Incorporating Differential Privacy Protection to a Basic Recommendation Engine

Basit Öğe Kaydı
dc.contributor.authorİnan, Ali
dc.date.accessioned2025-01-06T17:24:14Z
dc.date.available2025-01-06T17:24:14Z
dc.date.issued2020
dc.departmentAdana Alparslan Türkeş Bilim ve Teknoloji Üniversitesi
dc.description.abstractRecommendation engines analyze ratings data to suggest individuals new products or services based on their past experiences. However, the set of items that an individual has rated and the ratings on these items are critical for protecting individual privacy. Existing work on the problem focus on overly complicated recommendation engines. In this study, we concentrate on the case of a very simple engine protected with a very strong mechanism. Towards this goal, we incorporate differential privacy to an item-based neighborhood predictor. Empirical analyses over large-scale, real-world rating data indicate the efficiency of our proposed solution. Even at very high levels of protection, the rate of loss in prediction accuracy is below 5%, a reasonable trade-off for privacy protection.
dc.identifier.endpage12
dc.identifier.issn2147-0030
dc.identifier.issue1
dc.identifier.startpage1
dc.identifier.trdizinid359923
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/359923
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1136
dc.identifier.volume9
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofINTERNATIONAL JOURNAL OF INFORMATION SECURITY SCIENCE
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.titleIncorporating Differential Privacy Protection to a Basic Recommendation Engine
dc.typeArticle

Dosyalar

Koleksiyon

TR-Dizin İndeksli Yayınlar Koleksiyonu