SmartSAT | Zechun Cao

Project Overview

SmartSAT utilizes a multipronged approach to conduct quantitative and qualitative research using secure intelligent technology, build intra- and inter-disciplinary research capacity at A&M-SA, build research partnerships between A&M-SA and SA VIA Metropolitan Transit (VIA Transit) to facilitate data driven transit planning decisions, and increase the competitiveness of A&M-SA faculty-student research for future CISE grants. The SmartSAT app will be customizable and will include real-time bus arrival information, notices when seating is limited, instant alert messages for schedule changes and other important information, and interactive features to directly collect data from riders about their commute experience. All services will be available in both English and Spanish. Researchers and students from diverse disciplines (sociology, computer science, cyber security, and information science) will work collaboratively to achieve project success.

Privacy-Preserving Spatial Queries in SmartSAT

Bus route planning and real-time, traffic-aware arrival time estimation are essential services offered by SmartSAT. However, like most location-based services, these features typically require the client to disclose their exact location and destination to the cloud service provider in order to compute the fastest bus route and the estimated arrival time at the selected stop.

We propose a novel exact nearest neighbor spatial search algorithm, Dynamic Hierarchical Voronoi Overlay (DHVO).

In this work, we design privacy-preserving spatial computing algorithms and a secure network communication protocol that enable accurate route and time estimations while protecting the rider’s sensitive location information (Cao et al., 2025).

Acknowledgements

This research was supported in part by NSF under grants CNS-2131193 and CNS-2219588.

References

2025

JISA
PrivNN: A private and efficient framework for spatial nearest neighbor query processing

Zechun Cao, Brian Kishiyama, and Jeong Yang

Journal of Information Security and Applications, 2025

Abs DOI Bib PDF

A common query type in location-based services (LBS) is finding the nearest neighbor (NN) of a given query object. However, the exact location of the query object is often sensitive information, posing significant privacy risks if the LBS server is untrusted or compromised. In this paper, we propose PrivNN, a novel spatial NN query processing framework that allows users to perform exact NN queries without revealing their location. Our framework introduces a novel spatial NN search algorithm, Dynamic Hierarchical Voronoi Overlay (DHVO), which efficiently finds the nearest neighbor by iteratively refining the search region using multi-granular Voronoi diagrams. We also present a client–server communication protocol that enables the server to respond to encrypted spatial NN queries by employing homomorphic encryption. We rigorously prove the correctness of our algorithm, analyze the theoretical properties of our framework, and demonstrate its strong security and robust privacy bounds. We implement and evaluate PrivNN on real-world spatial datasets, showing that it substantially reduces computational and communication overhead while remaining practical for private NN search in LBS applications.
@article{cao2025jisa, title = {PrivNN: A private and efficient framework for spatial nearest neighbor query processing}, author = {Cao, Zechun and Kishiyama, Brian and Yang, Jeong}, journal = {Journal of Information Security and Applications}, volume = {94}, pages = {104244}, year = {2025}, issn = {2214-2126}, doi = {https://doi.org/10.1016/j.jisa.2025.104244}, url = {https://www.sciencedirect.com/science/article/pii/S2214212625002819}, keywords = {Privacy, Security, Privacy-preserving computation, Location-based services, Nearest neighbor search, Spatial query processing}, publisher = {Elsevier}, }