Personalized recommendations combat internet information overload. Privacy concerns hinder this; users hesitate to share data. We suggest generating fake profiles to protect privacy without compromising recommendation accuracy. Our client-based framework addresses this, ensuring existing algorithms remain unchanged. Our privacy model defines ideal fake profiles, emphasizing feature distribution similarity and sensitive subject coverage. We implement this using a product classification subject repository. Theoretical and experimental evidence supports our approach’s effectiveness.
- Fake Profiles for Privacy
- Personalized Recommendations
- Privacy Protection
Security stats reveal a surge in data leaks, often due to human errors. To combat this, a privacy-preserving data-leak detection method is proposed. It allows secure delegation of detection operations without disclosing sensitive data, offering a privacy-focused add-on service for Internet providers, with effective results in real-world scenarios.
- Data Security
- Data-Leak Detection
- Privacy Protection
