Anonymizing Periodical Releases of SRS Data by Fusing Differential Privacy

TL;DR

This paper introduces a new framework combining differential privacy with existing anonymization models to enhance privacy protection in periodic SRS data releases, demonstrating improved privacy without losing data utility.

Contribution

It proposes the PPMS-DP(k, { heta}*, {psilon}) framework and two algorithms, PPMS-DPnum and PPMS-DPall, for better privacy in SRS data releases by integrating differential privacy.

Findings

PPMS-DP algorithms outperform previous models in privacy protection.

Experimental results show no significant loss in data utility.

Enhanced privacy against cross-release attacks.

Abstract

Spontaneous reporting systems (SRS) have been developed to collect adverse event records that contain personal demographics and sensitive information like drug indications and adverse reactions. The release of SRS data may disclose the privacy of the data provider. Unlike other microdata, very few anonymyization methods have been proposed to protect individual privacy while publishing SRS data. MS(k, {\theta}*)-bounding is the first privacy model for SRS data that considers multiple individual records, mutli-valued sensitive attributes, and rare events. PPMS(k, {\theta}*)-bounding then is proposed for solving cross-release attacks caused by the follow-up cases in the periodical SRS releasing scenario. A recent trend of microdata anonymization combines the traditional syntactic model and differential privacy, fusing the advantages of both models to yield a better privacy protection method. This paper proposes the PPMS-DP(k, {\theta}*, {\epsilon}) framework, an enhancement of PPMS(k, {\theta}*)-bounding that embraces differential privacy to improve privacy protection of periodically released SRS data. We propose two anonymization algorithms conforming to the PPMS-DP(k, {\theta}*, {\epsilon}) framework, PPMS-DPnum and PPMS-DPall. Experimental results on the FAERS datasets show that both PPMS-DPnum and PPMS-DPall provide significantly better privacy protection than PPMS-(k, {\theta}*)-bounding without sacrificing data distortion and data utility.