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Journals / Turkish Journal of Electrical Engineering and Computer Science / 2010 / Cilt: 18 - Sayı: 1

P2P collaborative filtering with privacy

Cihan KALELİ
Hüseyin POLAT
Pages
101–116
Publication Date
2010-01-01
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Our real data-based results show that our schemes offer accurate NBC-based predictions with privacy eliminating central server.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:With the evolution of the Internet and e-commerce, collaborative filtering (CF) and privacy-preserving collaborative filtering (PPCF) have become popular.
📊
Key Findings & Data:The goal in CF is to generate predictions with decent accuracy, efficiently.
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Conclusion & Impact:Our real data-based results show that our schemes offer accurate NBC-based predictions with privacy eliminating central server.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Privacy · P2P · collaborative filtering

Abstract

With the evolution of the Internet and e-commerce, collaborative filtering (CF) and privacy-preserving collaborative filtering (PPCF) have become popular. The goal in CF is to generate predictions with decent accuracy, efficiently. The main issue in PPCF, however, is achieving such a goal while preserving users' privacy. Many implementations of CF and PPCF techniques proposed so far are centralized. In centralized systems, data is collected and stored by a central server for CF purposes. Centralized storage poses several hazards to users because the central server controls users' data. In this work, we investigate how to produce naïve Bayesian classifier (NBC)-based recommendations while preserving users' privacy without using a central server. In a community of people, users might create a peer-to-peer (P2P) network. Through P2P network, users can communicate with each other and exchange data to produce predictions. We share the workload of prediction process and offer referrals efficiently using P2P network. We propose privacy-preserving schemes and analyze them in terms of accuracy, privacy, and efficiency. Our real data-based results show that our schemes offer accurate NBC-based predictions with privacy eliminating central server.

Özet

With the evolution of the Internet and e-commerce, collaborative filtering (CF) and privacy-preserving collaborative filtering (PPCF) have become popular. The goal in CF is to generate predictions with decent accuracy, efficiently. The main issue in PPCF, however, is achieving such a goal while preserving users' privacy. Many implementations of CF and PPCF techniques proposed so far are centralized. In centralized systems, data is collected and stored by a central server for CF purposes. Centralized storage poses several hazards to users because the central server controls users' data. In this work, we investigate how to produce naïve Bayesian classifier (NBC)-based recommendations while preserving users' privacy without using a central server. In a community of people, users might create a peer-to-peer (P2P) network. Through P2P network, users can communicate with each other and exchange data to produce predictions. We share the workload of prediction process and offer referrals efficiently using P2P network. We propose privacy-preserving schemes and analyze them in terms of accuracy, privacy, and efficiency. Our real data-based results show that our schemes offer accurate NBC-based predictions with privacy eliminating central server.

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Cite This Article

KALELİ, C., POLAT, H. (2010). P2P collaborative filtering with privacy. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 18 - Sayı: 1, 101–116. https://ajindex.com/articles/188835

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