Open Access
IEEE Access, volume 10, pages 4374-4379
IEEE Access Special Section: Privacy Preservation for Large-Scale User Data in Social Networks
Publication type: Journal Article
Publication date: 2022-01-17
General Materials Science
General Engineering
General Computer Science
Abstract
Social networks have become one of the most popular platforms for people to communicate and interact with their friends and share personal information and experiences (e.g., Facebook owns over 1.23 billion monthly active users). The increasing popularity of social networks has generated extremely large-scale user data (e.g., Twitter generates 500 million tweets per day and around 200 billion tweets per year). These data can help improve people’s quality of life as well as benefit various interest groups such as advertisers, application developers, and so on. However, privacy may be compromised if learning algorithms are used to infer unpublished privacy information from published data. Hence, user data privacy preservation has become one of the most urgent research issues in social networks.
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Gao Y. et al. IEEE Access Special Section: Privacy Preservation for Large-Scale User Data in Social Networks // IEEE Access. 2022. Vol. 10. pp. 4374-4379.
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Gao Y., Li Y., SUN Y., Cai Z., Ma L., Pustisek M., HU S. IEEE Access Special Section: Privacy Preservation for Large-Scale User Data in Social Networks // IEEE Access. 2022. Vol. 10. pp. 4374-4379.
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TY - JOUR
DO - 10.1109/access.2020.3036101
UR - https://doi.org/10.1109/access.2020.3036101
TI - IEEE Access Special Section: Privacy Preservation for Large-Scale User Data in Social Networks
T2 - IEEE Access
AU - Gao, Yuan
AU - Li, Yi
AU - SUN, YUNCHUAN
AU - Cai, Zhipeng
AU - Ma, Liran
AU - Pustisek, Matevz
AU - HU, SU
PY - 2022
DA - 2022/01/17
PB - Institute of Electrical and Electronics Engineers (IEEE)
SP - 4374-4379
VL - 10
SN - 2169-3536
ER -
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@article{2022_Gao,
author = {Yuan Gao and Yi Li and YUNCHUAN SUN and Zhipeng Cai and Liran Ma and Matevz Pustisek and SU HU},
title = {IEEE Access Special Section: Privacy Preservation for Large-Scale User Data in Social Networks},
journal = {IEEE Access},
year = {2022},
volume = {10},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
month = {jan},
url = {https://doi.org/10.1109/access.2020.3036101},
pages = {4374--4379},
doi = {10.1109/access.2020.3036101}
}