TY - GEN
T1 - Privacy-Preserving Social Media Data Outsourcing
AU - Zhang, Jinxue
AU - Sun, Jingchao
AU - Zhang, Rui
AU - Zhang, Yanchao
AU - Hu, Xia
N1 - Funding Information:
This work was supported by US Army Research Office (W911NF-15-1-0328), Defense Advanced Research Projects Agency (N66001-17-2-4031) and National Science Foundation (CNS-1619251, CNS-1514381, CNS-1421999, CNS- 1320906, CNS-1700032, CNS-1700039, CNS-1651954 (CAREER), CNS-1718078, IIS-1657196, IIS-1718840).
Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/8
Y1 - 2018/10/8
N2 - User-generated social media data are exploding and of high demand in public and private sectors. The disclosure of intact social media data exacerbates the threats to user privacy. In this paper, we first identify a text-based user-linkage attack on current data outsourcing practices, in which the real users in an anonymized dataset can be pinpointed based on the users' unprotected text data. Then we propose a framework for differentially privacy-preserving social media data outsourcing for the first time in literature. Within our framework, social media data service providers can outsource perturbed datasets to provide users differential privacy while offering high data utility to social media data consumers. Our differential privacy mechanism is based on a novel notion of E - text indistinguishability, which we propose to thwart the text-based user-linkage attack. Extensive experiments on real-world and synthetic datasets confirm that our framework can enable high-level differential privacy protection and also high data utility.
AB - User-generated social media data are exploding and of high demand in public and private sectors. The disclosure of intact social media data exacerbates the threats to user privacy. In this paper, we first identify a text-based user-linkage attack on current data outsourcing practices, in which the real users in an anonymized dataset can be pinpointed based on the users' unprotected text data. Then we propose a framework for differentially privacy-preserving social media data outsourcing for the first time in literature. Within our framework, social media data service providers can outsource perturbed datasets to provide users differential privacy while offering high data utility to social media data consumers. Our differential privacy mechanism is based on a novel notion of E - text indistinguishability, which we propose to thwart the text-based user-linkage attack. Extensive experiments on real-world and synthetic datasets confirm that our framework can enable high-level differential privacy protection and also high data utility.
UR - http://www.scopus.com/inward/record.url?scp=85056156737&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056156737&partnerID=8YFLogxK
U2 - 10.1109/INFOCOM.2018.8486242
DO - 10.1109/INFOCOM.2018.8486242
M3 - Conference contribution
AN - SCOPUS:85056156737
T3 - Proceedings - IEEE INFOCOM
SP - 1106
EP - 1114
BT - INFOCOM 2018 - IEEE Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Conference on Computer Communications, INFOCOM 2018
Y2 - 15 April 2018 through 19 April 2018
ER -