icmpnorequest / Attribute-Inference-Attack-Paper-Reading

Paper list of attribute inference attack

Paper List of User Attributes/Traits Inference

Contributed by Yantong Lai

Welcome to your contribution:

• Paper about User Attributes Inference / Defense methods;
• Experiments code in those paper;
• Paper reading notes with those paper

Thanks in advance!

1. User Attributes/Traits Inference

Please Notice: Paper listed below is in Paper/Attack directory.

1.1 User Attributes Inference Based on Text

1. [18] Preoţiuc-Pietro D, Ungar L. User-level race and ethnicity predictors from twitter text[C]//Proceedings of the 27th International Conference on Computational Linguistics. 2018: 1534-1545.
2. [19 ACL] Preotiuc-Pietro D, Gaman M, Aletras N. Automatically identifying complaints in social media[J]. arXiv preprint arXiv:1906.03890, 2019.
3. [18 BigData] Roy Khristopher Bayot and Teresa Gonçalves. 2018. Age and Gender Classification of Tweets Using Convolutional Neural Networks. In Machine Learning, Optimization, and Big Data. Springer International Publishing, Cham
4. [17 ACL Text + Social network]Mac Kim, S., Xu, Q., Qu, L., Wan, S., Paris, C.: Demographic inference on twitter using recursive neural networks. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). pp. 471–477 (2017)
5. [19 WWW] Tigunova,A.,Yates,A.,Mirza,P.,Weikum,G.:Listening between the lines: Learning personal attributes from conversations. In: The World Wide Web Conference. pp. 1818–1828 (2019)

1.2 User Attributes Inference Based on Behavior

Please Notice: Behavior mainly stands for "likes" or "status" in social media, not including "following/followed".

1. [13 PNAS] M.Kosinski, D.Stillwell, and T.Graepel. Private traits and attributes are predictable from digital records of human behavior. PNAS, 110(13).

1.3 User Attributes Inference Based on Social Network

1. [16 USENIX] Gong, Neil Zhenqiang, and Bin Liu. "You are who you know and how you behave: Attribute inference attacks via users' social friends and behaviors." 25th {USENIX} Security Symposium ({USENIX} Security 16). 2016.
2. [17 WWW] Jia, Jinyuan, et al. "AttriInfer: Inferring user attributes in online social networks using markov random fields." Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2017.
3. [18 ACM TOPS] Gong, Neil Zhenqiang, and Bin Liu. "Attribute inference attacks in online social networks." ACM Transactions on Privacy and Security (TOPS) 21.1 (2018): 3.
4. [19 ACL] Pan J, Bhardwaj R, Lu W, et al. Twitter Homophily: Network Based Prediction of User’s Occupation[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019: 2633-2638.
5. [18 Hypertext] Aletras N, Chamberlain B P. Predicting twitter user socioeconomic attributes with network and language information[M]//Proceedings of the 29th on Hypertext and Social Media. 2018: 20-24.�
   
   

2. User Attributes Helps Improve Classification

In the following fields:

• sentiment classification
• topic detection
• sarcasm detection
• fake news detection
• hate speech detection/toxic comment detection
• mental health detection
• stance detection
• opinion change prediction
• life satisfaction or mortality rate
• detection/identification of linguistic features of specific group
  
  

3. Defense against User Attributes Inference

Please Notice: Paper listed below is in Paper/Defense directory.

1. [18 USENIX] Jia, Jinyuan, and Neil Zhenqiang Gong. "Attriguard: A practical defense against attribute inference attacks via adversarial machine learning." 27th {USENIX} Security Symposium ({USENIX} Security 18). 2018.
2. [18 TDSC] Cai, Zhipeng, et al. "Collective data-sanitization for preventing sensitive information inference attacks in social networks." IEEE Transactions on Dependable and Secure Computing15.4 (2016): 577-590.
   
   

4. Data Utility

Please Notice: Paper listed below is in Paper/Data Utility directory.

1. [09 KDD] Li, Tiancheng, and Ninghui Li. "On the tradeoff between privacy and utility in data publishing." Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2009.
2. [Public Data Utility] Rajeshwari, N. O., and C. N. Sowmyarani. "Data utility measures-a survey." 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT). IEEE, 2016.
   
   

5. Security and Privacy of Models in Machine Learning

Please Notice: Paper listed below is in Paper/Application directory.

1. [18 CCS] Ganju, Karan, et al. "Property inference attacks on fully connected neural networks using permutation invariant representations." Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2018.
2. [15 IJSN Attribute Infenrence Attack] Hacking Smart Machines with Smarter Ones How to Extract Meaningful Data from Machine Learning Classifiers
3. [19 S&P DL Privacy] Comprehensive Privacy Analysis of Deep Learning
   
   

6. Discussion/Survey/Review about User Attributes/User Traits (No model proposed)

1. [18 Survey] Pan S, Ding T. Automatically Infer Human Traits and Behavior from Social Media Data[J]. arXiv preprint arXiv:1804.04191, 2018.
2. [20 ACL Survey & Discussion] Flek L. Returning the N to NLP: Towards Contextually Personalized Classification Models[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020: 7828-7838.
3. [19 IJCAI] Pan S, Ding T. Social Media-based User Embedding: A Literature Review
   
   

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