Efficient and secure outsourcing of genomic data storage

• Published in: BMC medical genomics Vol. 10; no. Suppl 2; p. 46
• Main Authors: Sousa, João Sá, Lefebvre, Cédric, Huang, Zhicong, Raisaro, Jean Louis, Aguilar-Melchor, Carlos, Killijian, Marc-Olivier, Hubaux, Jean-Pierre
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 26-07-2017; BioMed Central; BMC
• Subjects: Algorithms; Cloud Computing; Computer Science; Computer Security; Confidentiality; Cryptography and Security; Data integrity; DNA sequencing; Genetic research; Genomes; Genomic variants; Genomics; High-throughput screening (Biochemical assaying); Homomorphic encryption; iDash; Information management; Information processing; Information retrieval; Information sharing; Information Storage and Retrieval - methods; Leakage; Models, Theoretical; Nucleotide sequencing; Outsourced Services - methods; Outsourcing; Privacy; Private information retrieval; Secure outsourcing; Ubiquitous Computing; Private information retrieval; Genomic variants; Secure outsourcing; iDash; Homomorphic encryption
• ISSN: 1755-8794; 1755-8794
• DOI: 10.1186/s12920-017-0275-0

Cloud computing is becoming the preferred solution for efficiently dealing with the increasing amount of genomic data. Yet, outsourcing storage and processing sensitive information, such as genomic data, comes with important concerns related to privacy and security. This calls for new sophisticated techniques that ensure data protection from untrusted cloud providers and that still enable researchers to obtain useful information.
We present a novel privacy-preserving algorithm for fully outsourcing the storage of large genomic data files to a public cloud and enabling researchers to efficiently search for variants of interest. In order to protect data and query confidentiality from possible leakage, our solution exploits optimal encoding for genomic variants and combines it with homomorphic encryption and private information retrieval. Our proposed algorithm is implemented in C++ and was evaluated on real data as part of the 2016 iDash Genome Privacy-Protection Challenge.
Results show that our solution outperforms the state-of-the-art solutions and enables researchers to search over millions of encrypted variants in a few seconds.
As opposed to prior beliefs that sophisticated privacy-enhancing technologies (PETs) are unpractical for real operational settings, our solution demonstrates that, in the case of genomic data, PETs are very efficient enablers.