Genomic Data Privacy Protection using Compressed Sensing
Aminmohammad Roozgard(a),(*), Nafise Barzigar(a), Pramode Verma(a), Samuel Cheng(a)
Transactions on Data Privacy 9:1 (2016) 1 - 13
(a) Department of Electrical and Computer Engineering, University of Oklahoma, 4502 E. 41st St. Tulsa, OK, USA
e-mail:roozgard @ou.edu; barzigar @ou.edu; pverma @ou.edu; samuel.cheng @ou.edu
In this article, we present a privacy preserving genomic data dissemination algorithm based on compressed sensing. We participated in the challenge at the iDASH on March 24, 2014 in La Jolla, California and the result of the challenge are available online. In our proposed method, we are adding noise to the sparse representation of the input vector to make it differentially private. First, we find the sparse representation using SubSpace Pursuit and then perturb it with sufficient Laplacian noise. We also compared our method with a state-of-the-art compressed sensing privacy protection method.