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[CourseClub.Me] Oreilly - Privacy-Preserving Machine Learning
magnet:?xt=urn:btih:338f20d7411c2a59a3196e14d21ed7b2b3010c17&dn=[CourseClub.Me] Oreilly - Privacy-Preserving Machine Learning
磁力链接详情
Hash值:
338f20d7411c2a59a3196e14d21ed7b2b3010c17
点击数:
39
文件大小:
1.15 GB
文件数量:
49
创建日期:
2024-1-17 23:26
最后访问:
2024-5-2 19:32
访问标签:
CourseClub
Me
Oreilly
-
Privacy-Preserving
Machine
Learning
文件列表详情
001. Part 1. Basics of privacy-preserving machine learning with differential privacy.mp4 2.34 MB
002. Chapter 1. Privacy considerations in machine learning.mp4 12.17 MB
003. Chapter 1. The threat of learning beyond the intended purpose.mp4 15.55 MB
004. Chapter 1. Threats and attacks for ML systems.mp4 34.41 MB
005. Chapter 1. Securing privacy while learning from data Privacy-preserving machine learning.mp4 28.85 MB
006. Chapter 1. How is this book structured.mp4 6.44 MB
007. Chapter 1. Summary.mp4 3.81 MB
008. Chapter 2. Differential privacy for machine learning.mp4 60.01 MB
009. Chapter 2. Mechanisms of differential privacy.mp4 52.83 MB
010. Chapter 2. Properties of differential privacy.mp4 47.45 MB
011. Chapter 2. Summary.mp4 5.1 MB
012. Chapter 3. Advanced concepts of differential privacy for machine learning.mp4 19.6 MB
013. Chapter 3. Differentially private supervised learning algorithms.mp4 47.46 MB
014. Chapter 3. Differentially private unsupervised learning algorithms.mp4 17.24 MB
015. Chapter 3. Case study Differentially private principal component analysis.mp4 64.12 MB
016. Chapter 3. Summary.mp4 4.54 MB
017. Part 2. Local differential privacy and synthetic data generation.mp4 1.14 MB
018. Chapter 4. Local differential privacy for machine learning.mp4 48.89 MB
019. Chapter 4. The mechanisms of local differential privacy.mp4 45.43 MB
020. Chapter 4. Summary.mp4 3.61 MB
021. Chapter 5. Advanced LDP mechanisms for machine learning.mp4 3.84 MB
022. Chapter 5. Advanced LDP mechanisms.mp4 25.93 MB
023. Chapter 5. A case study implementing LDP naive Bayes classification.mp4 53.74 MB
024. Chapter 5. Summary.mp4 2.49 MB
025. Chapter 6. Privacy-preserving synthetic data generation.mp4 18 MB
026. Chapter 6. Assuring privacy via data anonymization.mp4 15.08 MB
027. Chapter 6. DP for privacy-preserving synthetic data generation.mp4 28.43 MB
028. Chapter 6. Case study on private synthetic data release via feature-level micro-aggregation.mp4 44.93 MB
029. Chapter 6. Summary.mp4 2.83 MB
030. Part 3. Building privacy-assured machine learning applications.mp4 1.67 MB
031. Chapter 7. Privacy-preserving data mining techniques.mp4 9.68 MB
032. Chapter 7. Privacy protection in data processing and mining.mp4 8.09 MB
033. Chapter 7.3 Protecting privacy by modifying the input.mp4 4.36 MB
034. Chapter 7. Protecting privacy when publishing data.mp4 48.96 MB
035. Chapter 7. Summary.mp4 2.27 MB
036. Chapter 8. Privacy-preserving data management and operations.mp4 4.52 MB
037. Chapter 8. Privacy protection beyond k-anonymity.mp4 29.68 MB
038. Chapter 8. Protecting privacy by modifying the data mining output.mp4 13.89 MB
039. Chapter 8. Privacy protection in data management systems.mp4 80.56 MB
040. Chapter 8. Summary.mp4 3.63 MB
041. Chapter 9. Compressive privacy for machine learning.mp4 14.2 MB
042. Chapter 9. The mechanisms of compressive privacy.mp4 15.76 MB
043. Chapter 9. Using compressive privacy for ML applications.mp4 36.4 MB
044. Chapter 9. Case study Privacy-preserving PCA and DCA on horizontally partitioned data.mp4 103.76 MB
045. Chapter 9. Summary.mp4 3.38 MB
046. Chapter 10. Putting it all together Designing a privacy-enhanced platform (DataHub).mp4 19.64 MB
047. Chapter 10. Understanding the research collaboration workspace.mp4 27.07 MB
048. Chapter 10. Integrating privacy and security technologies into DataHub.mp4 31.84 MB
049. Chapter 10. Summary.mp4 3.42 MB
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