Holistic Collaborative Privacy Framework for Users' Privacy in Social Recommender Service
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Summary
This paper assert that utilizing third-party recommender services to generate accurate referrals are feasible, while preserving the privacy of the users' sensitive information which will be residing on a clear form only on his/her own device.
- Type
- preprint
- Published
- 2014-03-01
- Cited by
- 0
- References
- 29
- Access
- Open access
- OpenAlex
- https://openalex.org/W82952083
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:16386488
Keywords
Computer science, Service provider, Privacy policy, Internet privacy, Information privacy
References
- Attitudes Towards Information Privacy
- Tor: The Second-Generation Onion Router
- Jester 2.0 (poster abstract): evaluation of an new linear time collaborative filtering algorithm
- Collaborative privacy framework for minimizing privacy risks in an IPTV social recommender service
- Applying Consistency-Based Trust Definition to Collaborative Filtering
- 'I didn't buy it for myself' privacy and ecommerce personalization
- PocketLens: Toward a personal recommender system
- Deriving private information from randomized data
- Privacy preserving data obfuscation for inherently clustered data
- Collaborative filtering with privacy via factor analysis
- SVD-based collaborative filtering with privacy
- Privacy Preserving Distributed Learning Clustering of HealthCare Data Using Cryptography Protocols
- A study of preferences for sharing and privacy
- Privacy Risks in Recommender Systems
- Challenges in supporting end-user privacy and security management with social navigation
- Privacy-preserving collaborative filtering using randomized perturbation techniques
- Agent based middleware for private data mashup in IPTV recommender services
- Experimental Demonstration of a Hybrid Privacy-Preserving Recommender System
- PRIVE: anonymous location-based queries in distributed mobile systems
- Privacy Aware Recommender Service for IPTV Networks
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