Differentially Private Data Publishing and Analysis: A Survey <s> Differential Privacy in Genetic Data <s> Genome-wide association studies (GWAS) have become a popular method for analyzing sets of DNA sequences in order to discover the genetic basis of disease. Unfortunately, statistics published as the result of GWAS can be used to identify individuals participating in the study. To prevent privacy breaches, even previously published results have been removed from public databases, impeding researchers' access to the data and hindering collaborative research. Existing techniques for privacy-preserving GWAS focus on answering specific questions, such as correlations between a given pair of SNPs (DNA sequence variations). This does not fit the typical GWAS process, where the analyst may not know in advance which SNPs to consider and which statistical tests to use, how many SNPs are significant for a given dataset, etc. We present a set of practical, privacy-preserving data mining algorithms for GWAS datasets. Our framework supports exploratory data analysis, where the analyst does not know a priori how many and which SNPs to consider. We develop privacy-preserving algorithms for computing the number and location of SNPs that are significantly associated with the disease, the significance of any statistical test between a given SNP and the disease, any measure of correlation between SNPs, and the block structure of correlations. We evaluate our algorithms on real-world datasets and demonstrate that they produce significantly more accurate results than prior techniques while guaranteeing differential privacy. <s> BIB001 </s> Differentially Private Data Publishing and Analysis: A Survey <s> Differential Privacy in Genetic Data <s> 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. <s> BIB002 </s> Differentially Private Data Publishing and Analysis: A Survey <s> Differential Privacy in Genetic Data <s> 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 chi2-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. <s> BIB003
Given the advances in genome sequencing technology, highly detailed genetic data are being generated inexpensively at exponential rates. The collection and analysis of these data may accelerate biomedical discoveries and support various applications, including personalized medical services. Despite all the benefits, Naveed et al. BIB002 reviewed the mitigation strategies for a wide variety of attacks and contextualized them from the perspective of medicine and public policy. Several initial genome-wide association studies (GWAS) have explored the potential of differentially private data publishing by shifting the original location of the variants. Johnson et al. BIB001 developed a framework for ab initio exploration of case-control GWAS. This framework provides privacy-preserving answers to some key GWAS queries. They designed the operators to output the differentially private number of SNPs associated with the disease and other related statistical results. By considering protection against set membership disclosure, Tramer et al. BIB003 proposed a relaxation of the adversarial model of differential privacy and showed that this weaker setting achieves higher utility.