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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 theprocess. Several solutions employ Searchable Encryption (SE) to achieve securealerts directly on encrypted locations. While doing so preserves privacy, theperformance overhead incurred is high. We focus on a prominent SE technique inthe public-key setting -- Hidden Vector Encryption (HVE), and propose a graphembedding technique to encode location data in a way that significantly booststhe performance of processing on ciphertexts. We show that finding the optimalencoding is NP-hard, and provide several heuristics that are fast and obtainsignificant performance gains. Furthermore, we investigate the more challengingcase of dynamic alert zones, where the area of interest changes over time. Ourextensive experimental evaluation shows that our solutions can significantlyimprove computational overhead compared to existing baselines.

Supporting Secure Dynamic Alert Zones Using Searchable Encryption and Graph Embedding