EPSpatial: Achieving Efficient and Private Statistical Analytics of Geospatial Data

2025-05-19

AI Summary

To address location privacy preservation in real-time spatial statistical analysis over dynamic mobile scenarios, this paper proposes a high-accuracy, low-overhead, and strongly privacy-preserving solution. Methodologically, it introduces the first integration of Spatial Distributed Point Functions (SDPF) with Gray-coded quadtree spatial partitioning, enabling succinct ciphertext sharing over exponential-scale spatial indexes; it further designs a region-encoded incremental location update mechanism to substantially reduce communication and computational overhead. Theoretically, the scheme is proven to satisfy rigorous location privacy security under standard cryptographic assumptions. Experimental evaluation demonstrates that, compared to state-of-the-art approaches, the proposed method reduces both computation and communication costs by over 50%, while achieving statistical accuracy comparable to non-private baseline methods.

Technology Category

• Machine Learning: Privacy
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Geospatial data statistics involve the aggregation and analysis of location data to derive the distribution of clients within geospatial. The need for privacy protection in geospatial data analysis has become paramount due to concerns over the misuse or unauthorized access of client location information. However, existing private geospatial data statistics mainly rely on privacy computing techniques such as cryptographic tools and differential privacy, which leads to significant overhead and inaccurate results. In practical applications, geospatial data is frequently generated by mobile devices such as smartphones and IoT sensors. The continuous mobility of clients and the need for real-time updates introduce additional complexity. To address these issues, we first design extit{spatially distributed point functions (SDPF)}, which combines a quad-tree structure with distributed point functions, allowing clients to succinctly secret-share values on the nodes of an exponentially large quad-tree. Then, we use Gray code to partition the region and combine SDPF with it to propose $mathtt{EPSpatial}$, a scheme for accurate, efficient, and private statistical analytics of geospatial data. Moreover, considering clients' frequent movement requires continuous location updates, we leverage the region encoding property to present an efficient update algorithm.Security analysis shows that $mathtt{EPSpatial}$ effectively protects client location privacy. Theoretical analysis and experimental results on real datasets demonstrate that $mathtt{EPSpatial}$ reduces computational and communication overhead by at least $50%$ compared to existing statistical schemes.

Problem

• Ensuring privacy in geospatial data analytics
• Reducing computational overhead in location statistics
• Enabling real-time updates for mobile client data

Innovation

• Combines quad-tree with distributed point functions
• Uses Gray code for efficient region partitioning
• Leverages region encoding for location updates