Differential privacy in data publication and analysis
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Privacy-preserving data exploration in genome-wide association studies
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Addressing the concerns of the lacks family: quantification of kin genomic privacy
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
Protecting and evaluating genomic privacy in medical tests and personalized medicine
Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society
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Traditional statistical methods for the confidentiality protection for statistical databases do not scale well to deal with GWAS (genome-wide association studies) databases and external information on them. The more recent concept of differential privacy, introduced by the cryptographic community, is an approach which provides a rigorous definition of privacy with meaningful privacy guarantees in the presence of arbitrary external information. Building on such notions, we propose new methods to release aggregate GWAS data without compromising an individual's privacy. We present methods for releasing differentially private minor allele frequencies, chi-square statistics and p-values. We compare these approaches on simulated data and on a GWAS study of canine hair length involving 685 dogs. We also propose a privacy-preserving method for finding genome-wide associations based on a differentially private approach to penalized logistic regression.