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This paper addresses the challenge of preserving user privacy in location-based services (LBSs) by proposing a novel, complementary approach to existing privacy-preserving techniques such as k-anonymity and l-diversity. Our approach implements collaborative caching strategies within a mobile ad hoc network (MANET), exploiting the geographic of location-based queries (LBQs) to reduce data exposure to untrusted LBS servers. Unlike existing approaches that rely on centralized servers or stationary infrastructure, our solution facilitates direct data exchange between users' devices, providing an additional layer of privacy protection. We introduce a new privacy entropy-based metric called accumulated privacy loss (APL) to quantify the privacy loss incurred when accessing either the LBS or our proposed system. Our approach implements a two-tier caching strategy: local caching maintained by each user and neighbor caching based on proximity. This strategy not only reduces the number of queries to the LBS server but also significantly enhances user privacy by minimizing the exposure of location data to centralized entities. Empirical results demonstrate that while our collaborative caching system incurs some communication costs, it significantly mitigates redundant data among user caches and reduces the need to access potentially privacy-compromising LBS servers. Our findings show a 40% reduction in LBS queries, a 64% decrease in data redundancy within cells, and a 31% reduction in accumulated privacy loss compared to baseline methods. In addition, we analyze the impact of data obsolescence on cache performance and privacy loss, proposing mechanisms for maintaining the relevance and accuracy of cached data. This work contributes to the field of privacy-preserving LBSs by providing a decentralized, user-centric approach that improves both cache redundancy and privacy protection, particularly in scenarios where central infrastructure is unreachable or untrusted. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of preserving user privacy in location-based services (LBSs) by proposing a novel, complementary approach to existing privacy-preserving techniques such as k-anonymity and l-diversity. Our approach implements collaborative caching strategies within a mobile ad hoc network (MANET), exploiting the geographic of location-based queries (LBQs) to reduce data exposure to untrusted LBS servers. Unlike existing approaches that rely on centralized servers or stationary infrastructure, our solution facilitates direct data exchange between users’ devices, providing an additional layer of privacy protection. We introduce a new privacy entropy-based metric called accumulated privacy loss (APL) to quantify the privacy loss incurred when accessing either the LBS or our proposed system. Our approach implements a two-tier caching strategy: local caching maintained by each user and neighbor caching based on proximity. This strategy not only reduces the number of queries to the LBS server but also significantly enhances user privacy by minimizing the exposure of location data to centralized entities. Empirical results demonstrate that while our collaborative caching system incurs some communication costs, it significantly mitigates redundant data among user caches and reduces the need to access potentially privacy-compromising LBS servers. Our findings show a 40% reduction in LBS queries, a 64% decrease in data redundancy within cells, and a 31% reduction in accumulated privacy loss compared to baseline methods. In addition, we analyze the impact of data obsolescence on cache performance and privacy loss, proposing mechanisms for maintaining the relevance and accuracy of cached data. This work contributes to the field of privacy-preserving LBSs by providing a decentralized, user-centric approach that improves both cache redundancy and privacy protection, particularly in scenarios where central infrastructure is unreachable or untrusted.