Lightweight Collaborative Filtering Method for Binary-Encoded Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
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 decision tree learning over multiple parties
Data & Knowledge Engineering
Privacy-preserving top-N recommendation on distributed data
Journal of the American Society for Information Science and Technology
Expert Systems with Applications: An International Journal
Privacy-preserving Naïve Bayes classification
The VLDB Journal — The International Journal on Very Large Data Bases
Privacy-preserving decision trees over vertically partitioned data
ACM Transactions on Knowledge Discovery from Data (TKDD)
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
Class dependent feature scaling method using naive Bayes classifier for text datamining
Pattern Recognition Letters
Similar or Dissimilar Users? Or Both?
ISECS '09 Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 02
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
Collaborative filtering with the simple Bayesian classifier
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Improving Privacy-Preserving NBC-Based Recommendations by Preprocessing
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
ARTCOM '10 Proceedings of the 2010 International Conference on Advances in Recent Technologies in Communication and Computing
Privacy preserving Back-propagation neural network learning over arbitrarily partitioned data
Neural Computing and Applications
TrustBus'11 Proceedings of the 8th international conference on Trust, privacy and security in digital business
A semi-naive bayesian learning method for utilizing unlabeled data
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Arbitrarily distributed data-based recommendations with privacy
Data & Knowledge Engineering
Privacy-preserving hybrid collaborative filtering on cross distributed data
Knowledge and Information Systems
Efficient privacy preserving k-means clustering
PAISI'10 Proceedings of the 2010 Pacific Asia conference on Intelligence and Security Informatics
Secure knowledge management: confidentiality, trust, and privacy
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Privacy-preserving SOM-based recommendations on horizontally distributed data
Knowledge-Based Systems
Privacy-preserving genetic algorithms for rule discovery
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Learning conditional preference network from noisy samples using hypothesis testing
Knowledge-Based Systems
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Providing partitioned data-based recommendations has been receiving increasing attention due to mutual advantages. In case of limited data, it is not likely to estimate accurate and reliable predictions. Therefore, e-commerce sites holding insufficient ratings prefer offering predictions to their customers based on integrated data. However, users' preferences about products are considered online vendors' confidential and valuable assets; and they do not want to disclose them their partners during collaborative prediction processes. In order to eliminate privacy, financial, and legal concerns of those companies having inadequate data and want to provide recommendations on combined data, we propose a privacy-preserving scheme to estimate naive Bayesian classifier-based predictions on arbitrarily partitioned data between two parties. Our method helps online vendors provide binary ratings-based predictions on partitioned data without violating their confidentiality requirements. We show that the proposed scheme is secure and able to offer recommendations efficiently. Our real data-based experiments demonstrate that collaboration is vital for better services; and accuracy losses due to privacy measures can be suppressed by the gains due to collaboration. Thus, our method is preferable for estimating accurate predictions efficiently on partitioned data while preserving data holders' privacy over the scheme on split data only.