A high-throughput method to detect privacy-sensitive human genomic data

Vinicius V. Cogo, Alysson Bessani, Francisco M. Couto, Paulo Verissimo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations


Finding the balance between privacy protection and data sharing is one of the main challenges in managing human genomic data nowadays. Novel privacy-enhancing technologies are required to address the known disclosure threats to personal sensitive genomic data without precluding data sharing. In this paper, we propose a method that systematically detects privacy-sensitive DNA segments coming directly from an input stream, using as reference a knowledge database of known privacy-sensitive nucleic and amino acid sequences. We show that adding our detection method to standard security techniques provides a robust, efficient privacy-preserving solution that neutralizes threats related to recently published attacks on genome privacy based on short tandem repeats, disease-related genes, and genomic variations. Current global knowledge on human genomes demonstrates the feasibility of our approach to obtain a comprehensive database immediately, which can also evolve automatically to address future attacks as new privacy-sensitive sequences are identified. Additionally, we validate that the detection method can be fitted inline with the NGS - Next Generation Sequencing - production cycle by using Bloom filters and scaling out to faster sequencing machines.
Original languageEnglish (US)
Title of host publicationWPES 2015 - Proceedings of the 2015 ACM Workshop on Privacy in the Electronic Society, co-located with CCS 2015
PublisherAssociation for Computing Machinery, Incacmhelp@acm.org
Number of pages10
ISBN (Print)9781450338202
StatePublished - Oct 12 2015
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2021-03-16


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