Abstract: Recently,many practical applications need to continuously respond to range queries over streaming data in real-time,and adopt the differential privacy protection to deal with the disclosure of sensitive data in the information publication process.Existing research adopts Fenwick tree as data structure to organize and store data items in the stream to satisfy the real-time requirement in the information publication process.However,parameters in the previous method are predefined,which cannot adapt dynamic changes of the queries well.To solve this problem,based on the framework of real-time differential privacy streaming data publication,this paper proposed a method introducing the historical query records to achieve the adaptive parameter optimization.Firstly,based on moving average method,the historical queries are analyzed to predict the subsequent queries.Then according to the prediction results,the optimistic height of the tree is calculated theoretically,which minimizes the expected error.Finally,the adaptive parameter optimization is achieved in real-time differential privacy streaming data publication.The experimental results show that this method can significantly improve the query accuracy while guaranteeing the time efficiency.

Key words: Adaptive parameter optimization, Differential privacy, Historical queries, Streaming data publication