Erman Ayday

This paper introduces a reinforcement learning-based method to dynamically protect genomic data sharing beacons against membership inference attacks by adapting strategies in real time.

This paper introduces a framework to evaluate privacy risks in genomic data sharing by assessing compliance with NIH policies and identifying vulnerabilities through self-reported practices.

The paper proposes a watermarking method for sequential genome data using belief propagation and local differential privacy to detect unauthorized sharing and ensure privacy.

This study shows that including family members in genomic data-sharing beacons reduces re-identification attack success by obscuring variant information, but also causes some utility loss.

This paper demonstrates a novel re-identification attack on genomic beacons by inferring alleles and membership through linkage disequilibrium and Markov chain models, showing higher efficiency than prior methods.