Yunxia Feng
ABSTRACT
This paper studies the problem of location and query content preserving in location based service (LBS) systems. Based on the private information retrieval (PIR) theory and location k-anonymity model, we propose a new privacy preserving model, called (k, R, r)-anonymity, which is a light-weight and personalized resolution that can be implemented on mobile terminals. Main idea of the proposed model is to replace the user's real location and the query target by a specific area and a set of location types, respectively. A user may control his privacy preserving degree according to his specific demand by dynamically adjusting parameters of the proposed model, such as query contents, the size and location of the anonymous region, etc. Taking the nearest neighbor query as example, we evaluate performance of the proposed model and make a brief comparison with k-anonymity and PIR. Results show that the proposed model provides stronger anonymity protection than k-anonymity, whereas it its simpler and induces less additional overheads than PIR. In addition, it does not need a third-party privacy anonymity server either.
- 2017. Baidu maps SDK. http://lbsyun.baidu.com. (2017).Google Scholar
- Alastair R Beresford and Frank Stajano. 2003. Location Privacy in Pervasive Computing. Pervasive Computing IEEE 2, 1 (2003), 46--55. Google ScholarDigital Library
- Butty, N Levente, Tam Holczer, and Istv Vajda. 2007. On the effectiveness of changing pseudonyms to provide location privacy in VANETS. In Security and Privacy in Ad-Hoc and Sensor Networks. 129--141. Google ScholarDigital Library
- S. Fang, Y. Liu, W. Shen, and H. Zhu. 2017. Virtual Multipath Attack and Defense for Location Distinction in Wireless Networks. IEEE Transactions on Mobile Computing 16, 02 (2017), 566--580. Google ScholarDigital Library
- Yunxia Feng, Peng Liu, and Jianhui Zhang. 2012. A Mobile Terminal Based Trajectory Preserving Strategy for Continuous Querying LBS Users. In DCOSS. 92--98. Google ScholarDigital Library
- Bugra Gedik and Ling Liu. 2005. Location Privacy in Mobile Systems: A Personalized Anonymization Model. In IEEE ICDCS. 620--629. Google ScholarDigital Library
- B. Gedik and Ling Liu. 2008. Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms. In Ieee Transactions On Mobile Computing. 1 - 18. Google ScholarDigital Library
- Gabriel Ghinita, Panos Kalnis, Ali Khoshgozaran, Cyrus Shahabi, and Kian Lee Tan. 2008. Private queries in location based services: anonymizers are not necessary. In ACM SIGMOD. 121--132. Google ScholarDigital Library
- Gabriel Ghinita, Keliang Zhao, Dimitris Papadias, and Panos Kalnis. 2010. A reciprocal framework for spatial K -anonymity. Information Systems 35, 3 (2010), 299--314. Google ScholarDigital Library
- Marco Gruteser and Dirk Grunwald. 2003. Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking. In International Conference on Mobile Systems, Applications, and Services. 31--42. Google ScholarDigital Library
- Huaxin Li, Haojin Zhu, Suguo Du, Xiaohui Liang, and Xuemin Shen. 2016. Privacy Leakage of Location Sharing in Mobile Social Networks: Attacks and Defense. IEEE Transactions on Dependable and Secure Computing (2016).Google Scholar
- Yi Li, Lu Zhou, Haojin Zhu, and Limin Sun. 2016. Privacy-Preserving Location Proof for Securing Large-Scale Database-Driven Cognitive Radio Networks. IEEE Internet of Things Journal 3, 4 (2016), 563--571.Google ScholarCross Ref
- Kyriakos Mouratidis, Dimitris Papadias, and Marios Hadjieleft-heriou. 2005. Conceptual Partitioning: An Efficient Method For Continuous Nearest Neighbor Monitoring. In ACM SIGMOD. 634--645. Google ScholarDigital Library
- Stavros Papadopoulos, Spiridon Bakiras, and Dimitris Papadias. 2010. Nearest Neighbor Search with Strong Location Privacy. PVLDB 3, 1 (2010), 619--629. Google ScholarDigital Library
- Latanya Sweeney. 2002. k-Anonymity: A Model For Protecting Privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, 5 (2002), 1--14. Google ScholarDigital Library
- Peter Williams and Radu Sion. 2008. Usable PIR. In NDSS.Google Scholar
- Qiuyu Xiao, Jiayi Chen, Le Yu, Huaxin Li, Haojin Zhu, Muyuan Li, and Kui Ren. 2014. POSTER:LocMask: A Location Privacy Protection Framework in Android System. (2014), 1526--1528. Google ScholarDigital Library
- T Xu and Ying Cai. 2008. Exploring Historical Location Data for Anonymity Preservation in Location-Based Services. In IN-FOCOM. 547--555.Google Scholar
Index Terms
- (k, R, r)-anonymity: a light-weight and personalized location protection model for LBS query
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