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Secure genomic data sharing with encryption-based privacy preserving tools

Room 2.95, 06/12/2018 (11:30-12:15)

Making at least 1 Mil genomes accessible by 2022 is the target set by the EU in the Declaration on cross-border access to genomic databases (10 April 2018). To pinpoint disease genetic bases, large datasets have to be gathered through vast collaborative efforts. Meanwhile, genetic data represent very sensitive information carrying high risk of identification, needing special protection.

To this end, different types of privacy-preserving (PP) methods can be put in place to avoid disclosure, such as AI-based homomorphic encryption (HE) or secure multi-party computation (SMC), which allow distrustful parties to perform computation directly on encrypted data or in a distributed manner, while remaining oblivious to the input data and the intermediate results.

Another approach is based on generating synthetic data, artificial data generated by machine learning algorithms through recursive conditional parameter aggregation. While retaining significant information, these data do not belong by definition to any existing person, which puts them out of the scope of the GDPR and make them freely tradeable. Synthetic replicas of gene expression, single nucleotide polymorphism, copy number variation or protein-protein/gene-gene interaction data are possible ways for dealing with the genome-sharing challenge.


Edwin Morley-Fletcher, Lynkeus

Davide Zaccagnini, Lynkeus

Lucian Itu, Transylvania University of Brasov

Minos Garofalakis, Athena Research and Innovation Centre

Organised by: Mirko DE MALDÈ (Lynkeus, Italy)



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