# Collaborative Caching for Implementing a Location-Privacy Aware LBS on a MANET
## 1 Introduction
Our model considers the LBS as the main adversary seeking to compromise location privacy while acknowledging that users in the MANET could also potentially compromise privacy--albeit to a lesser extent due to their limited storage and computing capabilities compared to an LBS server. Unlike existing privacy metrics that focus solely on k-anonymity levels or query diversity, our APL metric provides a comprehensive measure of privacy loss across both centralized and distributed components of the system. It explicitly accounts for the differential privacy implications of data exposure to limited-capacity MANET peers versus a centralized LBS server with extensive data aggregation capabilities. This allows us to quantitatively assess the privacy benefits of our hybrid approach and guide the development of effective privacy preservation strategies. Our mathematical foundation for APL builds on information theory principles while incorporating practical considerations such as node mobility, cache obsolescence, and query patterns. Our approach has two main goals: (1) to develop a decentralized peer-to-peer LBS architecture for mobile users that complements traditional LBS servers and (2) to design a storage-efficient collaborative caching scheme to protect user privacy and conserve LBS resources. To provide a framework for our approach, we consider the following aspects:  
* The LBS is the main adversary, as it has all data, which compromises the information of users who access the services. * The queries submitted by users underlie geographical proximity (i.e., these queries are well-known location-based queries, LBQs), and they are not confidential; queries such as \"What is the nearest hotel?\" or \"What is the nearest major tourist center to my location?\" correspond to queries of this type. * In MANET, users are constantly moving into and out of service ranges. Some users within the service range act as caches, forming a collaborative caching scheme. * Our model operates at the application layer of the OSI model; details about the operation of the physical layer are not considered in this paper. Implementing distributed geographic caching and LBS functionality in MANET introduces several challenges. 1. Maintaining location-relevant data near a corresponding geographic area is difficult when mobile users are constantly moving. To address this, we propose a dynamic data exchange strategy that considers both geographic proximity and user mobility patterns. 2. Disconnections in ad hoc networks cause delays; therefore, users must have latency tolerance thresholds aligned with their privacy risk aversion profiles. 3. The efficient use of limited storage across nodes is critical, as excessive redundancy reduces the space for new data, eventually pushing users back to the LBS server when the collective storage capacity is exceeded. 4. More data exchange helps coordination and reduces redundancy, but it consumes energy and bandwidth. The solution must balance response time, energy efficiency, and location privacy. 5. Data obsolescence in caches can lead to undesired access to the LBS and unnecessary storage consumption. We study the effect of data becoming obsolete and propose strategies for the timely removal of outdated information from caches. 6. Balancing privacy protection with system usability requires a careful consideration of when to utilize MANET peers versus accessing the LBS server, particularly when dealing with time-sensitive queries or sparse network conditions. To our knowledge, no previous work has focused on implementing a privacy-aware location-based service directly in a MANET, particularly considering the challenges of data redundancy, privacy loss, and obsolescence in a distributed caching system. Moreover, the lack of a suitable metric to evaluate privacy loss in this context highlights the novelty of our approach.