BlurMe: inferring and obfuscating user gender based on ratings

By Udi Weinsberg, Smriti Bhagat, Stratis Ioannidis, Nina Taft. 2012

194 citations recorded by the source corpus.

Subfields: Clinical Psychology, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Clinical Psychology

1. Neil Zhenqiang Gong
2. Jinyuan Jia
3. Bin Liu
4. Dan Boneh

In Information Systems

1. Neil Zhenqiang Gong
2. Jinyuan Jia
3. Binghui Wang
4. Le Zhang
5. Bin Liu
6. Shotaro Akaho
7. Marc Joye
8. Valeria Nikolaenko

Citations in the corpus

• Applying Differential Privacy to Matrix Factorization (2015, background): “The raw user ratings, even if anonymized, pose a privacy risk: the data can be de-anonymized using information obtained from other sources and then be used to infer sensitive information [18], e.”
• AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning (2018, methodology): “The LR classifier was also used by previous attribute inference attacks [3, 5, 6, 9].”
• Efficiency Improvement of Neutrality-Enhanced Recommendation (2013, background): “To protect private information contained in rating information, dummy ratings were added [18].”
• Privacy-preserving matrix factorization (2013, background): “This requirement is key: earlier studies [61] show that even knowing which movie a user has rated can be used to infer, e.”
• AttriInfer: Inferring User Attributes in Online Social Networks Using Markov Random Fields (2017, methodology): “One problem of increasing interest revolves around these private attributes [16, 19, 34, 28, 13, 22, 18, 32, 4, 17, 12].”
• Secure Function Evaluation Using an FPGA Overlay Architecture (2017, background): “3021746 mining has given rise to significant privacy concerns, extensively documented by researchers [20, 24, 26, 30, 34, 36, 40] as well as the popular press [2, 42].”
• You Are Who You Know and How You Behave: Attribute Inference Attacks via Users' Social Friends and Behaviors (2016, methodology): “[42] investigated the inference of gender using the rating scores that users gave to different movies.”