Recommender systems enable merchants to assist customers in finding products that best satisfy their needs. Unfortunately, current recommender systems suffer from various privacy-protection vulnerabilities. We report on the first experimental realization of a theoretical framework called ALAMBIC, which we had previously put forth to protect the privacy of customers and the commercial interests of merchants. Our system is a hybrid recommender that combines content-based, demographic and collaborative filtering techniques. The originality of our approach is to split customer data between the merchant and a semi- trusted third party, so that neither can derive sensitive information from their share alone. Therefore, the system can only be subverted by a coalition between these two parties. Experimental results confirm that the performance and user-friendliness of the application need not suffer from the adoption of such privacy-protection solutions. Furthermore, user testing of our prototype show that users react positively to the privacy model proposed.
%0 Conference Paper
%1 citeulike:4226878
%A Aimeur, E.
%A Brassard, G.
%A Fernandez, J. M.
%A Onana, F. S. M.
%A Rakowski, Z.
%B Availability, Reliability and Security, 2008. ARES 08. Third International Conference on
%D 2008
%I IEEE
%J Availability, Reliability and Security, 2008. ARES 08. Third International Conference on
%K collaborative-filtering dlpaws recommender trust
%P 161--170
%R 10.1109/ares.2008.193
%T Experimental Demonstration of a Hybrid Privacy-Preserving Recommender System
%U http://dx.doi.org/10.1109/ares.2008.193
%X Recommender systems enable merchants to assist customers in finding products that best satisfy their needs. Unfortunately, current recommender systems suffer from various privacy-protection vulnerabilities. We report on the first experimental realization of a theoretical framework called ALAMBIC, which we had previously put forth to protect the privacy of customers and the commercial interests of merchants. Our system is a hybrid recommender that combines content-based, demographic and collaborative filtering techniques. The originality of our approach is to split customer data between the merchant and a semi- trusted third party, so that neither can derive sensitive information from their share alone. Therefore, the system can only be subverted by a coalition between these two parties. Experimental results confirm that the performance and user-friendliness of the application need not suffer from the adoption of such privacy-protection solutions. Furthermore, user testing of our prototype show that users react positively to the privacy model proposed.
%@ 978-0-7695-3102-1
@inproceedings{citeulike:4226878,
abstract = {{Recommender systems enable merchants to assist customers in finding products that best satisfy their needs. Unfortunately, current recommender systems suffer from various privacy-protection vulnerabilities. We report on the first experimental realization of a theoretical framework called ALAMBIC, which we had previously put forth to protect the privacy of customers and the commercial interests of merchants. Our system is a hybrid recommender that combines content-based, demographic and collaborative filtering techniques. The originality of our approach is to split customer data between the merchant and a semi- trusted third party, so that neither can derive sensitive information from their share alone. Therefore, the system can only be subverted by a coalition between these two parties. Experimental results confirm that the performance and user-friendliness of the application need not suffer from the adoption of such privacy-protection solutions. Furthermore, user testing of our prototype show that users react positively to the privacy model proposed.}},
added-at = {2018-03-19T12:24:51.000+0100},
author = {Aimeur, E. and Brassard, G. and Fernandez, J. M. and Onana, F. S. M. and Rakowski, Z.},
biburl = {https://www.bibsonomy.org/bibtex/2747049c82a94e151f88997f4b3b2d8e3/aho},
booktitle = {Availability, Reliability and Security, 2008. ARES 08. Third International Conference on},
citeulike-article-id = {4226878},
citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1371602.1371893},
citeulike-linkout-1 = {http://dx.doi.org/10.1109/ares.2008.193},
citeulike-linkout-2 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4529334},
doi = {10.1109/ares.2008.193},
institution = {Dept. d''Inf. et de R.O, Univ. de Montreal, Montreal, QC},
interhash = {0301df03fe9af372da19551baa402664},
intrahash = {747049c82a94e151f88997f4b3b2d8e3},
isbn = {978-0-7695-3102-1},
journal = {Availability, Reliability and Security, 2008. ARES 08. Third International Conference on},
keywords = {collaborative-filtering dlpaws recommender trust},
month = mar,
pages = {161--170},
posted-at = {2009-04-29 15:13:57},
priority = {2},
publisher = {IEEE},
timestamp = {2018-03-19T12:24:51.000+0100},
title = {{Experimental Demonstration of a Hybrid Privacy-Preserving Recommender System}},
url = {http://dx.doi.org/10.1109/ares.2008.193},
year = 2008
}