Efficient and Secure Range Counting over Distributed Geographic Data with Query Range Protection

2026-07-05

AI Summary

This work addresses the challenge of performing range counting over multi-party distributed geospatial data while simultaneously preserving query privacy, ensuring computational efficiency, and maintaining accuracy in the presence of overlapping data—goals that existing methods struggle to reconcile. To this end, the paper proposes the PPRC protocol, which for the first time achieves a unified optimization of these three objectives. PPRC leverages two key techniques: Private Range Predicates (PRP) and Oblivious Linear Counting (OLC), replacing secure comparison with encrypted membership testing and employing lightweight cryptographic operations to enable efficient and secure range evaluation and aggregation. Experimental results on both real-world and synthetic datasets demonstrate that PPRC reduces estimation error by up to 55× and improves runtime performance by as much as 37× compared to baseline approaches.

Abstract

Range counting is a core primitive in geographic information systems. When data is distributed across multiple organizations, conducting range counting raises substantial privacy concerns. Existing privacy-preserving protocols focus on protecting organizations' datasets, but cannot simultaneously achieve efficiency, query privacy, and accuracy on overlapping data. Typical protocols process query range in plaintext for efficient point-in-range evaluation, since query-private designs rely on expensive secure comparisons. Moreover, most works assume non-overlapping datasets across organizations, which leads to huge errors in overlapping scenarios. In this paper, we propose PPRC, the first protocol that jointly satisfies all the privacy, efficiency, and accuracy requirements. PPRC makes two key technical contributions. First, we design the Private Range Predicate (PRP) technique that supports efficient point-in-range evaluation while protecting the query range. PRP reformulates range evaluation as encrypted membership tests, effectively replacing costly secure comparisons with faster secure multiplications. Second, we propose Oblivious Linear Counting (OLC), an aggregation scheme that efficiently and securely aggregates partial results from organizations with overlapping data. OLC involves only lightweight cryptographic operations and ensures that no information is leaked beyond the final range count. We theoretically analyze the accuracy, efficiency, and security of PPRC. Experiments on real-world and synthetic datasets show that PPRC achieves up to 55x smaller errors and 37x speedup compared to baseline protocols.

Problem

• range counting
• query privacy
• distributed geographic data
• data overlap
• privacy-preserving

Innovation

• Private Range Predicate
• Oblivious Linear Counting
• Secure Multiparty Computation
• Query Privacy
• Range Counting