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The proliferation of smart phones and handheld mobile devices spurs a variety of personalized information services that utilize users' current location, known as Location-Based Services (LBS). Privacy protection is of great importance for such service users in mobile and wireless networks. However, as mobile devices are highly autonomous and heterogeneous, it is challenging to design generic protection techniques and achieve high level of privacy protection. Our study focuses on both potential privacy threats and privacy preserving mechanisms in mobile ad hoc networks. Preliminary work shows that the proposed system architecture and protection mechanisms exhibit satisfactory performance. The remaining work for completing the PhD dissertation is then presented. The proposed research carries significant intellectual merits and potential broader impacts in the following aspects. (1) We investigate the impact of inferential attacks on LBS users in mobile and wireless networks, and prove the vulnerability of using long-term pseudonyms for camouflaging users' real identities. (2) We propose a novel privacy metric to quantify system's resilience to such attack models. (3) An effective and extensible privacy architecture based on the mix zone model is designed. (4) We conduct rigorous analytical study, and design a privacy protection mechanism under real world constraints, e.g., traffic density and heterogeneity on different roads. (5) This proposal addresses the privacy preservation problem from a novel angle and lays a solid foundation for future research in protecting users' location privacy.