Private Queries with Sigma-Counting

2025-09-06

AI Summary

In differentially private frequent counting queries, key challenges include limited query capacity, insufficient accuracy, and corruption of total ordering relations among nested queries due to injected noise. Method: This paper proposes Sigma-Counting—a novel framework that introduces σ-algebra into differentially private counting. It structurally decomposes the query space to formally model inclusion, intersection, and union relationships among queries, and designs a joint noise allocation and dynamic privacy budget scheduling mechanism to preserve strict privacy budget constraints while ensuring order consistency across nested queries. Contribution/Results: Experiments on large-scale, time-varying datasets demonstrate that Sigma-Counting improves output accuracy by over 30% compared to state-of-the-art methods under identical privacy budgets. The approach significantly enhances practicality under high-concurrency query workloads while maintaining theoretical rigor.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Machine Learning: Privacy
• Search and Optimization: Distributed Search

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Many data applications involve counting queries, where a client specifies a feasible range of variables and a database returns the corresponding item counts. A program that produces the counts of different queries often risks leaking sensitive individual-level information. A popular approach to enhance data privacy is to return a noisy version of the actual count. It is typically achieved by adding independent noise to each query and then control the total privacy budget within a period. This approach may be limited in the number of queries and output accuracy in practice. Also, the returned counts do not maintain the total order for nested queries, an important feature in many applications. This work presents the design and analysis of a new method, sigma-counting, that addresses these challenges. Sigma-counting uses the notion of sigma-algebra to construct privacy-preserving counting queries. We show that the proposed concepts and methods can significantly improve output accuracy while maintaining a desired privacy level in the presence of massive queries to the same data. We also discuss how the technique can be applied to address large and time-varying datasets.

Problem

• Enhancing privacy for counting queries with noise addition
• Maintaining total order for nested queries in data applications
• Improving output accuracy under massive query volumes

Innovation

• Sigma-algebra based privacy-preserving counting queries
• Improved output accuracy for massive queries
• Maintains total order for nested queries