research-article

Differential Privacy with Bounded Priors: Reconciling Utility and Privacy in Genome-Wide Association Studies

Authors:

Florian Tramèr,

Jean-Pierre Hubaux,

Erman AydayAuthors Info & Claims

CCS '15: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security

Pages 1286 - 1297

https://doi.org/10.1145/2810103.2813610

Published: 12 October 2015 Publication History

Abstract

Differential privacy (DP) has become widely accepted as a rigorous definition of data privacy, with stronger privacy guarantees than traditional statistical methods. However, recent studies have shown that for reasonable privacy budgets, differential privacy significantly affects the expected utility. Many alternative privacy notions which aim at relaxing DP have since been proposed, with the hope of providing a better tradeoff between privacy and utility.

At CCS'13, Li et al. introduced the membership privacy framework, wherein they aim at protecting against set membership disclosure by adversaries whose prior knowledge is captured by a family of probability distributions. In the context of this framework, we investigate a relaxation of DP, by considering prior distributions that capture more reasonable amounts of background knowledge. We show that for different privacy budgets, DP can be used to achieve membership privacy for various adversarial settings, thus leading to an interesting tradeoff between privacy guarantees and utility.

We re-evaluate methods for releasing differentially private chi²-statistics in genome-wide association studies and show that we can achieve a higher utility than in previous works, while still guaranteeing membership privacy in a relevant adversarial setting.

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https://doi.org/10.1109/OJCOMS.2024.3521940
Kulesza ASuresh AWang YSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Mean estimation in the add-remove model of differential privacyProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693097(25642-25661)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693097
Song CShi X(2024)ReActHE: A homomorphic encryption friendly deep neural network for privacy-preserving biomedical predictionSmart Health10.1016/j.smhl.2024.10046932(100469)Online publication date: Jun-2024
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Index Terms

Differential Privacy with Bounded Priors: Reconciling Utility and Privacy in Genome-Wide Association Studies

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Published In

cover image ACM Conferences

CCS '15: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security

October 2015

1750 pages

ISBN:9781450338325

DOI:10.1145/2810103

General Chair:
Indrajit Ray
Colorado State University, USA
,
Program Chairs:
Ninghui Li
Purdue University, USA
,
Christopher Kruegel
University of California, Santa Barbara, USA

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSAC: ACM Special Interest Group on Security, Audit, and Control

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Association for Computing Machinery

New York, NY, United States

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Published: 12 October 2015

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CCS'15

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SIGSAC

CCS'15: The 22nd ACM Conference on Computer and Communications Security

October 12 - 16, 2015

Colorado, Denver, USA

Acceptance Rates

CCS '15 Paper Acceptance Rate 128 of 660 submissions, 19%;

Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

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CCS '25

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sigsac

ACM SIGSAC Conference on Computer and Communications Security

October 13 - 17, 2025

Taipei , Taiwan

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Cited By

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https://doi.org/10.1109/OJCOMS.2024.3521940
Kulesza ASuresh AWang YSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Mean estimation in the add-remove model of differential privacyProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693097(25642-25661)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693097
Song CShi X(2024)ReActHE: A homomorphic encryption friendly deep neural network for privacy-preserving biomedical predictionSmart Health10.1016/j.smhl.2024.10046932(100469)Online publication date: Jun-2024
https://doi.org/10.1016/j.smhl.2024.100469
Ayday EVaidya JJiang XTelenti A(2023)Ensuring Trust in Genomics Research2023 5th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)10.1109/TPS-ISA58951.2023.00011(1-12)Online publication date: 1-Nov-2023
https://doi.org/10.1109/TPS-ISA58951.2023.00011
Saeidian SCervia GOechtering TSkoglund M(2023)Pointwise Maximal LeakageIEEE Transactions on Information Theory10.1109/TIT.2023.330437869:12(8054-8080)Online publication date: Dec-2023
https://doi.org/10.1109/TIT.2023.3304378
Dong CWeng JLiu JYang ALiu ZYang YMa J(2023)Maliciously Secure and Efficient Large-Scale Genome-Wide Association Study With Multi-Party ComputationIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.315249820:2(1243-1257)Online publication date: 1-Mar-2023
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Liu HLuo HLi SDong TChen GMeng YZhu H(2023)Privacy Computing with Right to Be Forgotten in Trusted Execution EnvironmentGLOBECOM 2023 - 2023 IEEE Global Communications Conference10.1109/GLOBECOM54140.2023.10437471(2566-2571)Online publication date: 4-Dec-2023
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Hamdi HBrahmi ZAlaerjan AMhamdi L(2023)Enhancing Security and Privacy Preservation of Sensitive Information in e-Health Datasets Using FCA ApproachIEEE Access10.1109/ACCESS.2023.328540711(62591-62604)Online publication date: 2023
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Liu WZhang YYang HMeng Q(2023)A Survey on Differential Privacy for Medical Data AnalysisAnnals of Data Science10.1007/s40745-023-00475-311:2(733-747)Online publication date: 10-Jun-2023
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Zhao YChen J(2022)A Survey on Differential Privacy for Unstructured Data ContentACM Computing Surveys10.1145/349023754:10s(1-28)Online publication date: 13-Sep-2022
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