Privacy-Preserving and Robust Watermarking on Sequential Genome Data using Belief Propagation and Local Differential Privacy

Abstract

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. Genome data can be shared on public websites or with service providers. However, this sharing compromises the privacy of donors even under partial sharing conditions. 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. To detect malicious correspondents and service providers (SPs) -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) Embedding robust watermarks so that the malicious adversaries can not temper the watermark by modification and are identified with high probability (ii) Achieving ε-local differential privacy in all data sharings with SPs. For the preservation of system robustness against single SP and collusion attacks, we consider publicly available genomic information like Minor Allele Frequency, Linkage Disequilibrium, Phenotype Information and Familial Information. Our proposed scheme achieves 100% detection rate against the single SP attacks with only 3% watermark length. For the worst case scenario of collusion attacks (50% of SPs are malicious), 80% detection is achieved with 5% watermark length and 90% detection is achieved with 10% watermark length. For all cases, ε's impact on precision remained negligible and high privacy is ensured.

Robust fingerprinting of genomic databases

“…However, these two works focus on the genomic data of an individual, instead of a genomic database, where individuals may have kinship relationships. Öksüz et al (2021) develop a watermarking scheme for sequential genomic data based on belief propagation, which considers the privacy of data and the robustness of watermark requirements at the same time. Ji et al (2021a ) propose mitigation techniques against general correlation attacks targeted on generic relational databases and show that the proposed technique can be applied after any existing relational database fingerprinting scheme to achieve robustness against correlation attacks.…”