Privacy-safe linkage analysis with homomorphic encryption

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)

Abstract

Genetic data are important dataset utilised in genetic epidemiology to investigate biologically coded information within the human genome. Enormous research has been delved into in recent years in order to fully sequence and understand the genome. Personalised medicine, patient response to treatments and relationships between specific genes and certain characteristics such as phenotypes and diseases, are positive impacts of studying the genome, just to mention a few. The sensitivity, longevity and non-modifiable nature of genetic data make it even more interesting, consequently, the security and privacy for the storage and processing of genomic data beg for attention. A common activity carried out by geneticists is the association analysis between allele-allele, or even a genetic locus and a disease. We demonstrate the use of cryptographic techniques such as homomorphic encryption schemes and multiparty computations, how such analysis can be carried out in a privacy friendly manner. We compute a 3 × 3 contingency table, and then, genome analyses algorithms such as linkage disequilibrium (LD) measures, all on the encrypted domain. Our computation guarantees privacy of the genome data under our security settings, and provides up to 98:4% improvement, compared to an existing solution.

Original languageEnglish
Title of host publication25th European Signal Processing Conference, EUSIPCO 2017
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages961-965
Number of pages5
ISBN (Electronic)978-0-9928626-7-1
DOIs
Publication statusPublished - 2017
EventEUSIPCO 2017: 25th European Signal Processing Conference - Kos Island, Greece
Duration: 28 Aug 20172 Sep 2017
Conference number: 25
https://www.eusipco2017.org/

Conference

ConferenceEUSIPCO 2017
Abbreviated titleEUSIPCO
CountryGreece
CityKos Island
Period28/08/172/09/17
Internet address

Keywords

  • Genomics
  • Bioinformatics
  • Encryption
  • Data privacy
  • Diseases

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