GenoGuard: Protecting Genomic Data against Brute-Force Attacks

Abstract

Secure storage of genomic data is of great and increasing importance. The scientific community's improving ability to interpret individuals' genetic materials and the growing size of genetic database populations have been aggravating the potential consequences of data breaches. The prevalent use of passwords to generate encryption keys thus poses an especially serious problem when applied to genetic data. Weak passwords can jeopardize genetic data in the short term, but given the multi-decade lifespan of genetic data, even the use of strong passwords with conventional encryption can lead to compromise. We present a tool, called Geno Guard, for providing strong protection for genomic data both today and in the long term. Geno Guard incorporates a new theoretical framework for encryption called honey encryption (HE): it can provide information-theoretic confidentiality guarantees for encrypted data. Previously proposed HE schemes, however, can be applied to messages from, unfortunately, a very restricted set of probability distributions. Therefore, Geno Guard addresses the open problem of applying HE techniques to the highly non-uniform probability distributions that characterize sequences of genetic data. In Geno Guard, a potential adversary can attempt exhaustively to guess keys or passwords and decrypt via a brute-force attack. We prove that decryption under any key will yield a plausible genome sequence, and that Geno Guard offers an information-theoretic security guarantee against message-recovery attacks. We also explore attacks that use side information. Finally, we present an efficient and parallelized software implementation of Geno Guard.

DOI: 10.1109/SP.2015.34

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Cite this paper

@article{Huang2015GenoGuardPG, title={GenoGuard: Protecting Genomic Data against Brute-Force Attacks}, author={Zhicong Huang and Erman Ayday and Jacques Fellay and Jean-Pierre Hubaux and Ari Juels}, journal={2015 IEEE Symposium on Security and Privacy}, year={2015}, pages={447-462} }