Privacy Protection in Mobile Social Network in the Context of Big Data

DOI

10.2991/icmess-17.2017.64How to use a DOI?

Keywords

privacy protection; mobile social network; big data; models and algorithms

Abstract

With the rapid development of Web 2.0 technology, mobile social network data has shown the classical big data characteristics. The analysis of social network big data is getting deeper and deeper. At the same time, the risk of privacy disclosure in social network is also very obvious. A series of privacy preserving models and algorithms for social network data are proposed. In this paper, we summarized the privacy leakage type of social network, such as attacks on social network nodes and edges, deeply analyzed the existing privacy preserving technology from the following aspects: node K-anonymity, sub-graph K-anonymity, and data disturbance, pointed out its advantages and disadvantages, and prospected the future research directions from four aspects.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Pingshui Wang
AU  - Zecheng Wang
PY  - 2017/06
DA  - 2017/06
TI  - Privacy Protection in Mobile Social Network in the Context of Big Data
BT  - Proceedings of the 2017 International Conference on Management, Education and Social Science (ICMESS 2017)
PB  - Atlantis Press
SP  - 271
EP  - 274
SN  - 2352-5398
UR  - https://doi.org/10.2991/icmess-17.2017.64
DO  - 10.2991/icmess-17.2017.64
ID  - Wang2017/06
ER  -