MBeacon: Privacy-Preserving Beacons for DNA Methylation Data
Inken Hagestedt, Yang Zhang, Mathias Humbert, Pascal Berrang, Haixu Tang, XiaoFeng Wang, Michael Backes
摘要
The advancement of molecular profiling techniques fuels biomedical research with a deluge of data. To facilitate data sharing, the Global Alliance for Genomics and Health established the Beacon system, a search engine designed to help researchers find datasets of interest. While the current Beacon system only supports genomic data, other types of biomedical data, such as DNA methylation, are also essential for advancing our understanding in the field. In this paper, we propose the first Beacon system for DNA methylation data sharing: MBeacon. As the current genomic Beacon is vulnerable to privacy attacks, such as membership inference, and DNA methylation data is highly sensitive, we take a privacy-by-design approach to construct MBeacon. First, we demonstrate the privacy threat, by proposing a membership inference attack tailored specifically to unprotected methylation Beacons. Our experimental results show that 100 queries are sufficient to achieve a successful attack with AUC (area under the ROC curve) above 0.9. To remedy this situation, we propose a novel differential privacy mechanism, namely SVT2 , which is the core component of MBeacon. Extensive experiments over multiple datasets show that SVT2 can successfully mitigate membership privacy risks without significantly harming utility. We further implement a fully functional prototype of MBeacon which we make available to the research community
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引用它的顶会 Paper11
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- Stealing Links from Graph Neural NetworksXinlei He, Jinyuan Jia, Michael Backes, Neil Zhenqiang Gong 等USENIX Security 2021 · 被引用 226 次
- Membership Leakage in Label-Only ExposuresZheng Li, Yang ZhangCCS 2021 · 被引用 185 次
- When Machine Unlearning Jeopardizes PrivacyMin Chen, Zhikun Zhang, Tianhao Wang, Michael Backes 等CCS 2021 · 被引用 146 次
它引用的顶会 Paper7
- Membership Inference Attacks Against Machine Learning ModelsReza Shokri, Marco Stronati, Congzheng Song, Vitaly ShmatikovS&P 2017 · 被引用 5,137 次
- ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning ModelsAhmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang 等NDSS 2019 · 被引用 1,141 次
- Knock Knock, Who's There? Membership Inference on Aggregate Location DataApostolos Pyrgelis, Carmela Troncoso, Emiliano De CristofaroNDSS 2018 · 被引用 293 次
- Membership Privacy in MicroRNA-based StudiesMichael Backes, Pascal Berrang, Mathias Humbert, Praveen ManoharanCCS 2016 · 被引用 154 次
- walk2friends: Inferring Social Links from Mobility ProfilesMichael Backes, Mathias Humbert, Jun Pang, Yang ZhangCCS 2017 · 被引用 123 次
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