JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2019, Vol. 54 ›› Issue (3): 10-17, 27.doi: 10.6040/j.issn.1671-9352.2.2018.084

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Sensitive attribute iterative inference method for social network users

Xiao-jie XIE1,2(),Ying LIANG1,*(),Xiang-xiang DONG1,2   

  1. 1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
    2. School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2018-09-20 Online:2019-03-20 Published:2019-03-19
  • Contact: Ying LIANG E-mail:mailbox_of_xxj@126.com;liangy@ict.ac.cn
  • Supported by:
    国家重点研发计划(2018YFB1004704);国家重点研发计划(2016YFB0800403)

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Abstract:

Analyzing and inferring sensitive information of social network users is conducive to technically quantifying the degree of privacy leakage and protecting privacy. Aiming at the problem that existing user attribute inference methods needs to make strong assumptions on the value of user attributes, an iterative method for user sensitive attributes in social network is proposed by combining the RL iterative classification framework and extending the wvRN relation inference method. Extracting probabilities of user sensitive attributes based on user text and convolution neural network and iteratively updating inference results with neighboring nodes, not only weakens the assumption of user attributes, but also improves the degree of application. The experimental results show that by obtaining a small amount of labeled data in social networks and setting reasonable parameter values for iterative inference methods, better user sensitive attribute inference results can be obtained.

Key words: social network, text classification, social link, attribute inference, data mining

CLC Number: 

  • TP309.2

Fig.1

Iterative inference process"

Fig.2

An example of TextCNN structure"

Table 1

Dataset division details"

标注率 训练集用户数 测试集用户数
0.1 3 220 28 983
0.2 6 440 25 763
0.3 9 660 22 543
0.4 12 881 19 322
0.5 16 101 16 102
0.6 22 542 9 661
0.7 22 542 9 661
0.8 25 762 6 441
0.9 28 982 3 221

Fig.3

Gender inference accuracy"

Fig.4

Province inference accuracy"

Fig.5

City inference accuracy"

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