International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Impact Factor (2012): 3.358 Volume 3 Issue 9, September 2014 www.ijsr.net Licensed Under Creative Commons Attribution CC BY Achieving Location Privacy through the Impact of Changing Pseudonyms S. Sharath Chandra 1 , Dasu Vaman Ravi Prasad 2 1 M.Tech Student, Dept of CSE, Anurag Group of Institutions (Formerly CVSR College of Engineering), Hyderabad, T.S, India 2 Associate Professor, Dept of CSE, Anurag Group of Institutions, (Formerly CVSR College of Engineering), Hyderabad, T.S, India Abstract: In mobile networks, authentication is a required primitive for most security protocols. Unfortunately, a competitor can monitor pseudonyms used for authentication to track the location of mobile nodes. A regularly proposed solution to protect location privacy suggests that mobile nodes collectively change their pseudonyms in regions called mix zones. This approach is very expensive. Self-interested mobile nodes might thus decide not to cooperate and jeopardize the achievable location privacy. In this paper, we examine non-cooperative behaviour of mobile nodes, where each node aims at increasing its location privacy at a least cost. As in practice mobile nodes do not know their rivals’ payoffs, we then consider static incomplete information. By means of numerical outcomes, we then predict the behaviour of selfish mobile nodes. Keywords: Security and privacy protection, pseudonym, mobile computing, network protocols, mix zones. 1. Introduction The growing popularity of Bluetooth and WiFi in ad hoc mode [3], and other similar techniques is likely to fuel the adoption of peer-to-peer wireless communications. Corporations are developing wireless peer-to-peer technologies such as Nokia Instant Community [4] and Qualcomm FlashLinQ [8]. In addition to classic infrastructure based communications, mobile devices communicate directly with each other in an ad hoc wireless fashion. Such communications dramat1ically increase mobile devices’ awareness of their environment, enabling a new breed of context-aware applications. The integration of peer-to-peer wireless communications into mobile devices brings new security challenges, due to their mobile and ad hoc nature. Wireless communications are inherently dependent on geographic proximity: mobile devices detect each other’s presence by periodically broadcasting beacon messages. These messages include pseudonyms such as public keys in order to identify communicating parties, route communications and secure communications. Much to the detriment of privacy, outer parties can monitor pseudonyms in broadcasted messages in order to track the locations of mobile devices. A change of pseudonym by an isolated device in a wireless network can be trivially identified by an external party noticing transmitted messages. Hence, a change of pseudonym should be spatially coordinated among mobile devices, i.e., a collective effort. One solution consists in changing pseudonyms periodically, at a predetermined frequency. This works if at least two mobile nodes change their pseudonyms in proximity, a rarely met condition. Base stations can be used as coordinators to synchronize pseudonym changes, but this solution needs help from the infrastructure. This approach enables mobile nodes to change their pseudonyms at specific time instances. However, this solution achieves location privacy only with respect to the infrastructure. Another approach [1] coordinates pseudonym changes by forcing mobile nodes to change their pseudonyms within predetermined regions called mix zones. This approach lacks flexibility and is liable to attacks because a central authority fixes mix zone locations and must share them with mobile nodes. 2. Preliminaries System Model A network where mobile nodes are autonomous entities equipped with Wi-Fi or Bluetooth enabled devices that communicate with each other upon coming in radio range. In other words, consider a mobile wireless system such as a vehicular network [10] or a network of directly communicating hand-held devices. Without loss of generality, assume that each user in the system has a single mobile device and thus corresponds to a single node in the network. Now assume that mobile nodes automatically exchange information (unbeknownst to their users) as soon as they are in communication range of each other. Note that the evaluation is independent of the communication protocol. Without loss of generality, assume that mobile nodes advertise their presence by periodically broadcasting proximity beacons containing the node’s identifying/ authenticating information (i.e., the sender attaches its pseudonym to its messages). Due to the broadcast nature of wireless communications, beacons enable mobile nodes to discover their neighbors. For example, when a node s receives an authenticated beacon, it controls the permissibility of the sender by checking the certificate of the public key of the sender. After that, s verifies the signature of the beacon message. Threat Model An adversary À aims at tracking the location of some mobile nodes. In practice, the rivals can be a rogue individual, a set of malicious mobile nodes even deploy its own infrastructure (e.g., by placing eavesdropping devices in the considered area). Let’s consider that the adversary is passive and simply eavesdrops on communications. In the worst case, À obtains complete coverage and tracks mobile nodes throughout the entire area. And characterize the latter type of adversary as global. Paper ID: SEP14157 391