Privacy-Preserving Search of Similar Patients in Genomic Data

The growing availability of genomic data holds great promise for advancing medicine and research, but unlocking its full potential requires adequate methods for protecting the privacy of individuals whose genome data we use. One example of this tension is running Similar Patient Query on remote geno...

Full description

Bibliographic Details
Main Authors: Asharov Gilad, Halevi Shai, Lindell Yehuda, Rabin Tal
Format: Article
Language:English
Published: Sciendo 2018-10-01
Series:Proceedings on Privacy Enhancing Technologies
Subjects:
Online Access:https://doi.org/10.1515/popets-2018-0034
id doaj-b950d86e991d4fa3a4ecf7e1bf1834ef
record_format Article
spelling doaj-b950d86e991d4fa3a4ecf7e1bf1834ef2021-09-05T13:59:52ZengSciendoProceedings on Privacy Enhancing Technologies2299-09842018-10-012018410412410.1515/popets-2018-0034popets-2018-0034Privacy-Preserving Search of Similar Patients in Genomic DataAsharov Gilad0Halevi Shai1Lindell Yehuda2Rabin Tal3Cornell Tech, NY.IBM Research, NY.Bar-Ilan University, Israel.IBM Research, NY.The growing availability of genomic data holds great promise for advancing medicine and research, but unlocking its full potential requires adequate methods for protecting the privacy of individuals whose genome data we use. One example of this tension is running Similar Patient Query on remote genomic data: In this setting a doctor that holds the genome of his/her patient may try to find other individuals with “close” genomic data, and use the data of these individuals to help diagnose and find effective treatment for that patient’s conditions. This is clearly a desirable mode of operation. However, the privacy exposure implications are considerable, and so we would like to carry out the above “closeness” computation in a privacy preserving manner.https://doi.org/10.1515/popets-2018-0034genomic privacycryptographic protocolsedit-distance
collection DOAJ
language English
format Article
sources DOAJ
author Asharov Gilad
Halevi Shai
Lindell Yehuda
Rabin Tal
spellingShingle Asharov Gilad
Halevi Shai
Lindell Yehuda
Rabin Tal
Privacy-Preserving Search of Similar Patients in Genomic Data
Proceedings on Privacy Enhancing Technologies
genomic privacy
cryptographic protocols
edit-distance
author_facet Asharov Gilad
Halevi Shai
Lindell Yehuda
Rabin Tal
author_sort Asharov Gilad
title Privacy-Preserving Search of Similar Patients in Genomic Data
title_short Privacy-Preserving Search of Similar Patients in Genomic Data
title_full Privacy-Preserving Search of Similar Patients in Genomic Data
title_fullStr Privacy-Preserving Search of Similar Patients in Genomic Data
title_full_unstemmed Privacy-Preserving Search of Similar Patients in Genomic Data
title_sort privacy-preserving search of similar patients in genomic data
publisher Sciendo
series Proceedings on Privacy Enhancing Technologies
issn 2299-0984
publishDate 2018-10-01
description The growing availability of genomic data holds great promise for advancing medicine and research, but unlocking its full potential requires adequate methods for protecting the privacy of individuals whose genome data we use. One example of this tension is running Similar Patient Query on remote genomic data: In this setting a doctor that holds the genome of his/her patient may try to find other individuals with “close” genomic data, and use the data of these individuals to help diagnose and find effective treatment for that patient’s conditions. This is clearly a desirable mode of operation. However, the privacy exposure implications are considerable, and so we would like to carry out the above “closeness” computation in a privacy preserving manner.
topic genomic privacy
cryptographic protocols
edit-distance
url https://doi.org/10.1515/popets-2018-0034
work_keys_str_mv AT asharovgilad privacypreservingsearchofsimilarpatientsingenomicdata
AT halevishai privacypreservingsearchofsimilarpatientsingenomicdata
AT lindellyehuda privacypreservingsearchofsimilarpatientsingenomicdata
AT rabintal privacypreservingsearchofsimilarpatientsingenomicdata
_version_ 1717812847713452032