Privacy-preserving collaborative filtering using randomized perturbation techniques

By Huseyin Polat, Wenliang Du. Third IEEE International Conference on Data Mining, 2003

358 citations recorded by the source corpus.

Collaborative filtering (CF) techniques are becoming increasingly popular with the evolution of the Internet. To conduct collaborative filtering, data from customers are needed. However, collecting high quality data from customers is not an easy task because many customers are so concerned about their privacy that they might decide to give false information. We propose a randomized perturbation (RP) technique to protect users' privacy while still producing accurate recommendations.

Subfields: Artificial Intelligence, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/fab892a03b74c19af0fb5e5c0222ae5e1a7d8769

How researchers see this paper

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

In Artificial Intelligence

1. Jun Zhou
2. Yuan Qi
3. Xiaolin Zheng
4. Chaochao Chen
5. Wolfgang Nejdl
6. W. Fang
7. Li Wang
8. Paul-Alexandru Chirita

In Sociology and Political Science

1. Yuan Qi
2. Xiaolin Zheng
3. Chaochao Chen
4. W. Fang
5. Jun Zhou
6. Li Wang
7. Bingzhe Wu

Citations in the corpus

• Preventing shilling attacks in online recommender systems (2005, background): “We think that zero-knowledge attacks such as the Random Bot are particularly interesting, since for the other attacks, recommender systems administrators could increase the privacy of user profiles using cryptographic means [6, 10, 2], thus falling back to the zero-knowledge ones.”
• Practical Privacy Preserving POI Recommendation (2020, background): “Therefore, both data and models of most existing recommender systems could be in high privacy risks [43, 48].”
• Introduction to Recommender Systems Handbook (2011, neutral): “• Privacy preserving recommender systems [81, 26, 79, 56, 17, 28, 102, 16, 5, 53, 70, 114].”
• Recommender Systems (2016, neutral): “The earliest studies on the topic of privacy-preserving recommendations were presented in [133, 451, 484, 485, 667].”