Scalable Privacy-preserving Geo-distance Evaluation for Precision Agriculture IoT Systems

Precision agriculture has become a promising paradigm to transform modern agriculture. The recent revolution in big data and Internet-of-Things (IoT) provides unprecedented benefits including optimizing yield, minimizing environmental impact, reducing cost. However, the mass collection of farm IoT applications raises serious concerns about potential privacy leakage that may harm farmers’ welfare. In this work, we propose novel scalable private geo-distance evaluation system, called SPRIDE, allow application servers provide geographic-based services by computing distances among sensors farms privately. determine without learning any additional information their locations. key idea SPRIDE is perform efficient distance measurement comparison on encrypted locations over sphere leveraging homomorphic cryptosystem. To serve large user base, further SPRIDE+ with practical performance enhancements based pre-computation cryptographic elements. Through extensive experiments using real-world datasets, show achieves network farms, attaining 3+ times runtime improvement existing techniques. We can run resource-constrained mobile devices, which offers solution for privacy-preserving precision applications.