Differentially Private Distributed Frequency Estimation

In order to remain competitive, Internet companies collect and analyse user data for the purpose of improvement experiences. Frequency estimation is a widely used statistical tool, which could potentially conflict with relevant privacy regulations. Privacy preserving analytic methods based on differential have been proposed, require either large base or trusted server. Although requirements such solutions may not be problem larger companies, they unattainable smaller organizations. To address this issue, we propose distributed privacy-preserving sampling-based frequency method has high accuracy even in scenario small number users while requiring any This achieved by combining multi-party computation sampling techniques. We also provide relation between its guarantee, output accuracy, participants. Distinct from most existing methods, our achieve centralized guarantee without need established that, participants, mechanisms can produce estimates hence more opportunity growth through analysis. further an architectural model support weighted aggregation higher estimate cater varying requirements. Compared unweighted aggregation, provides accurate estimate. Extensive experiments are conducted show effectiveness proposed methods.