Emerging technologies towards enhancing privacy in genomic data sharing
By Bonnie Berger, Hyunghoon Cho. 2019
62 citations recorded by the source corpus.
As the scale of genomic and health-related data explodes and our understanding of these data matures, the privacy of the individuals behind the data is increasingly at stake. Traditional approaches to protect privacy have fundamental limitations. Here we discuss emerging privacy-enhancing technologies that can enable broader data sharing and collaboration in genomics research.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Sociology and Political Science
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In Artificial Intelligence
- Or Sheffet
- Aleksandra Korolova
- Uri Stemmer
- Kobbi Nissim
- Amos Beimel
- Jonathan Ullman
- Vitaly Feldman
- Mark Bun
In Electrical and Electronic Engineering
- Uri Stemmer
- Kobbi Nissim
- Aleksandra Korolova
- Or Sheffet
- Amos Beimel
- Adam D. Smith
- Mark Bun
- Kunal Talwar
In Sociology and Political Science
- Aleksandra Korolova
- Kobbi Nissim
- Amos Beimel
- Or Sheffet
- Uri Stemmer
- Vitaly Feldman
- Krishnaram Kenthapadi
- Jan Vondr'ak
Citations in the corpus
- The power of synergy in differential privacy: Combining a small curator with local randomizers (2019, background): “Solving the problem while ensuring privacy is particularly challenging when the feature dimension is large compared to the number of available samples, which is the case for GWAS [13, 3, 39, 57].”