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Expected 10-anonymity of HyperLogLog sketches for federated queries of clinical data repositories.
- Source :
-
Bioinformatics (Oxford, England) [Bioinformatics] 2021 Jul 12; Vol. 37 (Suppl_1), pp. i151-i160. - Publication Year :
- 2021
-
Abstract
- Motivation: The rapid growth in of electronic medical records provide immense potential to researchers, but are often silo-ed at separate hospitals. As a result, federated networks have arisen, which allow simultaneously querying medical databases at a group of connected institutions. The most basic such query is the aggregate count-e.g. How many patients have diabetes? However, depending on the protocol used to estimate that total, there is always a tradeoff in the accuracy of the estimate against the risk of leaking confidential data. Prior work has shown that it is possible to empirically control that tradeoff by using the HyperLogLog (HLL) probabilistic sketch.<br />Results: In this article, we prove complementary theoretical bounds on the k-anonymity privacy risk of using HLL sketches, as well as exhibit code to efficiently compute those bounds.<br />Availability and Implementation: https://github.com/tzyRachel/K-anonymity-Expectation.<br /> (© The Author(s) 2021. Published by Oxford University Press.)
- Subjects :
- Databases, Factual
Humans
Privacy
Research Personnel
Subjects
Details
- Language :
- English
- ISSN :
- 1367-4811
- Volume :
- 37
- Issue :
- Suppl_1
- Database :
- MEDLINE
- Journal :
- Bioinformatics (Oxford, England)
- Publication Type :
- Academic Journal
- Accession number :
- 34252969
- Full Text :
- https://doi.org/10.1093/bioinformatics/btab292