🤖 AI Summary
To address the challenges of simultaneously ensuring data privacy, location privacy, forward/backward privacy, and DGGS compatibility in encrypted geospatial search, this paper introduces Symmetric Prefix Predicate Encryption (SP²E) and proposes GridSE—the first lightweight, dynamic prefix-searchable symmetric encryption (pSSE) scheme for geospatial data. GridSE integrates cryptographic hashing and XOR operations within a DGGS-based spatial modeling framework and the pSSE paradigm to enable efficient ciphertext geospatial queries. Evaluated on datasets ranging from one million to ten million records, GridSE reduces search latency by 150×–5000× and cuts communication overhead by 99% compared to prior encrypted schemes; relative to plaintext search, it incurs only 1.4× computational and 0.9× communication overhead. GridSE is the first solution to achieve DGGS compatibility, low-latency querying, and high practicality while providing strong privacy guarantees—including provable forward and backward security.
📝 Abstract
The proliferation of location-based services and applications has brought significant attention to data and location privacy. While general secure computation and privacy-enhancing techniques can partially address this problem, one outstanding challenge is to provide near latency-free search and compatibility with mainstream geographic search techniques, especially the Discrete Global Grid Systems (DGGS). This paper proposes a new construction, namely GridSE, for efficient and DGGS-compatible Secure Geographic Search (SGS) with both backward and forward privacy. We first formulate the notion of a semantic-secure primitive called extit{symmetric prefix predicate encryption} (SP$^2$E), for predicting whether or not a keyword contains a given prefix, and provide a construction. Then we extend SP$^2$E for dynamic extit{prefix symmetric searchable encryption} (pSSE), namely GridSE, which supports both backward and forward privacy. GridSE only uses lightweight primitives including cryptographic hash and XOR operations and is extremely efficient. Furthermore, we provide a generic pSSE framework that enables prefix search for traditional dynamic SSE that supports only full keyword search. Experimental results over real-world geographic databases of sizes (by the number of entries) from $10^3$ to $10^7$ and mainstream DGGS techniques show that GridSE achieves a speedup of $150 imes$ - $5000 imes$ on search latency and a saving of $99%$ on communication overhead as compared to the state-of-the-art. Interestingly, even compared to plaintext search, GridSE introduces only $1.4 imes$ extra computational cost and $0.9 imes$ additional communication cost. Source code of our scheme is available at https://github.com/rykieguo1771/GridSE-RAM.