Pistis: A Privacy-preserving Content Recommender System For Online Social Communities.pdf

pistis_waiat11.pdf
Preview of Pistis: A Privacy-preserving Content Recommender System for Online Social Communities
🔗 Source: recmind.cn
📊 Size: 422 KB
👤 Author: Dongsheng Li, Qin Lv, Huanhuan Xia, Li Shang, Tun Lu, Ning Gu
⬇️ Downloads: 83

Summary

Pistis is a privacy-preserving content recommender system for online social communities that identifies user interests without disclosing personal information. It addresses the privacy issue of collaborative filtering (CF) methods, which can expose sensitive personal interests. Pistis uses interest groups and distributed secure multi-party computation to conceal sensitive interests while achieving high recommendation quality. It has been deployed and evaluated in an online social community, outperforming two state-of-the-art CF methods in privacy preservation, recommendation quality, and efficiency.

Description

Pistis is a privacy-preserving content recommender system for online social communities that identifies user interests without disclosing personal information.

Technical Information

  • File Format: PDF
  • File Size: 422 KB
  • Pages: 8
  • Language: EN
  • Author: Dongsheng Li, Qin Lv, Huanhuan Xia, Li Shang, Tun Lu, Ning Gu
  • Total Downloads: 83
  • Last Updated: 1 month ago

Document Overview

This PDF document about Pistis: A Privacy-preserving Content Recommender System for Online Social Communities provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Pistis: A Privacy-preserving Content Recommender System for Online Social Communities.

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