Anonymizing Periodical Releases of SRS Data by Fusing Differential Privacy

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.Comment: 10 pages, 11 figure

Microaggregation Sorting Framework for K-Anonymity Statistical Disclosure Control in Cloud Computing

In cloud computing, there have led to an increase in the capability to store and record personal data ( microdata ) in the cloud. In most cases, data providers have no/little control that has led to concern that the personal data may be beached. Microaggregation techniques seek to protect microdata in such a way that data can be published and mined without providing any private information that can be linked to specific individuals. An optimal microaggregation method must minimize the information loss resulting from this replacement process. The challenge is how to minimize the information loss during the microaggregation process. This paper presents a sorting framework for Statistical Disclosure Control (SDC) to protect microdata in cloud computing. It consists of two stages. In the first stage, an algorithm sorts all records in a data set in a particular way to ensure that during microaggregation very dissimilar observations are never entered into the same cluster. In the second stage a microaggregation method is used to create k -anonymous clusters while minimizing the information loss. The performance of the proposed techniques is compared against the most recent microaggregation methods. Experimental results using benchmark datasets show that the proposed algorithms perform significantly better than existing associate techniques in the literature