Differentially private sequential pattern mining considering time interval for electronic medical record systems
Hieu Le, Muneo Kushima, Kenji Araki, Haruo Yokota · 2019
Electronic medical record (EMR) systems have now been widely adopted to support medical workers. There also has been much interest in the machine-based generation of clinical pathways that can utilize sequential pattern mining (SPM) to extract them from historical EMR systems. However, the existing methods do not protect individual privacy, even though they involve sensitive medical data. To ensure the privacy of individual data, this paper describes two algorithms that deploy differential privacy by adding noise during calculations in the SPM considering time interval for guaranteeing privacy. The proposals can limit the amount of added noise by adding noise to the frequency calculations of only a part of candidate closed sequences. Experiments on real medical datasets show that our proposal can ensure the robust and high utility of mining process even with minimum privacy budget and amount of added noise.