Expected 10-anonymity of HyperLogLog sketches for federated queries of clinical data repositories
AbstractMotivationThe 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.ResultsIn 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.Availabilityhttps://github.com/tzyRachel/[email protected] informationN/A
