Enabling Efficient Privacy-Preserving Spatiotemporal Location-Based Services for Smart Cities

GPS-enabled Internet of Things devices, which can obtain the location and temporal information of installed objects to promote location-based service, are completely changing our lives. In recent years, the confidentiality and privacy of personal data have attracted widespread attention, especially when outsourcing to third-party providers. To achieve both the confidentiality and availability of outsourced data, various dynamic searchable symmetric encryption (DSSE) schemes have been proposed. However, existing solutions are limited either in terms of security or efficiency. To address this challenge, we propose a secure and efficient search over encrypted spatiotemporal data (SES-ESTD) scheme that utilizes constrained pseudo-random function and enhanced asymmetric scalar-product preserving encryption. Our scheme not only achieves high retrieval efficiency but also ensures forward security and content privacy. We provide a formal security analysis to prove SES-ESTD is forward secure and content private. Furthermore, extensive experiments indicate that SES-ESTD incurs lower computation and storage overheads compared to other schemes. Most importantly, SES-ESTD achieves millisecond-level retrieval for millions of data points and provides a retrieval speed that is 2.85 times faster than existing forward secure spatiotemporal DSSE schemes.