Quantifying Genomic Privacy via Inference Attack with High-Order SNV Correlations

Sahel Shariati Samani; Zhicong Huang; E. Ayday; Mark Elliot; J. Fellay; J. P. Hubaux; Z. Kutalik

doi:10.1109/SPW.2015.21

Quantifying Genomic Privacy via Inference Attack with High-Order SNV Correlations

Sahel Shariati Samani, Zhicong Huang, E. Ayday, Mark Elliot, J. Fellay, J. P. Hubaux, Z. Kutalik

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Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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Abstract

As genomic data becomes widely used, the problem
of genomic data privacy becomes a hot interdisciplinary research
topic among geneticists, bioinformaticians and security and pri-
vacy experts. Practical attacks have been identified on genomic
data, and thus break the privacy expectations of individuals who
contribute their genomic data to medical research, or simply
share their data online. Frustrating as it is, the problem could
become even worse. Existing genomic privacy breaches rely on
low-order SNV (Single Nucleotide Variant) correlations. Our
work shows that far more powerful attacks can be designed if
high-order correlations are utilized. We corroborate this concern
by making use of different SNV correlations based on various
genomic data models and applying them to an inference attack on
individuals’ genotype data with hidden SNVs. We also show that
low-order models behave very differently from real genomic data
and therefore should not be relied upon for privacy-preserving
solutions.

Original language	English
Title of host publication	Proceedings of the 2015 IEEE Security and Privacy Workshops
Subtitle of host publication	San Jose, CA, 21-22 May 2015
Publisher	IEEE Computer Society
Pages	32-40
Number of pages	9
DOIs	https://doi.org/10.1109/SPW.2015.21
Publication status	Published - 2015

Publication series

Name	2015 IEEE CS Security and Privacy Workshops

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10.1109/SPW.2015.21

9933a032Licence: Unspecified

Cite this

Shariati Samani, S., Huang, Z., Ayday, E., Elliot, M., Fellay, J., Hubaux, J. P., & Kutalik, Z. (2015). Quantifying Genomic Privacy via Inference Attack with High-Order SNV Correlations. In Proceedings of the 2015 IEEE Security and Privacy Workshops : San Jose, CA, 21-22 May 2015 (pp. 32-40). (2015 IEEE CS Security and Privacy Workshops). IEEE Computer Society . https://doi.org/10.1109/SPW.2015.21

@inproceedings{6fc377eb62f64995b485eca5e37a66d1,

title = "Quantifying Genomic Privacy via Inference Attack with High-Order SNV Correlations",

abstract = "As genomic data becomes widely used, the problemof genomic data privacy becomes a hot interdisciplinary researchtopic among geneticists, bioinformaticians and security and pri-vacy experts. Practical attacks have been identified on genomicdata, and thus break the privacy expectations of individuals whocontribute their genomic data to medical research, or simplyshare their data online. Frustrating as it is, the problem couldbecome even worse. Existing genomic privacy breaches rely onlow-order SNV (Single Nucleotide Variant) correlations. Ourwork shows that far more powerful attacks can be designed ifhigh-order correlations are utilized. We corroborate this concernby making use of different SNV correlations based on variousgenomic data models and applying them to an inference attack onindividuals{\textquoteright} genotype data with hidden SNVs. We also show thatlow-order models behave very differently from real genomic dataand therefore should not be relied upon for privacy-preservingsolutions.",

author = "{Shariati Samani}, Sahel and Zhicong Huang and E. Ayday and Mark Elliot and J. Fellay and Hubaux, {J. P.} and Z. Kutalik",

year = "2015",

doi = "10.1109/SPW.2015.21",

language = "English",

series = "2015 IEEE CS Security and Privacy Workshops",

publisher = "IEEE Computer Society ",

pages = "32--40",

booktitle = "Proceedings of the 2015 IEEE Security and Privacy Workshops",

address = "United States",

}

Shariati Samani, S, Huang, Z, Ayday, E, Elliot, M, Fellay, J, Hubaux, JP & Kutalik, Z 2015, Quantifying Genomic Privacy via Inference Attack with High-Order SNV Correlations. in Proceedings of the 2015 IEEE Security and Privacy Workshops : San Jose, CA, 21-22 May 2015 . 2015 IEEE CS Security and Privacy Workshops, IEEE Computer Society , pp. 32-40. https://doi.org/10.1109/SPW.2015.21

Quantifying Genomic Privacy via Inference Attack with High-Order SNV Correlations. / Shariati Samani, Sahel; Huang, Zhicong; Ayday, E. et al.
Proceedings of the 2015 IEEE Security and Privacy Workshops : San Jose, CA, 21-22 May 2015 . IEEE Computer Society , 2015. p. 32-40 (2015 IEEE CS Security and Privacy Workshops).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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AU - Elliot, Mark

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AU - Hubaux, J. P.

AU - Kutalik, Z.

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AB - As genomic data becomes widely used, the problemof genomic data privacy becomes a hot interdisciplinary researchtopic among geneticists, bioinformaticians and security and pri-vacy experts. Practical attacks have been identified on genomicdata, and thus break the privacy expectations of individuals whocontribute their genomic data to medical research, or simplyshare their data online. Frustrating as it is, the problem couldbecome even worse. Existing genomic privacy breaches rely onlow-order SNV (Single Nucleotide Variant) correlations. Ourwork shows that far more powerful attacks can be designed ifhigh-order correlations are utilized. We corroborate this concernby making use of different SNV correlations based on variousgenomic data models and applying them to an inference attack onindividuals’ genotype data with hidden SNVs. We also show thatlow-order models behave very differently from real genomic dataand therefore should not be relied upon for privacy-preservingsolutions.

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Quantifying Genomic Privacy via Inference Attack with High-Order SNV Correlations

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