Multi-level local differential privacy algorithm recommendation framework - Hanyi WANG; Xiaoguang LI; Wenqing BI; Yahong CHEN; Fenghua LI; Ben NIU (Tongxin xuebao)
Local differential privacy (LDP) algorithm usually assigned the same protection mechanism and parameters to different users.However, it ignored the differences among the device resources and the privacy requirements of different users.For this reason, a multi-level LDP algorithm recommendation framework was proposed.The server and the users’ requirements were considered in the framework, and the multi-users’ differential privacy protections were realized by the server and the users’ multi-level management.The framework was applied to the frequency statistics scenario to form an LDP algorithm recommendation scheme.LDP algorithm was improved to ensure the availability of statistical results, and a collaborative mechanism was designed to protect users’ privacy preferences.The experimental results demonstrate the availability of the proposed scheme.