Countering GATTACA: efficient and secure testing of fully-sequenced human genomes

By Pierre Baldi, Roberta Baronio, Emiliano De Cristofaro, Paolo Gasti, Gene Tsudik. 2011

279 citations recorded by the source corpus.

Recent advances in DNA sequencing technologies have put ubiquitous availability of fully sequenced human genomes within reach. It is no longer hard to imagine the day when everyone will have the means to obtain and store one's own DNA sequence. Widespread and affordable availability of fully sequenced genomes immediately opens up important opportunities in a number of health-related fields. In particular, common genomic applications and tests performed in vitro today will soon be conducted computationally, using digitized genomes. New applications will be developed as genome-enabled medicine becomes increasingly preventive and personalized. However, this progress also prompts significant privacy challenges associated with potential loss, theft, or misuse of genomic data. In this paper, we begin to address genomic privacy by focusing on three important applications: Paternity Tests, Personalized Medicine, and Genetic Compatibility Tests. After carefully analyzing these applications and their privacy requirements, we propose a set of efficient techniques based on private set operations. This allows us to implement in in silico some operations that are currently performed via in vitro methods, in a secure fashion. Experimental results demonstrate that proposed techniques are both feasible and practical today.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Pharmacy

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Citations in the corpus

Secure Federated Submodel Learning (2019, background): “Compared with Private Set Intersection (PSI) [71]–[76] and Private Set union Cardinality (PSC) [77]– [79], which have received tremendous attention and also have seen several practical applications, such as in social networks [80], [81]; human genome testing [82]; locationbased services [83]; securi”
Fast and Private Computation of Cardinality of Set Intersection and Union (2012, methodology): “Finally, efficient PSI-CA protocols are becoming instrumental to privacy-preserving genomic tests, as recently showed in [4].”
Private genome analysis through homomorphic encryption (2015, background): “[7] proposed a cryptographic protocol to determine whether there exists a biological parent-child relationship between two individuals.”
Scalable Private Set Intersection Based on OT Extension (2018, background): “2010), testing of fully sequenced human genomes (Baldi et al. 2011), proximity testing (Narayanan et al.”
Labeled PSI from Fully Homomorphic Encryption with Malicious Security (2018, methodology): “In the case of [5, 35, 47], the receiver learns exactly how many items we inserted and removed fromX .”