AnoniMME: Bringing Anonymity to the Matchmaker Exchange Platform for Rare Disease Gene Discovery

Oprisanu, Bristena; Cristofaro, Emiliano De

doi:10.1101/262295

Cited by 2 publications

(2 citation statements)

References 17 publications

(33 reference statements)

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“…Data sharing is essential for advancing biomedical research. However, large-scale data sharing has been limited, primarily due to privacy concerns [12], [22], [2], [24]. Homer et al [16] have shown that an adversary can effectively predict the presence of an individual in a genomic dataset.…”

Section: Introductionmentioning

confidence: 99%

MBeacon: Privacy-Preserving Beacons for DNA Methylation Data

Hagestedt

Zhang

Humbert

et al. 2019

Proceedings 2019 Network and Distributed System Security Symposium

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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 SVT 2 , which is the core component of MBeacon. Extensive experiments over multiple datasets show that SVT 2 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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Section: Introductionmentioning

confidence: 99%

MBeacon: Privacy-Preserving Beacons for DNA Methylation Data

Hagestedt

Zhang

Humbert

et al. 2019

Proceedings 2019 Network and Distributed System Security Symposium

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“…Finally, Oprisanu and De Cristofaro propose a privacy-enhanced version [56] of the Matchmaker Exchange (MME) platform [10]. More specifically, they rely on Reverse Private Information Retrieval (PIR) to allow researchers to query a gene within the MME platform anonymously.…”

Section: Related Workmentioning

confidence: 99%

Privacy-Preserving Similar Patient Queries for Combined Biomedical Data

Salem

Berrang

Humbert

et al. 2018

Proceedings on Privacy Enhancing Technologies

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The decreasing costs of molecular profiling have fueled the biomedical research community with a plethora of new types of biomedical data, enabling a breakthrough towards more precise and personalized medicine. Naturally, the increasing availability of data also enables physicians to compare patients’ data and treatments easily and to find similar patients in order to propose the optimal therapy. Such similar patient queries (SPQs) are of utmost importance to medical practice and will be relied upon in future health information exchange systems. While privacy-preserving solutions have been previously studied, those are limited to genomic data, ignoring the different newly available types of biomedical data. In this paper, we propose new cryptographic techniques for finding similar patients in a privacy-preserving manner with various types of biomedical data, including genomic, epigenomic and transcriptomic data as well as their combination. We design protocols for two of the most common similarity metrics in biomedicine: the Euclidean distance and Pearson correlation coefficient. Moreover, unlike previous approaches, we account for the fact that certain locations contribute differently to a given disease or phenotype by allowing to limit the query to the relevant locations and to assign them different weights. Our protocols are specifically designed to be highly efficient in terms of communication and bandwidth, requiring only one or two rounds of communication and thus enabling scalable parallel queries. We rigorously prove our protocols to be secure based on cryptographic games and instantiate our technique with three of the most important types of biomedical data – namely DNA, microRNA expression, and DNA methylation. Our experimental results show that our protocols can compute a similarity query over a typical number of positions against a database of 1,000 patients in a few seconds. Finally, we propose and formalize strategies to mitigate the threat of malicious users or hospitals.

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AnoniMME: Bringing Anonymity to the Matchmaker Exchange Platform for Rare Disease Gene Discovery

Cited by 2 publications

References 17 publications

MBeacon: Privacy-Preserving Beacons for DNA Methylation Data

MBeacon: Privacy-Preserving Beacons for DNA Methylation Data

Privacy-Preserving Similar Patient Queries for Combined Biomedical Data

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