TY - GEN
T1 - Protecting user privacy
T2 - 12th ACM International Conference on Web Search and Data Mining, WSDM 2019
AU - Beigi, Ghazaleh
AU - Guo, Ruocheng
AU - Nou, Alexander
AU - Zhang, Yanchao
AU - Liu, Huan
N1 - Funding Information:
This material is based upon the work supported, in part, by NSF #1614576, ARO W911NF-15-1-0328 and ONR N00014-17-1-2605.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/1/30
Y1 - 2019/1/30
N2 - The overturning of the Internet Privacy Rules by the Federal Communications Commissions (FCC) in late March 2017 allows Internet Service Providers (ISPs) to collect, share and sell their customers' Web browsing data without their consent. With third-party trackers embedded on Web pages, this new rule has put user privacy under more risk. The need arises for users on their own to protect their Web browsing history from any potential adversaries. Although some available solutions such as Tor, VPN, and HTTPS can help users conceal their online activities, their use can also significantly hamper personalized online services, i.e., degraded utility. In this paper, we design an effective Web browsing history anonymization scheme, PBooster, aiming to protect users' privacy while retaining the utility of their Web browsing history. The proposed model pollutes users' Web browsing history by automatically inferring how many and what links should be added to the history while addressing the utility-privacy trade-off challenge. We conduct experiments to validate the quality of the manipulated Web browsing history and examine the robustness of the proposed approach for user privacy protection.
AB - The overturning of the Internet Privacy Rules by the Federal Communications Commissions (FCC) in late March 2017 allows Internet Service Providers (ISPs) to collect, share and sell their customers' Web browsing data without their consent. With third-party trackers embedded on Web pages, this new rule has put user privacy under more risk. The need arises for users on their own to protect their Web browsing history from any potential adversaries. Although some available solutions such as Tor, VPN, and HTTPS can help users conceal their online activities, their use can also significantly hamper personalized online services, i.e., degraded utility. In this paper, we design an effective Web browsing history anonymization scheme, PBooster, aiming to protect users' privacy while retaining the utility of their Web browsing history. The proposed model pollutes users' Web browsing history by automatically inferring how many and what links should be added to the history while addressing the utility-privacy trade-off challenge. We conduct experiments to validate the quality of the manipulated Web browsing history and examine the robustness of the proposed approach for user privacy protection.
KW - Privacy
KW - Trade-off
KW - Utility
KW - Web browsing history anonymization
UR - http://www.scopus.com/inward/record.url?scp=85061705239&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061705239&partnerID=8YFLogxK
U2 - 10.1145/3289600.3291026
DO - 10.1145/3289600.3291026
M3 - Conference contribution
AN - SCOPUS:85061705239
T3 - WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining
SP - 213
EP - 221
BT - WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining
PB - Association for Computing Machinery, Inc
Y2 - 11 February 2019 through 15 February 2019
ER -