SHELBRS: Location Based Recommendation Services using Switchable Homomorphic Encryption
Mishel Jain, Priyanka Singh, Balasubramanian Raman

TL;DR
SHELBRS introduces a lightweight, switchable homomorphic encryption-based system for location-based recommendation services, balancing privacy preservation with reduced computational overhead compared to fully homomorphic encryption methods.
Contribution
The paper presents SHELBRS, a novel LBRS framework utilizing switchable homomorphic encryption to enhance privacy while maintaining efficiency, addressing the limitations of existing FHE-based solutions.
Findings
SHELBRS achieves lower computational overhead than FHE-based approaches.
The scheme maintains comparable security levels to existing privacy-preserving methods.
Experimental results demonstrate improved performance in real-world scenarios.
Abstract
Location-Based Recommendation Services (LBRS) has seen an unprecedented rise in its usage in recent years. LBRS facilitates a user by recommending services based on his location and past preferences. However, leveraging such services comes at a cost of compromising one's sensitive information like their shopping preferences, lodging places, food habits, recently visited places, etc. to the third-party servers. Losing such information could be crucial and threatens one's privacy. Nowadays, the privacy-aware society seeks solutions that can provide such services, with minimized risks. Recently, a few privacy-preserving recommendation services have been proposed that exploit the fully homomorphic encryption (FHE) properties to address the issue. Though, it reduced privacy risks but suffered from heavy computational overheads that ruled out their commercial applications. Here, we propose…
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