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文档列表

044. Chapter 9. Case study Privacy-preserving PCA and DCA on horizontally partitioned data.mp4 108.8 MB
039. Chapter 8. Privacy protection in data management systems.mp4 84.5 MB
015. Chapter 3. Case study Differentially private principal component analysis.mp4 67.2 MB
008. Chapter 2. Differential privacy for machine learning.mp4 62.9 MB
023. Chapter 5. A case study implementing LDP naive Bayes classification.mp4 56.3 MB
009. Chapter 2. Mechanisms of differential privacy.mp4 55.4 MB
034. Chapter 7. Protecting privacy when publishing data.mp4 51.3 MB
018. Chapter 4. Local differential privacy for machine learning.mp4 51.3 MB
013. Chapter 3. Differentially private supervised learning algorithms.mp4 49.8 MB
010. Chapter 2. Properties of differential privacy.mp4 49.8 MB
019. Chapter 4. The mechanisms of local differential privacy.mp4 47.6 MB
028. Chapter 6. Case study on private synthetic data release via feature-level micro-aggregation.mp4 47.1 MB
043. Chapter 9. Using compressive privacy for ML applications.mp4 38.2 MB
004. Chapter 1. Threats and attacks for ML systems.mp4 36.1 MB
048. Chapter 10. Integrating privacy and security technologies into DataHub.mp4 33.4 MB
037. Chapter 8. Privacy protection beyond k-anonymity.mp4 31.1 MB
005. Chapter 1. Securing privacy while learning from data Privacy-preserving machine learning.mp4 30.2 MB
027. Chapter 6. DP for privacy-preserving synthetic data generation.mp4 29.8 MB
047. Chapter 10. Understanding the research collaboration workspace.mp4 28.4 MB
022. Chapter 5. Advanced LDP mechanisms.mp4 27.2 MB
046. Chapter 10. Putting it all together Designing a privacy-enhanced platform (DataHub).mp4 20.6 MB
012. Chapter 3. Advanced concepts of differential privacy for machine learning.mp4 20.6 MB
025. Chapter 6. Privacy-preserving synthetic data generation.mp4 18.9 MB
014. Chapter 3. Differentially private unsupervised learning algorithms.mp4 18.1 MB
042. Chapter 9. The mechanisms of compressive privacy.mp4 16.5 MB
003. Chapter 1. The threat of learning beyond the intended purpose.mp4 16.3 MB
026. Chapter 6. Assuring privacy via data anonymization.mp4 15.8 MB
041. Chapter 9. Compressive privacy for machine learning.mp4 14.9 MB
038. Chapter 8. Protecting privacy by modifying the data mining output.mp4 14.6 MB
002. Chapter 1. Privacy considerations in machine learning.mp4 12.8 MB
031. Chapter 7. Privacy-preserving data mining techniques.mp4 10.2 MB
032. Chapter 7. Privacy protection in data processing and mining.mp4 8.5 MB
006. Chapter 1. How is this book structured.mp4 6.7 MB
011. Chapter 2. Summary.mp4 5.3 MB
016. Chapter 3. Summary.mp4 4.8 MB
036. Chapter 8. Privacy-preserving data management and operations.mp4 4.7 MB
033. Chapter 7.3 Protecting privacy by modifying the input.mp4 4.6 MB
021. Chapter 5. Advanced LDP mechanisms for machine learning.mp4 4.0 MB
007. Chapter 1. Summary.mp4 4.0 MB
040. Chapter 8. Summary.mp4 3.8 MB
020. Chapter 4. Summary.mp4 3.8 MB
049. Chapter 10. Summary.mp4 3.6 MB
045. Chapter 9. Summary.mp4 3.5 MB
029. Chapter 6. Summary.mp4 3.0 MB
024. Chapter 5. Summary.mp4 2.6 MB
001. Part 1. Basics of privacy-preserving machine learning with differential privacy.mp4 2.5 MB
035. Chapter 7. Summary.mp4 2.4 MB
030. Part 3. Building privacy-assured machine learning applications.mp4 1.8 MB
017. Part 2. Local differential privacy and synthetic data generation.mp4 1.2 MB
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