharing and computation in genome-wide analysis. υFrag mitigates privacy risks by using a vertical fragmentation to disrupt the genetic architecture on which the adversary relies for identity tracing without sacrificing the capability of genome-wide analysis. We theoretically prove that it preserves the correctness of the primitive functionalities and algorithms ranging from basic summary statistics to advanced neural networks. Our experiments demonstrate that υFrag outperforms secure multiparty computation (MPC) and homomorphic encryption (HE) protocols, with a speedup of more than 221x for training neural networks, and also traditional non-private algorithms and a state-of-the-art noise-based differential privacy (DP) solution in most settings.
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