USST: a two-phase privacy-preserving framework for personalized recommendation with semi-distributed training

DOI10.1016/J.INS.2022.05.083MaRDI QIDQ6191169

No author found.

Publication date: 6 March 2024

Published in: Information Sciences (Search for Journal in Brave)

zbMATH Keywords

privacy protectionrecommender systemsampled users

Mathematics Subject Classification ID

Computer science (68-XX) Game theory, economics, finance, and other social and behavioral sciences (91-XX)

Cites Work

• Locally differentially private item-based collaborative filtering
• Encrypted data processing with homomorphic re-encryption
• The Algorithmic Foundations of Differential Privacy

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