Expected 10-anonymity of HyperLogLog sketches for federated queries of clinical data repositories

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 mo...

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Bibliographic Details
Published in:bioRxiv 2021-02
Main Authors: Ziye Tao, Weber, Griffin M, Yu, Yun William
Format: Article
Language:English
Subjects:
Diabetes mellitus
Electronic medical records
Online Access:Request full text
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Summary: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 trade-off 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 trade-off by using the HyperLogLog (HLL) probabilistic sketch. 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. Availability https://github.com/tzyRachel/K-anonymity-Expectation Contact ywyu{at}math.toronto.edu Supplementary information N/A Competing Interest Statement The authors have declared no competing interest. Footnotes * https://github.com/tzyRachel/K-anonymity-Expectation
DOI:10.1101/2021.01.30.428918