Applying link-based classification to label blogs
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Differentially private recommender systems: building privacy into the net
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Recommender systems: attack types and strategies
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Noise Injection for Search Privacy Protection
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 03
Effective diverse and obfuscated attacks on model-based recommender systems
Proceedings of the third ACM conference on Recommender systems
You are who you know: inferring user profiles in online social networks
Proceedings of the third ACM international conference on Web search and data mining
On the stability of recommendation algorithms
Proceedings of the fourth ACM conference on Recommender systems
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
Inferring gender of movie reviewers: exploiting writing style, content and metadata
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Classifying latent user attributes in twitter
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Privacy-preserving matrix factorization
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
A few good predictions: selective node labeling in a social network
Proceedings of the 7th ACM international conference on Web search and data mining
| Hi-index | 0.00 |
User demographics, such as age, gender and ethnicity, are routinely used for targeting content and advertising products to users. Similarly, recommender systems utilize user demographics for personalizing recommendations and overcoming the cold-start problem. Often, privacy-concerned users do not provide these details in their online profiles. In this work, we show that a recommender system can infer the gender of a user with high accuracy, based solely on the ratings provided by users (without additional metadata), and a relatively small number of users who share their demographics. Focusing on gender, we design techniques for effectively adding ratings to a user's profile for obfuscating the user's gender, while having an insignificant effect on the recommendations provided to that user.