LPPS: A Distributed Cache Pushing Based K-Anonymity Location Privacy Preserving Scheme

Hindawi Publishing Corporation Mobile Information Systems Volume 2016, Article ID 7164126, 16 pages http://dx.doi.org/10.1155/2016/7164126 Research Article LPPS: A Distributed Cache Pushing Based K-Anonymity Location Privacy Preserving Scheme Ming Chen,1 Wenzhong Li,1 Xu Chen,2,3 Zhuo Li,4 Sanglu Lu,1 and Daoxu Chen1 1 State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210046, China Institute of Computer Science, University of Göttingen, Göttingen, Germany 3 School of Data and Computer Science, Sun Yat-Sen University, Guangzhou 510006, China 4 School of Computer Science & Tecnology, Beijing Information Science & Technology University, Beijing 100192, China 2 Correspondence should be addressed to Wenzhong Li; Received 3 December 2015; Accepted 2 June 2016 Academic Editor: Juan C. Cano Copyright © 2016 Ming Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Recent years have witnessed the rapid growth of location-based services (LBSs) for mobile social network applications. To enable location-based services, mobile users are required to report their location information to the LBS servers and receive answers of location-based queries. Location privacy leak happens when such servers are compromised, which has been a primary concern for information security. To address this issue, we propose the Location Privacy Preservation Scheme (LPPS) based on distributed cache pushing. Unlike existing solutions, LPPS deploys distributed cache proxies to cover users mostly visited locations and proactively push cache content to mobile users, which can reduce the risk of leaking users’ location information. The proposed LPPS includes three major process. First, we propose an algorithm to find the optimal deployment of proxies to cover popular locations. Second, we present cache strategies for location-based queries based on the Markov chain model and propose update and replacement strategies for cache content maintenance. Third, we introduce a privacy protection scheme which is proved to achieve 𝑘-anonymity guarantee for location-based services. Extensive experiments illustrate that the proposed LPPS achieves decent service coverage ratio and cache hit ratio with lower communication overhead compared to existing solutions. 1. Introduction Mobile social network applications are booming in recent years. With the rapid development of localization technologies (such as GPS) and Mobile Internet, mobile social network applications with location-based service (LBS) embedded are very popular such as Foursquare and Twitter. As a result, people use LBS more and more often. With the help of LBS, these mobile social network applications help users to connect to each other better. Typical applications include discovering popular restaurants in local area [1], traffic navigation [2], recommendation of friends nearby, and advertisement based on locations (such as “Promoted Tweets” [3]). According to the recent report, LBS is envisioned to become an over 10-billion-per-year business by the year 2016 [4]. As each coin has two sides, the LBS for mobile social applications may leak the users’ location trajectory and so on, with a rising concern in location-based service about privacy protection. In order to obtain LBS, a user needs to submit his query and location to the server and fetches the desired answer. The leak of user location information will increase the risk of adversary tracking the daily life of the user or will receive customized ADs which is unwilling or even revealing his private activities such as visiting a bank or going to a hospital [5]. It is important to protect user’s location privacy for LBS. Many efforts have been made to protect user’s location privacy. The 𝑘-anonymity model was proposed in [6], which declaimed that when 𝑘-anonymity was satisfied, each individual should be indistinguishable from 𝑘 − 1 other individuals. A user can achieve 𝑘-anonymity by sending out 𝑘 queries with different locations to the server and choosing the desired answer from the responses. However, such method wastes network bandwidth and causes extra overhead in both client and server sides. A few works introduced trust-worthy middleware or cache proxies by using random noise to conceal 2 Mobile Information Systems A Cache proxy LBS server B C Figure 1: An example of location privacy preservation. user’s real ID and locations [7–13]. However, once the middleware or the proxies are compromised, the location privacy is not guaranteed. In a word, most existing works adopt the pull-based strategy where the user submits the location-based query to the LBS server or the third-party server. The disadvantage of such pull-based strategy is the difficulty of avoiding the compromise of LBS servers or proxies in different levels. To deal with this problem, we propose location privacy preservation by using cache pushing. The basic idea is applying distributed cache proxies to store the most popular location-related data and pushing the data to the users proactively. If the desired data is available from the cache, the user does not need to send out the location-based query; thus his privacy is preserved. We use Figure 1 to illustrate the motivation of our work. Assume a user is working on a road and he wants to request the information regarding location 𝐴 (𝐴 could be a bank or a hospital, which is the sensitive information to be preserved). In the traditional pull-based strategy, the user will send the query to the LBS server. Once the LBS server receives the query, it knows that the user is heading for 𝐴. In our pushbased strategy, the nearby popular location-related data about locations 𝐴, 𝐵, and 𝐶 could have been stored in the cache proxy beforehand (e.g., such locations have been requested by other users previously and have been stored in the cache proxy), and they will be pushed to the user when he passes by the wireless access point. As a result, the user obtains the desire data without contacting the LBS server or reporting his location to the proxy. Since the LBS server is not aware of the query and the proxy only knows the user may go to 𝐴, 𝐵, or 𝐶, both of them cannot decide the user’s real destination. We propose the Location Privacy Preservation Scheme (LPPS) to achieve location anonymity. The LPPS need to answer three key questions: where to deploy cache proxies, how to organize and maintain cache content in proxies, and how to achieve 𝑘-anonymity assurance. We address these issues; we first introduce a greedy deployment algorithm to calculate the most frequently visit locations of mobile users (known as stay points) and deploys proxies to cover such regions as possible. Then we introduce a cache pushing strategy using group index to record the popular cache items and pushing the reques (...truncated)