An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
Collaborative Filtering with Privacy
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
Privacy-preserving distributed k-means clustering over arbitrarily partitioned data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Association rules mining in vertically partitioned databases
Data & Knowledge Engineering - Special issue: WIDM 2004
Privacy-Preserving Two-Party K-Means Clustering via Secure Approximation
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 01
Privacy preserving decision tree learning over multiple parties
Data & Knowledge Engineering
Privacy-preserving distributed association rule mining via semi-trusted mixer
Data & Knowledge Engineering
Privacy preserving clustering on horizontally partitioned data
Data & Knowledge Engineering
Distributed prediction from vertically partitioned data
Journal of Parallel and Distributed Computing
Guest editorial: Recent advances in preserving privacy when mining data
Data & Knowledge Engineering
Privacy-preserving top-N recommendation on distributed data
Journal of the American Society for Information Science and Technology
Using error-correcting dependencies for collaborative filtering
Data & Knowledge Engineering
Privacy-preserving decision trees over vertically partitioned data
ACM Transactions on Knowledge Discovery from Data (TKDD)
Data privacy protection in multi-party clustering
Data & Knowledge Engineering
Privacy Preserving BIRCH Algorithm for Clustering over Arbitrarily Partitioned Databases
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Providing Naïve Bayesian Classifier-Based Private Recommendations on Partitioned Data
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Providing predictions on distributed HMMs with privacy
Artificial Intelligence Review
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
Efficient privacy preserving distributed clustering based on secret sharing
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Privacy preserving Back-propagation neural network learning over arbitrarily partitioned data
Neural Computing and Applications
Recommendation-based editor for business process modeling
Data & Knowledge Engineering
Reliable representations for association rules
Data & Knowledge Engineering
Frequent Item Computation on a Chip
IEEE Transactions on Knowledge and Data Engineering
Privacy-preserving collaborative filtering on vertically partitioned data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Privacy-preserving hybrid collaborative filtering on cross distributed data
Knowledge and Information Systems
On private scalar product computation for privacy-preserving data mining
ICISC'04 Proceedings of the 7th international conference on Information Security and Cryptology
Privacy-preserving genetic algorithms for rule discovery
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Fully homomorphic encryption based two-party association rule mining
Data & Knowledge Engineering
Estimating NBC-based recommendations on arbitrarily partitioned data with privacy
Knowledge-Based Systems
| Hi-index | 0.00 |
Collaborative filtering (CF) systems use customers' preferences about various products to offer recommendations. Providing accurate and reliable predictions is vital for both e-commerce companies and their customers. To offer such referrals, CF systems should have sufficient data. When data collected for CF purposes held by a central server, it is an easy task to provide recommendations. However, customers' preferences represented as ratings might be partitioned between two vendors. To supply trustworthy and correct predictions, such companies might desire to collaborate. Due to privacy concerns, financial fears, and legal issues; however, the parties may not want to disclose their data to each other. In this study, we scrutinize how to estimate item-based predictions on arbitrarily distributed data (ADD) between two e-commerce sites without deeply jeopardizing their privacy. We analyze our proposed scheme in terms of privacy; and demonstrate that the method does not intensely violate data owners' confidentiality. We conduct experiments using real data sets to show how coverage and quality of the predictions improve due to collaboration. We also investigate our scheme in terms of online performance; and demonstrate that supplementary online costs caused by privacy measures are negligible. Moreover, we perform trials to show how privacy concerns affect accuracy. Our results show that accuracy and coverage improve due to collaboration; and the proposed scheme is still able to offer truthful predictions with privacy concerns.