Abstract: 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.

Key words: differential privacy, reputation, data optimization, privacy protection