A randomized protocol for signing contracts
Communications of the ACM
All-or-nothing disclosure of secrets
Proceedings on Advances in cryptology---CRYPTO '86
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Jester 2.0 (poster abstract): evaluation of an new linear time collaborative filtering algorithm
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Collaborative Filtering with Privacy
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
'I didn't buy it for myself' privacy and ecommerce personalization
Proceedings of the 2003 ACM workshop on Privacy in the electronic society
K-means clustering via principal component analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Privacy-Preserving Top-N Recommendation on Horizontally Partitioned Data
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Effects of inconsistently masked data using RPT on CF with privacy
Proceedings of the 2007 ACM symposium on Applied computing
Collaborative filtering based on iterative principal component analysis
Expert Systems with Applications: An International Journal
Achieving private recommendations using randomized response techniques
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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
An improved privacy-preserving DWT-based collaborative filtering scheme
Expert Systems with Applications: An International Journal
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With the evolution of e-commerce, privacy is becoming a major concern. Many e-companies employ collaborative filtering (CF) techniques to increase their sales by providing truthful recommendations to customers. Many algorithms have been employed for CF purposes; and Eigentaste-based algorithm is one of them. Customers' preferences about products they purchased previously or showed interest are needed to provide recommendations. However, due to privacy concerns, customers refuse to contribute their ratings at all; or they might decide to give false data. Providing truthful referrals based on such inadequate and false data is impossible. Therefore, providing privacy measures is vital for collecting truthful data and producing recommendations. In this paper, we investigate how to achieve CF tasks (predictions and top-N recommendations) using Eigentaste, which is a constant time CF algorithm, without greatly exposing users' privacy. To accomplish privacy, we employ randomized perturbation techniques (RPT). We modify and/or simplify original Eigentaste algorithm in such a way to provide private referrals efficiently with decent accuracy. We investigate our proposed schemes in terms of privacy. To evaluate the overall performance of our schemes, we conduct experiments using real data sets. We then analyze our outcomes and finally provide some suggestions.