Huseyin Polat
Wenliang Du
ABSTRACT
Randomized perturbation techniques (RPT) are applied to perturb the customers' private data to protect privacy while providing accurate referrals. In the RPT-based collaborative filtering (CF) with privacy schemes, proposed so far, users disguise their ratings in the same way to achieve consistently perturbed data. However, since users might have different levels of concerns about their privacy, the customers might decide to perturb their private data differently, which causes inconsistently masked data. How, then, can e-companies present referrals using such data and how can inconsistent data disguising affect accuracy and privacy?
- J. Canny. Collaborative filtering with privacy. In Proceedings of the IEEE Symposium on Security and Privacy, pages 45--57, Oakland, CA, USA, May 2002. Google Scholar
Digital Library
- J. Canny. Collaborative filtering with privacy via factor analysis. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 238--245, Tampere, Finland, August 2002. Google ScholarDigital Library
- L. F. Cranor. 'I didn't buy it for myself' privacy and E-commerce personalization. In Proceedings of the 2003 ACM Workshop on Privacy in the Electronic Society, pages 111--117, Washington, DC, October 30 2003. Google ScholarDigital Library
- L. F. Cranor, J. Reagle, and M. S. Ackerman. Beyond concern: Understanding net users' attitudes about online privacy. Technical report, AT&T Labs-Research, 1999.Google Scholar
- D. Gupta, M. Digiovanni, H. Narita, and K. Goldberg. Jester 2.0: A new linear-time collaborative filtering algorithm applied to jokes. In Proceedings of the Workshop on Recommender Systems: Algorithms and Evaluation, 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, CA, USA, August 1999. Google ScholarDigital Library
- J. L. Herlocker, J. A. Konstan, A. Borchers, and J. T. Riedl. An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, CA, USA, August 15--19 1999. Google ScholarDigital Library
- H. Polat and W. Du. Privacy-preserving collaborative filtering. International Journal of Electronic Commerce, 9(4):9--36, 2005.Google Scholar
- P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. T. Riedl. GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of the ACM Conference on Computer Supported Cooperative Work, pages 175--186, Chapel Hill, NC, USA, 1994. Google ScholarDigital Library
- B. M. Sarwar, G. Karypis, J. A. Konstan, and J. T. Reidl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International World Wide Web Conference (WWW10), pages 285--295, Hong Kong, China, May 1--5 2001. Google ScholarDigital Library
- B. M. Sarwar, G. Karypis, J. A. Konstan, and J. T. Riedl. Application of dimensionality reduction in recommender system--A case study. In Proceedings of the ACM WebKDD 2000 Web Mining for E-commerce Workshop, Boston, MA, USA, August 20 2000.Google ScholarCross Ref
- J. M. Seigneur and C. D. Jensen. Trading privacy for trust. In Proceedings of the 2nd International Conference on Trust Management, pages 93--107, Oxford, UK, March 29-April 1 2004.Google ScholarCross Ref
Index Terms
- Effects of inconsistently masked data using RPT on CF with privacy
Recommendations
Arbitrarily distributed data-based recommendations with privacy
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 ...
An Improved Profile-Based CF Scheme with Privacy
ICSC '11: Proceedings of the 2011 IEEE Fifth International Conference on Semantic ComputingTraditional collaborative filtering (CF) systems widely employing k-nearest neighbor (kNN) algorithms mostly attempt to alleviate the contemporary problem of information overload by generating personalized predictions for items that users might like. ...
Privacy-preserving hybrid collaborative filtering on cross distributed data
Data collected for collaborative filtering (CF) purposes might be cross distributed between two online vendors, even competing companies. Such corporations might want to integrate their data to provide more precise and reliable recommendations. However, ...
Comments