  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.
