Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Privacy in e-commerce: examining user scenarios and privacy preferences
Proceedings of the 1st ACM conference on Electronic commerce
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Peer-to-peer based recommendations for mobile commerce
WMC '01 Proceedings of the 1st international workshop on Mobile commerce
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Collaborative Filtering with Privacy
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
A Secure Protocol for Computing Dot-Products in Clustered and Distributed Environments
ICPP '02 Proceedings of the 2002 International Conference on Parallel Processing
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Order preserving encryption for numeric data
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
PocketLens: Toward a personal recommender system
ACM Transactions on Information Systems (TOIS)
A survey of peer-to-peer content distribution technologies
ACM Computing Surveys (CSUR)
A privacy-preserving collaborative filtering scheme with two-way communication
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Enhancing privacy and preserving accuracy of a distributed collaborative filtering
Proceedings of the 2007 ACM conference on Recommender systems
Privacy Preserving Collaborative Filtering Using Data Obfuscation
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
Preserving privacy in collaborative filtering through distributed aggregation of offline profiles
Proceedings of the third ACM conference on Recommender systems
Collaborative filtering by personality diagnosis: a hybrid memory- and model-based approach
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
ETRICS'06 Proceedings of the 2006 international conference on Emerging Trends in Information and Communication Security
A scalable privacy-preserving recommendation scheme via bisecting k-means clustering
Information Processing and Management: an International Journal
| Hi-index | 12.05 |
Collaborative filtering (CF) is a widely-used technique for generating personalized recommendations. CF systems are typically based on a central storage of user profiles, i.e., the ratings given by users to items. Such centralized storage introduces potential privacy breach, since all the user profiles may be accessible by untrusted parties when breaking the access control of the centralized system. Hence, recent studies have focused on enhancing the privacy of CF users by distributing their user profiles across multiple repositories and obfuscating the user profiles to partially hide the actual user ratings. This work combines these two techniques and investigates the unavoidable side effect of data obfuscation: the reduction of the accuracy of the generated CF predictions. The evaluation, which was conducted using three different datasets, shows that considerable parts of the user profiles can be modified without observing a substantial decrease of the CF prediction accuracy. The evaluation also indicates what parts of the user profiles are required for generating accurate CF predictions. In addition, we conducted an exploratory user study that reveals positive attitude of users towards the data obfuscation.