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Mechanisms for Hiding Sensitive Genotypes with Information-Theoretic Privacy.

Fangwei Ye1, Hyunghoon Cho2, Salim El Rouayheb3

  • 1Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.

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|June 7, 2023
PubMed
Summary

Sharing personal genomic data requires privacy. This study introduces a novel mechanism to protect sensitive genetic information, ensuring privacy while enabling data sharing. The method offers robust protection against genotype leakage.

Keywords:
Information-theoretic privacydata sanitizationgenomic data sharinggenomic privacyhidden Markov models

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Area of Science:

  • Genomics
  • Information Theory
  • Computer Science

Background:

  • Personal genomics services are increasing, raising concerns about genomic data privacy.
  • Sharing genome sequences can inadvertently reveal sensitive health-related information.
  • Simple masking of genotypes is insufficient due to correlations between genetic positions.

Purpose of the Study:

  • To develop an information-theoretic privacy mechanism for sharing genomic data.
  • To ensure sensitive genotypes remain hidden while releasing other sequence information.
  • To achieve perfect information-theoretic privacy for genomic data sharing.

Main Methods:

  • Introduced an erasure-based privacy mechanism.
  • Interpreted the mechanism as a greedy algorithm optimizing position release.
  • Analyzed the computational complexity for sequences modeled by hidden Markov models.
  • Bounded privacy leakage from erroneous prior distributions.

Main Results:

  • The proposed mechanism achieves perfect information-theoretic privacy, making the released sequence statistically independent of sensitive genotypes.
  • Finding the optimal processing order for the mechanism is NP-hard.
  • An efficient polynomial-time algorithm was developed for hidden Markov model sequences.
  • The mechanism demonstrates robustness against errors in prior distribution estimations.

Conclusions:

  • The developed erasure-based mechanism provides a rigorous solution for privacy in genomic data sharing.
  • The findings offer a pathway towards enhanced control over sensitive information in personal genomics.
  • Further research can build upon this foundation for more secure genomic data applications.