论文标题
通过融合差异隐私来匿名对SRS数据的定期发布
Anonymizing Periodical Releases of SRS Data by Fusing Differential Privacy
论文作者
论文摘要
已经开发了自发报告系统（SRS）来收集包含个人人口统计学和敏感信息（例如药物指示和不良反应）的不良事件记录。 SRS数据的发布可能会披露数据提供商的隐私。与其他微数据不同，在发布SRS数据时，很少提出匿名化方法来保护个人隐私。 MS（k，θ*） - 边界是SRS数据的第一个隐私模型，该模型考虑了多个单独的记录，MUTLI-VARIED敏感属性和罕见事件。 PPM（k，θ*） - 然后提出了边界，以解决由周期SRS释放场景中的后续情况引起的交叉释放攻击。 Microdata匿名化的最新趋势结合了传统的句法模型和差异隐私，融合了这两种模型的优势以产生更好的隐私保护方法。本文提出了PPMS-DP（K，θ*，ε）框架，PPMS（k，θ*）的增强 - 包含差异隐私的边界，以改善对定期发布的SRS数据的隐私保护。我们提出了符合PPMS-DP（K，θ*，ε）框架，PPMS-DPNUM和PPMS-DPALL的两种匿名算法。 FAERS数据集的实验结果表明，PPMS-DPNUM和PPMS-DPALL都比PPMS-（K，θ*）提供了明显更好的隐私保护，而无需牺牲数据失真和数据实用程序。
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, θ*)-bounding is the first privacy model for SRS data that considers multiple individual records, mutli-valued sensitive attributes, and rare events. PPMS(k, θ*)-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, θ*, ε) framework, an enhancement of PPMS(k, θ*)-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, θ*, ε) 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, θ*)-bounding without sacrificing data distortion and data utility.
