# Collaborative Caching for Implementing a Location-Privacy Aware LBS on a MANET
## 1 Introduction  
Location-based services (LBSs) have become integral to modern mobile computing thanks to the ubiquity of smartphones and social networks. These services provide essential functionality from navigation assistance to location-aware recommendations. However, increasing reliance on LBSs raises significant privacy concerns, as each query potentially exposes sensitive user information. Recent studies have shown that even seemingly innocent requests for public data can lead to privacy breaches when aggregated over time [1, 2]. For example, analyzing patterns of LBS queries can reveal sensitive details about a user's lifestyle, health conditions, or daily routines, which is information that could be exploited by malicious entities or commercial interests like insurance companies. The evolution of privacy protection in LBSs has seen several major developments. Early research established fundamental techniques such as _k-anonymity_, which builds cloaking areas to hide the exact location of a user among _k-1_ other possible locations [3]. This foundation was extended through _l-diversity_ approaches that protect query privacy bygenerating multiple plausible queries [4; 5]. More recent work has explored the combination of these techniques with advanced caching mechanisms [6] and continuous privacy protection schemes [7]. Although these approaches provide crucial privacy protections, they face a fundamental limitation. All are based on centralized LBS systems that accumulate significant amounts of user data over time. Even with privacy protections in place, this centralization enables comprehensive user profiling through long-term data analysis [8]. Researchers have attempted to address this centralization vulnerability through various distributed approaches using mobile ad hoc networks (MANETs). Some solutions leverage MANETs with cellular network infrastructure [9], while others focus on distributed query processing without considering caching efficiency [10]. Recent work has explored privacy protection through distributed storage [2] but still relies on stationary infrastructure in fixed locations. This gap between existing privacy-preserving techniques and the need for truly distributed LBS solutions motivates our work. Previous distributed approaches either depend too heavily on fixed infrastructure or fail to adequately address the unique challenges of maintaining privacy in a fully mobile environment. To address these limitations, we propose a novel approach that fundamentally re-imagines how location-based services can operate in a privacy-preserving manner. Our solution draws inspiration from a time-honored practice: seeking information from nearby individuals who are likely to have relevant local knowledge. Consider how travelers traditionally consulted locals about nearby amenities: Although the local person learns something about the inquire, their limited capacity and different purpose make it unlikely that they would capture a comprehensive user profile, which requires storing user history over time. This observation guides our development of a distributed LBS system that prioritizes user privacy while maintaining service utility. We implement this concept through a fully distributed LBS functionality operating directly between users in a MANET, minimizing reliance on centralized and potentially untrusted LBS servers. This approach complements existing privacy-preserving techniques by adding another layer of protection through peer-to-peer data sharing. By distributing queries among MANET peers before resorting to a centralized LBS, we can significantly reduce data exposure while maintaining service quality. To quantify the privacy benefits of our approach, we have developed a novel mathematical model that measures the loss of location privacy when users resolve queries in the LBS versus the mobile ad hoc network. This _accumulated privacy loss_ (APL) metric is, to our knowledge, the first proposed to evaluate privacy loss in the context of a distributed MANET-based LBS system.