Generalized and Multiple-Queries-Oriented Privacy Budget Strategies in Differential Privacy via Convergent Series

Abstract

For data analysis with differential privacy, an analysis task usually requires multiple queries to complete, and the total budget needs to be divided into different parts and allocated to each query. However, at present, the budget allocation in differential privacy lacks efficient and general allocation strategies, and most of the research tends to adopt an average or exclusive allocation method. In this paper, we propose two series strategies for budget allocation: the geometric series and the Taylor series. We show the different characteristics of the two series and provide a calculation method for selecting the key parameters. To better reflect a user’s preference of noise during the allocation, we explored the relationship between sensitivity and noise in detail, and, based on this, we propose an optimization for the series strategies. Finally, to prevent collusion attacks and improve security, we provide three ideas for protecting the budget sequence. Both the theoretical analysis and experimental results show that our methods can support more queries and achieve higher utility. This shows that our series allocation strategies have a high degree of flexibility which can meet the user’s need and allow them to be better applied to differentially private algorithms to achieve high performance while maintaining the security.