[CourseClub.Me] Oreilly - Privacy-Preserving Machine Learning
File List
- 044. Chapter 9. Case study Privacy-preserving PCA and DCA on horizontally partitioned data.mp4 103.8 MB
- 039. Chapter 8. Privacy protection in data management systems.mp4 80.6 MB
- 015. Chapter 3. Case study Differentially private principal component analysis.mp4 64.1 MB
- 008. Chapter 2. Differential privacy for machine learning.mp4 60.0 MB
- 023. Chapter 5. A case study implementing LDP naive Bayes classification.mp4 53.7 MB
- 009. Chapter 2. Mechanisms of differential privacy.mp4 52.8 MB
- 034. Chapter 7. Protecting privacy when publishing data.mp4 49.0 MB
- 018. Chapter 4. Local differential privacy for machine learning.mp4 48.9 MB
- 013. Chapter 3. Differentially private supervised learning algorithms.mp4 47.5 MB
- 010. Chapter 2. Properties of differential privacy.mp4 47.5 MB
- 019. Chapter 4. The mechanisms of local differential privacy.mp4 45.4 MB
- 028. Chapter 6. Case study on private synthetic data release via feature-level micro-aggregation.mp4 44.9 MB
- 043. Chapter 9. Using compressive privacy for ML applications.mp4 36.4 MB
- 004. Chapter 1. Threats and attacks for ML systems.mp4 34.4 MB
- 048. Chapter 10. Integrating privacy and security technologies into DataHub.mp4 31.8 MB
- 037. Chapter 8. Privacy protection beyond k-anonymity.mp4 29.7 MB
- 005. Chapter 1. Securing privacy while learning from data Privacy-preserving machine learning.mp4 28.8 MB
- 027. Chapter 6. DP for privacy-preserving synthetic data generation.mp4 28.4 MB
- 047. Chapter 10. Understanding the research collaboration workspace.mp4 27.1 MB
- 022. Chapter 5. Advanced LDP mechanisms.mp4 25.9 MB
- 046. Chapter 10. Putting it all together Designing a privacy-enhanced platform (DataHub).mp4 19.6 MB
- 012. Chapter 3. Advanced concepts of differential privacy for machine learning.mp4 19.6 MB
- 025. Chapter 6. Privacy-preserving synthetic data generation.mp4 18.0 MB
- 014. Chapter 3. Differentially private unsupervised learning algorithms.mp4 17.2 MB
- 042. Chapter 9. The mechanisms of compressive privacy.mp4 15.8 MB
- 003. Chapter 1. The threat of learning beyond the intended purpose.mp4 15.6 MB
- 026. Chapter 6. Assuring privacy via data anonymization.mp4 15.1 MB
- 041. Chapter 9. Compressive privacy for machine learning.mp4 14.2 MB
- 038. Chapter 8. Protecting privacy by modifying the data mining output.mp4 13.9 MB
- 002. Chapter 1. Privacy considerations in machine learning.mp4 12.2 MB
- 031. Chapter 7. Privacy-preserving data mining techniques.mp4 9.7 MB
- 032. Chapter 7. Privacy protection in data processing and mining.mp4 8.1 MB
- 006. Chapter 1. How is this book structured.mp4 6.4 MB
- 011. Chapter 2. Summary.mp4 5.1 MB
- 016. Chapter 3. Summary.mp4 4.5 MB
- 036. Chapter 8. Privacy-preserving data management and operations.mp4 4.5 MB
- 033. Chapter 7.3 Protecting privacy by modifying the input.mp4 4.4 MB
- 021. Chapter 5. Advanced LDP mechanisms for machine learning.mp4 3.8 MB
- 007. Chapter 1. Summary.mp4 3.8 MB
- 040. Chapter 8. Summary.mp4 3.6 MB
- 020. Chapter 4. Summary.mp4 3.6 MB
- 049. Chapter 10. Summary.mp4 3.4 MB
- 045. Chapter 9. Summary.mp4 3.4 MB
- 029. Chapter 6. Summary.mp4 2.8 MB
- 024. Chapter 5. Summary.mp4 2.5 MB
- 001. Part 1. Basics of privacy-preserving machine learning with differential privacy.mp4 2.3 MB
- 035. Chapter 7. Summary.mp4 2.3 MB
- 030. Part 3. Building privacy-assured machine learning applications.mp4 1.7 MB
- 017. Part 2. Local differential privacy and synthetic data generation.mp4 1.1 MB
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