TY - GEN
T1 - Multiple mix-zones deployment for continuous location privacy protection
AU - Xu, Zhikai
AU - Zhang, Hongli
AU - Yu, Xiangzhan
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016
Y1 - 2016
N2 - Privacy protection is critical for location-based services (LBS). A frequently proposed solution to protect location privacy suggests that the LBS users need to exchange their pseudonyms over time in protected regions called mix-zones. However, how to deploy mix-zones remains an open question and has not been well addressed in previous works. Inspired by the notion that 'where you drive more is where you more likely need to change pseudonyms', we then treat the problem of optimal multiple mix-zones as a transportation problem. We then characterize the properties and constraints of the optimization problem, and build a mixed-integer programming model with the objective of minimizing the amount of time the users' privacy level is lower than the privacy requirement. The placement optimization problem is NP-hard. Therefore, we propose a heuristic algorithm to strategically select the mix-zone locations. Finally, we demonstrate the effectiveness of our solutions through extensive simulations on real-world data traces.
AB - Privacy protection is critical for location-based services (LBS). A frequently proposed solution to protect location privacy suggests that the LBS users need to exchange their pseudonyms over time in protected regions called mix-zones. However, how to deploy mix-zones remains an open question and has not been well addressed in previous works. Inspired by the notion that 'where you drive more is where you more likely need to change pseudonyms', we then treat the problem of optimal multiple mix-zones as a transportation problem. We then characterize the properties and constraints of the optimization problem, and build a mixed-integer programming model with the objective of minimizing the amount of time the users' privacy level is lower than the privacy requirement. The placement optimization problem is NP-hard. Therefore, we propose a heuristic algorithm to strategically select the mix-zone locations. Finally, we demonstrate the effectiveness of our solutions through extensive simulations on real-world data traces.
UR - https://www.scopus.com/pages/publications/85015233800
U2 - 10.1109/TrustCom.2016.0136
DO - 10.1109/TrustCom.2016.0136
M3 - 会议稿件
AN - SCOPUS:85015233800
T3 - Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016
SP - 760
EP - 766
BT - Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - Joint 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016
Y2 - 23 August 2016 through 26 August 2016
ER -