Department of Computer Engineering
CS 590 SEMINAR
Differentially Private Watermarking for Sequential Data Using Belief Propagation
A. Çağlar Öksüz
Computer Engineering Department
Genome data is a subject of study for both biology and computer science since the start of Human Genome Project in 1990. Since then, genome sequencing for medical and social purposes becomes more and more available and affordable. For research, these genome data can be shared on public websites or with service providers. However, this sharing process compromises the privacy of donors even under partial sharing conditions. In this work, we mainly focus on the liability aspect ensued by unauthorized sharing of these genome data. One of the techniques to address the liability issues in data sharing is watermarking mechanism. In order to detect malicious correspondents and service providers -whose aim is to share genome data without individuals' consent and undetected-, we propose a novel watermarking method on sequential genome data using belief propagation algorithm. In our method, we have two criteria to satisfy. (i) Maximizing the privacy so that the malicious adversaries are always identified and (ii) Maximizing the utility through keeping the changes on original data minimal. For the preservation of system robustness against various attack types, we consider publicly available genomic information like Minor Allele Frequency, Linkage Disequilibrium, Phenotype Information and Familial Information. Also, considering the fact that the attackers may know our optimality strategy in watermarking, we incorporate differential privacy as plausible deniability factor. As opposed to traditional differential privacy-based data sharing schemes in which the noise is added based on summary statistic of population data, noise will be added in local setting based on local probabilities.
DATE: 06 May 2019, Monday, CS590 presentations begin at @ 15:40