Tramèr, F.Huang, Z.Hubaux J.-P.Ayday, Erman2016-02-082016-02-082015-10http://hdl.handle.net/11693/28296Conference name: CCS '15 Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications SecurityDate of Conference: 12 - 16 October, 2015Differential 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 χ2-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. © 2015 ACM.EnglishData-driven medicineDifferential privacyGenomic privacyGWASMembership privacyBudget controlGenesKnowledge managementProbability distributionsBack-ground knowledgeData drivenDifferential privaciesExpected utilityGenome-wide association studiesPrior distributionPrivacy frameworksData privacyConference Paper10.1145/2810103.2813610