{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T04:11:05Z","timestamp":1685419865820},"reference-count":0,"publisher":"National Library of Serbia","issue":"4","license":[{"start":{"date-parts":[[2015,1,1]],"date-time":"2015-01-01T00:00:00Z","timestamp":1420070400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2015]]},"abstract":"<jats:p>Recommender systems, tool for predicting users? potential preferences by\n   computing history data and users? interests, show an increasing importance in\n   various Internet applications such as online shopping. As a well-known\n   recommendation method, neighbourhood-based collaborative filtering has\n   attracted considerable attentions recently. The risk of revealing users?\n   private information during the process of filtering has attracted noticeable\n   research interests. Among the current solutions, the probabilistic techniques\n   have shown a powerful privacy preserving effect. The existing methods\n   deploying probabilistic methods are in three categories, one [19] adds\n   differential privacy noises in the covariance matrix; one [1] introduces the\n   randomisation in the neighbour selection process; the other [29] applies\n   differential privacy in both the neighbour selection process and covariance\n   matrix. When facing the k Nearest Neighbour (kNN) attack, all the existing\n   methods provide no data utility guarantee, for the introduction of global\n   randomness. In this paper, to overcome the problem of recommendation accuracy\n   loss, we propose a novel approach, Partitioned Probabilistic Neighbour\n   Selection, to ensure a required prediction accuracy while maintaining high\n   security against the kNN attack. We define the sum of k neighbours?\n   similarity as the accuracy metric ?, the number of user partitions, across\n   which we select the k neighbours, as the security metric ?. We generalise the\n   k Nearest Neighbour attack to the ?k Nearest Neighbours attack. Differing\n   from the existing approach that selects neighbours across the entire\n   candidate list randomly, our method selects neighbours from each exclusive\n   partition of size k with a decreasing probability. Theoretical and\n   experimental analysis show that to provide an accuracy-assured\n   recommendation, our Partitioned Probabilistic Neighbour Selection method\n   yields a better trade-off between the recommendation accuracy and system\n   security.<\/jats:p>","DOI":"10.2298\/csis140725056l","type":"journal-article","created":{"date-parts":[[2015,11,2]],"date-time":"2015-11-02T16:39:25Z","timestamp":1446482365000},"page":"1307-1326","source":"Crossref","is-referenced-by-count":2,"title":["An accuracy-assured privacy-preserving recommender system for internet commerce"],"prefix":"10.2298","volume":"12","author":[{"given":"Zhigang","family":"Lu","sequence":"first","affiliation":[{"name":"The University of Adelaide School of Computer Science, Adelaide, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Shen","sequence":"additional","affiliation":[{"name":"The University of Adelaide, School of Computer Science, Adelaide, Australia + Sun Yat-Sen University, School of Information Science and Technology, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1078","container-title":["Computer Science and Information Systems"],"original-title":[],"language":"en","deposited":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T08:32:39Z","timestamp":1685349159000},"score":1,"resource":{"primary":{"URL":"https:\/\/doiserbia.nb.rs\/Article.aspx?ID=1820-02141500056L"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015]]},"references-count":0,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2015]]}},"URL":"https:\/\/doi.org\/10.2298\/csis140725056l","relation":{},"ISSN":["1820-0214","2406-1018"],"issn-type":[{"value":"1820-0214","type":"print"},{"value":"2406-1018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015]]}}}