Supporting Secure Dynamic Alert Zones Using Searchable Encryption and Graph Embedding

TL;DR

This paper enhances privacy-preserving location-based alerts by combining searchable encryption with graph embedding techniques, significantly reducing computational overhead for dynamic alert zones.

Contribution

It introduces a novel graph embedding method for HVE encryption to improve performance and addresses dynamic alert zones with effective heuristics.

Findings

Performance improvements over existing methods

Effective heuristics for optimal encoding

Significant reduction in computational overhead

Abstract

Location-based alerts have gained increasing popularity in recent years, whether in the context of healthcare (e.g., COVID-19 contact tracing), marketing (e.g., location-based advertising), or public safety. However, serious privacy concerns arise when location data are used in clear in the process. Several solutions employ Searchable Encryption (SE) to achieve secure alerts directly on encrypted locations. While doing so preserves privacy, the performance overhead incurred is high. We focus on a prominent SE technique in the public-key setting -- Hidden Vector Encryption (HVE), and propose a graph embedding technique to encode location data in a way that significantly boosts the performance of processing on ciphertexts. We show that finding the optimal encoding is NP-hard, and provide several heuristics that are fast and obtain significant performance gains. Furthermore, we investigate the more challenging case of dynamic alert zones, where the area of interest changes over time. Our extensive experimental evaluation shows that our solutions can significantly improve computational overhead compared to existing baselines.