TY - GEN
T1 - You can hide, but your periodic schedule can't
AU - Ma, Minghua
AU - Zhao, Kai
AU - Sui, Kaixin
AU - Xu, Lei
AU - Li, Yong
AU - Pei, Dan
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/5
Y1 - 2017/7/5
N2 - The enterprise Wi-Fi networks enable the collection of large-scale users' trajectory datasets, which are highly desired for both research and commercial purposes. Meanwhile, releasing these mobility data also raises serious privacy concerns. A large body of work tries to achieve k-anonymity as the first step to solve the privacy problem and it has been qualitatively recognized that k-anonymity is still risky when the diversity of sensitive information in the k-anonymity set is low. However, there lacks a study that provides a quantitative understanding for trajectory data. In this work, we investigate the schedule-leakage risk for the first time, by presenting a large-scale measurement based analysis of the high schedule-leakage risk over sixteen weeks of trajectory data collected from Tsinghua University, a campus with 2,670 access points deployed in 111 buildings. Using this dataset, we recognize the high risk of the schedule-leakage, i.e., even when 4-anonymity is satisfied, 28% of individuals' schedules are totally disclosed, and 56% are partly disclosed.
AB - The enterprise Wi-Fi networks enable the collection of large-scale users' trajectory datasets, which are highly desired for both research and commercial purposes. Meanwhile, releasing these mobility data also raises serious privacy concerns. A large body of work tries to achieve k-anonymity as the first step to solve the privacy problem and it has been qualitatively recognized that k-anonymity is still risky when the diversity of sensitive information in the k-anonymity set is low. However, there lacks a study that provides a quantitative understanding for trajectory data. In this work, we investigate the schedule-leakage risk for the first time, by presenting a large-scale measurement based analysis of the high schedule-leakage risk over sixteen weeks of trajectory data collected from Tsinghua University, a campus with 2,670 access points deployed in 111 buildings. Using this dataset, we recognize the high risk of the schedule-leakage, i.e., even when 4-anonymity is satisfied, 28% of individuals' schedules are totally disclosed, and 56% are partly disclosed.
UR - http://www.scopus.com/inward/record.url?scp=85027889455&partnerID=8YFLogxK
U2 - 10.1109/IWQoS.2017.7969154
DO - 10.1109/IWQoS.2017.7969154
M3 - Conference contribution
AN - SCOPUS:85027889455
T3 - 2017 IEEE/ACM 25th International Symposium on Quality of Service, IWQoS 2017
BT - 2017 IEEE/ACM 25th International Symposium on Quality of Service, IWQoS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th IEEE/ACM International Symposium on Quality of Service, IWQoS 2017
Y2 - 14 June 2017 through 16 June 2017
ER -