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Proceedings on Privacy Enhancing Technologies

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Efficient Server-Aided Secure Two-Party Function Evaluation with Applications to Genomic Computation

Marina Blanton / Fattaneh Bayatbabolghani
Published Online: 2016-07-14 | DOI: https://doi.org/10.1515/popets-2016-0033

Abstract

Computation based on genomic data is becoming increasingly popular today, be it for medical or other purposes. Non-medical uses of genomic data in a computation often take place in a server-mediated setting where the server offers the ability for joint genomic testing between the users. Undeniably, genomic data is highly sensitive, which in contrast to other biometry types, discloses a plethora of information not only about the data owner, but also about his or her relatives. Thus, there is an urgent need to protect genomic data. This is particularly true when the data is used in computation for what we call recreational non-health-related purposes. Towards this goal, in this work we put forward a framework for server-aided secure two-party computation with the security model motivated by genomic applications. One particular security setting that we treat in this work provides stronger security guarantees with respect to malicious users than the traditional malicious model. In particular, we incorporate certified inputs into secure computation based on garbled circuit evaluation to guarantee that a malicious user is unable to modify her inputs in order to learn unauthorized information about the other user’s data. Our solutions are general in the sense that they can be used to securely evaluate arbitrary functions and offer attractive performance compared to the state of the art. We apply the general constructions to three specific types of genomic tests: paternity, genetic compatibility, and ancestry testing and implement the constructions. The results show that all such private tests can be executed within a matter of seconds or less despite the large size of one’s genomic data.

Keywords: Genomic computation; garbled circuits; server-aided computation; certified inputs

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About the article

Received: 2016-02-29

Revised: 2016-06-02

Accepted: 2016-06-02

Published Online: 2016-07-14

Published in Print: 2016-10-01


Citation Information: Proceedings on Privacy Enhancing Technologies, ISSN (Online) 2299-0984, DOI: https://doi.org/10.1515/popets-2016-0033.

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© 2016. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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