IGenoPri: Privacy-preserving genomic data processing with integrity and correctness proofs

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Abstract

Nowadays, governmental and non-governmental health organisations and insurance companies invest in integrating an individual's genetic information to their daily practices. In this paper, we focus on an emerging area of genome analysis, called Disease Susceptibility (DS), from which an individual's susceptibility to a disease is calculated by using her genetic information. Recent work by Danezis et al. [1] presents an approach for calculating DS in a privacy-preserving manner. However, the proposed solution has two drawbacks. First, it does not provide a mechanism to check the integrity of genomic data that is used to calculate the susceptibility and more importantly the computed result. Second, it lacks a mechanism to check the correctness of the performed DS test. In this paper, we present iGenoPri that aims at addressing both problems by employing the Message Authentication Code (MAC) and verifiable computing.

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Turkmen, F., Asghar, M. R., & Demchenko, Y. (2016). IGenoPri: Privacy-preserving genomic data processing with integrity and correctness proofs. In 2016 14th Annual Conference on Privacy, Security and Trust, PST 2016 (pp. 407–410). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/PST.2016.7906964

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