一种改进的差分隐私参数设置及数据优化算法

基于差分隐私的数据扰动技术是当前隐私保护技术的研究热点，为了实现对敏感数据差分隐私保护的同时，尽量提高数据的可用性，对隐私参数的合理设置、对添加噪声后数据进行优化是差分隐私保护中的关键技术。提出了隐私参数设置算法RBPPA以及加噪数据的优化算法DPSRUKF。RBPPA将隐私参数设置构建于数据访问者和贡献者的信誉度之上，并与数据隐私度以及访问权限值关联，构造了细粒度的隐私参数设置方案； DPSRUKF采用了平方根无味卡尔曼滤波处理加噪数据，提高了差分隐私数据的可用性。实验分析表明，该算法实现了隐私参数的细粒化设置以及加噪数据优化后数据精度的提高，既为敏感数据的应用提供了数据安全保障，又为数据访问者提供了数据的高可用性。

An improved differential privacy parameter setting and data optimization algorithm

Data perturbation based on differential privacy is a hotspot of privacy protection technology. In order to realize differential privacy protection for sensitive data and improve the usability of data as much as possible, reasonable Settings of privacy parameters and optimization of noised data are the key technologies. The privacy parameter setting algorithm RBPPA and the optimization algorithm DPSRUKF are proposed in this paper. RBPPA constructs a fine-grained privacy parameter setting scheme based on the reputation of data visitors and contributors, and is associated with data privacy degree and access rights value. DPSRUKF uses Square-Root Unscented Kalman Filter to process noisy data, which improves the usability of differential private data. Experimental results show that this algorithm can realize fine-grained setting of privacy parameters and improve the accuracy of noisy data. It not only provides data security for sensitive data for applications, but also provides high usability of data for data visitors.