(p,α)-k anonymous algorithm based on dynamic data
Ke Zheng, Huatang Wu · 2022
At present, most privacy protection models only consider static data, and it is easy for attackers to deduce some privacy data through data tables. In view of the changing characteristics of data, this paper proposes a set of dynamic data processing strategies, which are processed according to whether the data appears for the first time and the frequency of occurrence, and the KL divergence is adopted in the process of calculating similarity. Based on the dynamic data processing strategy, this paper proposes a (p,α)-Sensitive k anonymity algorithm for dynamic data. Through the analysis of the experimental results, it can be seen that the algorithm can protect the privacy of dynamic data and increase a certain amount of data-availability.