CourseClub Me Oreilly Privacy Preserving Machine Learning | 338F20D7411C2A59A3196E14D21ED7B2B3010C17

CourseClub Me Oreilly Privacy Preserving Machine Learning

Name	[CourseClub.Me] Oreilly - Privacy-Preserving Machine Learning	DOWNLOAD Copy Link
Total Size	1.2 GB
Total Files	50
Last Seen	2024-07-16 23:58
Hash	338F20D7411C2A59A3196E14D21ED7B2B3010C17

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001. Part 1. Basics of privacy-preserving machine learning with differential privacy.mp4	2.5 MB
002. Chapter 1. Privacy considerations in machine learning.mp4	12.8 MB
003. Chapter 1. The threat of learning beyond the intended purpose.mp4	16.3 MB
004. Chapter 1. Threats and attacks for ML systems.mp4	36.1 MB
005. Chapter 1. Securing privacy while learning from data Privacy-preserving machine learning.mp4	30.2 MB
006. Chapter 1. How is this book structured.mp4	6.7 MB
007. Chapter 1. Summary.mp4	4.0 MB
008. Chapter 2. Differential privacy for machine learning.mp4	62.9 MB
009. Chapter 2. Mechanisms of differential privacy.mp4	55.4 MB
010. Chapter 2. Properties of differential privacy.mp4	49.8 MB
011. Chapter 2. Summary.mp4	5.3 MB
012. Chapter 3. Advanced concepts of differential privacy for machine learning.mp4	20.6 MB
013. Chapter 3. Differentially private supervised learning algorithms.mp4	49.8 MB
014. Chapter 3. Differentially private unsupervised learning algorithms.mp4	18.1 MB
015. Chapter 3. Case study Differentially private principal component analysis.mp4	67.2 MB
016. Chapter 3. Summary.mp4	4.8 MB
017. Part 2. Local differential privacy and synthetic data generation.mp4	1.2 MB
018. Chapter 4. Local differential privacy for machine learning.mp4	51.3 MB
019. Chapter 4. The mechanisms of local differential privacy.mp4	47.6 MB
020. Chapter 4. Summary.mp4	3.8 MB
021. Chapter 5. Advanced LDP mechanisms for machine learning.mp4	4.0 MB
022. Chapter 5. Advanced LDP mechanisms.mp4	27.2 MB
023. Chapter 5. A case study implementing LDP naive Bayes classification.mp4	56.3 MB
024. Chapter 5. Summary.mp4	2.6 MB
025. Chapter 6. Privacy-preserving synthetic data generation.mp4	18.9 MB
026. Chapter 6. Assuring privacy via data anonymization.mp4	15.8 MB
027. Chapter 6. DP for privacy-preserving synthetic data generation.mp4	29.8 MB
028. Chapter 6. Case study on private synthetic data release via feature-level micro-aggregation.mp4	47.1 MB
029. Chapter 6. Summary.mp4	3.0 MB
030. Part 3. Building privacy-assured machine learning applications.mp4	1.8 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
033. Chapter 7.3 Protecting privacy by modifying the input.mp4	4.6 MB
034. Chapter 7. Protecting privacy when publishing data.mp4	51.3 MB
035. Chapter 7. Summary.mp4	2.4 MB
036. Chapter 8. Privacy-preserving data management and operations.mp4	4.7 MB
037. Chapter 8. Privacy protection beyond k-anonymity.mp4	31.1 MB
038. Chapter 8. Protecting privacy by modifying the data mining output.mp4	14.6 MB
039. Chapter 8. Privacy protection in data management systems.mp4	84.5 MB
040. Chapter 8. Summary.mp4	3.8 MB
041. Chapter 9. Compressive privacy for machine learning.mp4	14.9 MB
042. Chapter 9. The mechanisms of compressive privacy.mp4	16.5 MB
043. Chapter 9. Using compressive privacy for ML applications.mp4	38.2 MB
044. Chapter 9. Case study Privacy-preserving PCA and DCA on horizontally partitioned data.mp4	108.8 MB
045. Chapter 9. Summary.mp4	3.5 MB
046. Chapter 10. Putting it all together Designing a privacy-enhanced platform (DataHub).mp4	20.6 MB
047. Chapter 10. Understanding the research collaboration workspace.mp4	28.4 MB
048. Chapter 10. Integrating privacy and security technologies into DataHub.mp4	33.4 MB
049. Chapter 10. Summary.mp4	3.6 MB
[CourseClub.Me].url	0.1 KB
Total files 50