{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T11:49:57Z","timestamp":1726314597451},"reference-count":65,"publisher":"Association for Computing Machinery (ACM)","issue":"5","funder":[{"name":"DDAI"},{"name":"COMET Module within the COMET \u2014 Competence Centers for Excellent Technologies Programme"},{"name":"Austrian Federal Ministry for Transport, Innovation and Technology"},{"name":"Austrian Federal Ministry for Digital and Economic Affairs"},{"name":"Austrian Research Promotion Agency"},{"name":"TU Graz Open Access Publishing Fund, the Austrian Science Fund","award":["P33526 and DFH-23"]},{"name":"State of Upper Austria and the Federal Ministry of Education, Science, and Research","award":["LIT-2020-9-SEE-113"]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2023,10,31]]},"abstract":"\n User-based\n KNN<\/jats:italic>\n recommender systems (\n UserKNN<\/jats:italic>\n ) utilize the rating data of a target user\u2019s\n k<\/jats:italic>\n nearest neighbors in the recommendation process. This, however, increases the privacy risk of the neighbors, since the recommendations could expose the neighbors\u2019 rating data to other users or malicious parties. To reduce this risk, existing work applies differential privacy by adding randomness to the neighbors\u2019 ratings, which unfortunately reduces the accuracy of\n UserKNN<\/jats:italic>\n . In this work, we introduce\n ReuseKNN<\/jats:italic>\n , a novel differentially private KNN-based recommender system. The main idea is to identify small but highly reusable neighborhoods so that (i) only a minimal set of users requires protection with differential privacy and (ii) most users do not need to be protected with differential privacy since they are only rarely exploited as neighbors. In our experiments on five diverse datasets, we make two key observations. Firstly,\n ReuseKNN<\/jats:italic>\n requires significantly smaller neighborhoods and, thus, fewer neighbors need to be protected with differential privacy compared with traditional\n UserKNN<\/jats:italic>\n . Secondly, despite the small neighborhoods,\n ReuseKNN<\/jats:italic>\n outperforms\n UserKNN<\/jats:italic>\n and a fully differentially private approach in terms of accuracy. Overall,\n ReuseKNN<\/jats:italic>\n leads to significantly less privacy risk for users than in the case of\n UserKNN<\/jats:italic>\n .\n <\/jats:p>\n ","DOI":"10.1145\/3608481","type":"journal-article","created":{"date-parts":[[2023,7,13]],"date-time":"2023-07-13T12:09:09Z","timestamp":1689250149000},"page":"1-29","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["ReuseKNN: Neighborhood Reuse for Differentially Private KNN-Based Recommendations"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-6581-1945","authenticated-orcid":false,"given":"Peter","family":"M\u00fcllner","sequence":"first","affiliation":[{"name":"Know-Center GmbH and Graz University of Technology, Austria"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5293-2967","authenticated-orcid":false,"given":"Elisabeth","family":"Lex","sequence":"additional","affiliation":[{"name":"Graz University of Technology, Austria"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1706-3406","authenticated-orcid":false,"given":"Markus","family":"Schedl","sequence":"additional","affiliation":[{"name":"Johannes Kepler University Linz and Linz Institute of Technology, Austria"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3230-6234","authenticated-orcid":false,"given":"Dominik","family":"Kowald","sequence":"additional","affiliation":[{"name":"Know-Center GmbH and Graz University of Technology, Austria"}]}],"member":"320","published-online":{"date-parts":[[2023,8,11]]},"reference":[{"key":"e_1_3_4_2_2","volume-title":"Proc. of the RMSE\u201919 Workshop, in Conjunction with ACM RecSys\u201919","author":"Abdollahpouri Himan","year":"2019","unstructured":"Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2019. 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