Location Privacy Preservation for Mobile Users in Location-Based Services

Because location-based cyber services are increasingly found in mobile applications (e.g., social networking and maps), user location privacy preservation is essential and remains one of the several ongoing research challenges. In this paper, we propose a region-of-interest division-based algorithm to Preserve the location Privacy of mobile device users in location-based Cyber Services (PPCS). Unlike existing methods, our proposed PPCS approach generates dummy locations while considering the semantic information of those locations. The PPCS algorithm enables the generated locations to exclude or reduce the exposure of a user’s real location. In our analysis, we demonstrate that PPCS is resilient to both colluding attacks and inference attacks. We also evaluate the efficiency and demonstrate the utility of our proposed approach through extensive simulations.

[1]  Kim-Kwang Raymond Choo,et al.  Are You Dating Danger? An Interdisciplinary Approach to Evaluating the (In)Security of Android Dating Apps , 2017, IEEE Transactions on Sustainable Computing.

[2]  Huan Liu,et al.  An efficient privacy preserving location based service system , 2014, 2014 IEEE Global Communications Conference.

[3]  Ruchika Gupta,et al.  An Exploration to Location Based Service and Its Privacy Preserving Techniques: A Survey , 2017, Wirel. Pers. Commun..

[4]  Ling Liu,et al.  Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms , 2008, IEEE Transactions on Mobile Computing.

[5]  Hua Lu,et al.  PAD: privacy-area aware, dummy-based location privacy in mobile services , 2008, MobiDE '08.

[6]  Kim-Kwang Raymond Choo,et al.  A novel file carving algorithm for National Marine Electronics Association (NMEA) logs in GPS forensics , 2017, Digit. Investig..

[7]  Xiaoyan Zhu,et al.  Using dynamic pseudo-IDs to protect privacy in location-based services , 2014, 2014 IEEE International Conference on Communications (ICC).

[8]  Xiaojiang Du,et al.  Prometheus: Privacy-aware data retrieval on hybrid cloud , 2013, 2013 Proceedings IEEE INFOCOM.

[9]  Marco Gruteser,et al.  USENIX Association , 1992 .

[10]  Muthu Ramachandran,et al.  Efficient location privacy algorithm for Internet of Things (IoT) services and applications , 2017, J. Netw. Comput. Appl..

[11]  Xiaoqing Li,et al.  Privacy-area aware dummy generation algorithms for Location-Based Services , 2014, 2014 IEEE International Conference on Communications (ICC).

[12]  Victor I. Chang,et al.  Towards privacy preservation for "check-in" services in location-based social networks , 2019, Inf. Sci..

[13]  Munam Ali Shah,et al.  A novel model for preserving Location Privacy in Internet of Things , 2016, 2016 22nd International Conference on Automation and Computing (ICAC).

[14]  Xiaodong Lin,et al.  FINE: A fine-grained privacy-preserving location-based service framework for mobile devices , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[15]  Dimitrios Makrakis,et al.  Protecting Location Privacy with Clustering Anonymization in vehicular networks , 2014, 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[16]  Gang Sun,et al.  L2P2: A location-label based approach for privacy preserving in LBS , 2017, Future Gener. Comput. Syst..

[17]  Qinghua Li,et al.  Achieving k-anonymity in privacy-aware location-based services , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[18]  George Danezis,et al.  Towards an Information Theoretic Metric for Anonymity , 2002, Privacy Enhancing Technologies.

[19]  Sabrina De Capitani di Vimercati,et al.  An Obfuscation-Based Approach for Protecting Location Privacy , 2011, IEEE Transactions on Dependable and Secure Computing.

[20]  Muthu Ramachandran,et al.  Towards Achieving Data Security with the Cloud Computing Adoption Framework , 2016, IEEE Transactions on Services Computing.

[21]  Jie Wu,et al.  Effective Defense Schemes for Phishing Attacks on Mobile Computing Platforms , 2016, IEEE Transactions on Vehicular Technology.

[22]  Ruchika Gupta,et al.  Achieving location privacy through CAST in location based services , 2017, Journal of Communications and Networks.

[23]  Chi-Yin Chow,et al.  A peer-to-peer spatial cloaking algorithm for anonymous location-based service , 2006, GIS '06.

[24]  Zhonghui Wang,et al.  Protecting trajectory privacy: A user-centric analysis , 2017, J. Netw. Comput. Appl..

[25]  Long Hu,et al.  ASA: Against statistical attacks for privacy-aware users in Location Based Service , 2017, Future Gener. Comput. Syst..

[26]  Fenghua Li,et al.  Time obfuscation-based privacy-preserving scheme for Location-Based Services , 2016, 2016 IEEE Wireless Communications and Networking Conference Workshops (WCNCW).

[27]  Mohsen Guizani,et al.  Security and privacy preservation in fog-based crowd sensing on the internet of vehicles , 2019, J. Netw. Comput. Appl..

[28]  Victor I. Chang,et al.  User-defined privacy location-sharing system in mobile online social networks , 2017, J. Netw. Comput. Appl..

[29]  Victor I. Chang,et al.  A cybernetics Social Cloud , 2017, J. Syst. Softw..

[30]  Yuguang Fang,et al.  A game-theoretic approach for achieving k-anonymity in Location Based Services , 2013, 2013 Proceedings IEEE INFOCOM.

[31]  Nasser Ghadiri,et al.  $P^4QS$: A Peer-to-Peer Privacy Preserving Query Service for Location-Based Mobile Applications , 2016, IEEE Transactions on Vehicular Technology.

[32]  Pan Li,et al.  n-CD: A geometric approach to preserving location privacy in location-based services , 2013, 2013 Proceedings IEEE INFOCOM.

[33]  Miao Pan,et al.  Traffic-aware multiple mix zone placement for protecting location privacy , 2012, 2012 Proceedings IEEE INFOCOM.