Differential privacy with bounded priors: Reconciling utility and privacy in genome-wide association studies
Proceedings of the ACM Conference on Computer and Communications Security
Association for Computing Machinery
Please cite this item using this persistent URLhttp://hdl.handle.net/11693/28296
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 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.
- Conference Paper 
Showing items related by title, author, creator and subject.
Samani, S.S.; Huang, Z.; Ayday, E.; Elliot, M.; Fellay J.; Hubaux J.-P.; Kutalik, Z. (Institute of Electrical and Electronics Engineers Inc., 2015)As genomic data becomes widely used, the problem of genomic data privacy becomes a hot interdisciplinary research topic among geneticists, bioinformaticians and security and privacy experts. Practical attacks have been ...
Ayday E.; Humbert M. (Institute of Electrical and Electronics Engineers Inc., 2017)Genomic data poses serious interdependent risks: your data might also leak information about your family members' data. Methods attackers use to infer genomic information, as well as recent proposals for enhancing genomic ...
Ayday, E.; De Cristofaro, E.; Hubaux J.-P.; Tsudik G. (IEEE Computer Society, 2015)Whole genome sequencing will soon become affordable for many individuals, but thorny privacy and ethical issues could jeopardize its popularity and thwart the large-scale adoption of genomics in healthcare and slow potential ...