Privacy in the Genomic Era

Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward

Sociotechnical Safeguards for Genomic Data Privacy

Recent developments in a variety of sectors, including health care, research and the direct-to-consumer industry, have led to a dramatic increase in the amount of genomic data that are collected, used and shared. This state of affairs raises new and challenging concerns for personal privacy, both legally and technically. This Review appraises existing and emerging threats to genomic data privacy and discusses how well current legal frameworks and technical safeguards mitigate these concerns. It concludes with a discussion of remaining and emerging challenges and illustrates possible solutions that can balance protecting privacy and realizing the benefits that result from the sharing of genetic information

I-GWAS: Privacy-Preserving Interdependent Genome-Wide Association Studies

Genome-wide Association Studies (GWASes) identify genomic variations that are statistically associated with a trait, such as a disease, in a group of individuals. Unfortunately, careless sharing of GWAS statistics might give rise to privacy attacks. Several works attempted to reconcile secure processing with privacy-preserving releases of GWASes. However, we highlight that these approaches remain vulnerable if GWASes utilize overlapping sets of individuals and genomic variations. In such conditions, we show that even when relying on state-of-the-art techniques for protecting releases, an adversary could reconstruct the genomic variations of up to 28.6% of participants, and that the released statistics of up to 92.3% of the genomic variations would enable membership inference attacks. We introduce I-GWAS, a novel framework that securely computes and releases the results of multiple possibly interdependent GWASes. I-GWAS continuously releases privacy-preserving and noise-free GWAS results as new genomes become available

Privacy Preserving Data Publishing

Recent years have witnessed increasing interest among researchers in protecting individual privacy in the big data era, involving social media, genomics, and Internet of Things. Recent studies have revealed numerous privacy threats and privacy protection methodologies, that vary across a broad range of applications. To date, however, there exists no powerful methodologies in addressing challenges from: high-dimension data, high-correlation data and powerful attackers. In this dissertation, two critical problems will be investigated: the prospects and some challenges for elucidating the attack capabilities of attackers in mining individuals’ private information; and methodologies that can be used to protect against such inference attacks, while guaranteeing significant data utility. First, this dissertation has proposed a series of works regarding inference attacks laying emphasis on protecting against powerful adversaries with auxiliary information. In the context of genomic data, data dimensions and computation feasibility is highly challenging in conducting data analysis. This dissertation proved that the proposed attack can effectively infer the values of the unknown SNPs and traits in linear complexity, which dramatically improve the computation cost compared with traditional methods with exponential computation cost. Second, putting differential privacy guarantee into high-dimension and high-correlation data remains a challenging problem, due to high-sensitivity, output scalability and signal-to-noise ratio. Consider there are tens-of-millions of genomes in a human DNA, it is infeasible for traditional methods to introduce noise to sanitize genomic data. This dissertation has proposed a series of works and demonstrated that the proposed differentially private method satisfies differential privacy; moreover, data utility is improved compared with the states of the arts by largely lowering data sensitivity. Third, putting privacy guarantee into social data publishing remains a challenging problem, due to tradeoff requirements between data privacy and utility. This dissertation has proposed a series of works and demonstrated that the proposed methods can effectively realize privacy-utility tradeoff in data publishing. Finally, two future research topics are proposed. The first topic is about Privacy Preserving Data Collection and Processing for Internet of Things. The second topic is to study Privacy Preserving Big Data Aggregation. They are motivated by the newly proposed data mining, artificial intelligence and cybersecurity methods

A Game Theoretic Approach to Balance Privacy Risks and Familial Benefits

As recreational genomics continues to grow in its popularity, many people are afforded the opportunity to share their genomes in exchange for various services, including third-party interpretation (TPI) tools, to understand their predisposition to health problems and, based on genome similarity, to find extended family members. At the same time, these services have increasingly been reused by law enforcement to track down potential criminals through family members who disclose their genomic information. While it has been observed that many potential users shy away from such data sharing when they learn that their privacy cannot be assured, it remains unclear how potential users’ valuations of the service will affect a population’s behavior. In this paper, we present a game theoretic framework to model interdependent privacy challenges in genomic data sharing online. Through simulations, we find that in addition to the boundary cases when (1) no player and (2) every player joins, there exist pure-strategy Nash equilibria when a relatively small portion of players choose to join the genomic database. The result is consistent under different parametric settings. We further examine the stability of Nash equilibria and illustrate that the only equilibrium that is resistant to a random dropping of players is when all players join the genomic database. Finally, we show that when players consider the impact that their data sharing may have on their relatives, the only pure strategy Nash equilibria are when either no player or every player shares their genomic data

Quantifying genomic privacy via inference attack with high-order SNV correlations

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 identified on genomic data, and thus break the privacy expectations of individuals who contribute their genomic data to medical research, or simply share their data online. Frustrating as it is, the problem could become even worse. Existing genomic privacy breaches rely on low-order SNV (Single Nucleotide Variant) correlations. Our work shows that far more powerful attacks can be designed if high-order correlations are utilized. We corroborate this concern by making use of different SNV correlations based on various genomic data models and applying them to an inference attack on individuals' genotype data with hidden SNVs. We also show that low-order models behave very differently from real genomic data and therefore should not be relied upon for privacy-preserving solutions. © 2015 IEEE