PrivGen : Privacy-preserving sharing and processing of genetic data

Cloud computing has emerged as a successful paradigm allowing individuals and companies to flexibly store and process large amounts of data. However, cloud applications are subject to new security risks and risks to the privacy of data concerning the disclosure, ownership, and integrity of data. These problems are particularly important in the context of the sharing and processing of genetic data that require a multitude of security and privacy properties to be satisfied.

The PrivGen project aims at providing new techniques for making secure and protect the privacy of shared genetic data that is processed using distributed applications, but not only. To do so, PrivGen proposes to develop:

(i) new means for the combination of watermarking, encryption and fragmentation techniques to ensure the security and protection of privacy of shared genetic data,

(ii) a composition theory for security mechanisms that allows the enforcement of security and privacy properties in a constructive manner on the programming level

(iii) new service-based techniques for the distributed processing of shared genetic data.


Research team


Partners’ publications related to the project

D. Bouslimi, G. Coatrieux, M. Cozic, C. Roux. Data hiding in encrypted images based on predefined watermark embedding before encryption process. Sig. Proc.: Image Comm. 47: 263-270, 2016.

D. Bouslimi and G. Coatrieux. Encryption and Watermarking for Medical Image Protection, chapter Medical Data Privacy Handbook. Springer, 2016.

J. Franco-Contreras and G. Coatrieux. Robust watermarking of relational databases with ontology-guided distortion control. IEEE Trans. Information Forensics and Security, 10(9):1939–1952, 2015

R-A. Cherrueau, R. Douence, and M. Südholt. A Language for the Composition of Privacy-Enforcement Techniques. In the 2015 IEEE Int. Symp. RATSP, Aug. 2015.

R-A. Cherrueau, M. Südholt, and O. Chebaro. Adapting workflows using generic schemas: application to the security of business processes. In 5th IEEE Int. Conf. on Cloud Technology and Science , p. 6., Dec. 2013.

B. De Fraine, E. Ernst, and M. Südholt. Essential aop: The a calculus. ACM Trans. Program. Lang. Syst., 34(3):12:1–12:43, Nov. 2012.

A. Saint Pierre and E. Génin. How important are rare variants in common disease?. Briefings in Functional Genomics, 07, 2014.

S. Gazal, M. Sahbatou, M-C. Babron, E. Génin Emmanuelle, and A-L. Leutenegger. Fsuite: exploiting inbreeding in dense snp chip and exome data. Bioinformatics, 03 2014.

M-C. Babron, M. De Tayrac, D.N. Rutledge, E. Zeggini, and E. Génin. Rare and low frequency variant stratification in the uk population: Description and impact on association tests. PLoS ONE, 7(10), 10, (2012)

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