Incorporating Differential Privacy Protection to a Basic Recommendation Engine
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Tarih
2020
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Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Recommendation 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.
Açıklama
Anahtar Kelimeler
Kaynak
INTERNATIONAL JOURNAL OF INFORMATION SECURITY SCIENCE
WoS Q Değeri
Scopus Q Değeri
Cilt
9
Sayı
1
