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种子名称： [CourseClub.Me] Oreilly - Privacy-Preserving Machine Learning
文件类型： 视频
文件数目： 49个文件
文件大小： 1.15 GB
收录时间： 2024-1-17 23:26
已经下载： 3次
资源热度： 130
最近下载： 2024-12-23 08:40

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[CourseClub.Me] Oreilly - Privacy-Preserving Machine Learning.torrent

001. Part 1. Basics of privacy-preserving machine learning with differential privacy.mp42.34MB

002. Chapter 1. Privacy considerations in machine learning.mp412.17MB

003. Chapter 1. The threat of learning beyond the intended purpose.mp415.55MB

004. Chapter 1. Threats and attacks for ML systems.mp434.41MB

005. Chapter 1. Securing privacy while learning from data Privacy-preserving machine learning.mp428.85MB

006. Chapter 1. How is this book structured.mp46.44MB

007. Chapter 1. Summary.mp43.81MB

008. Chapter 2. Differential privacy for machine learning.mp460.01MB

009. Chapter 2. Mechanisms of differential privacy.mp452.83MB

010. Chapter 2. Properties of differential privacy.mp447.45MB

011. Chapter 2. Summary.mp45.1MB

012. Chapter 3. Advanced concepts of differential privacy for machine learning.mp419.6MB

013. Chapter 3. Differentially private supervised learning algorithms.mp447.46MB

014. Chapter 3. Differentially private unsupervised learning algorithms.mp417.24MB

015. Chapter 3. Case study Differentially private principal component analysis.mp464.12MB

016. Chapter 3. Summary.mp44.54MB

017. Part 2. Local differential privacy and synthetic data generation.mp41.14MB

018. Chapter 4. Local differential privacy for machine learning.mp448.89MB

019. Chapter 4. The mechanisms of local differential privacy.mp445.43MB

020. Chapter 4. Summary.mp43.61MB

021. Chapter 5. Advanced LDP mechanisms for machine learning.mp43.84MB

022. Chapter 5. Advanced LDP mechanisms.mp425.93MB

023. Chapter 5. A case study implementing LDP naive Bayes classification.mp453.74MB

024. Chapter 5. Summary.mp42.49MB

025. Chapter 6. Privacy-preserving synthetic data generation.mp418MB

026. Chapter 6. Assuring privacy via data anonymization.mp415.08MB

027. Chapter 6. DP for privacy-preserving synthetic data generation.mp428.43MB

028. Chapter 6. Case study on private synthetic data release via feature-level micro-aggregation.mp444.93MB

029. Chapter 6. Summary.mp42.83MB

030. Part 3. Building privacy-assured machine learning applications.mp41.67MB

031. Chapter 7. Privacy-preserving data mining techniques.mp49.68MB

032. Chapter 7. Privacy protection in data processing and mining.mp48.09MB

033. Chapter 7.3 Protecting privacy by modifying the input.mp44.36MB

034. Chapter 7. Protecting privacy when publishing data.mp448.96MB

035. Chapter 7. Summary.mp42.27MB

036. Chapter 8. Privacy-preserving data management and operations.mp44.52MB

037. Chapter 8. Privacy protection beyond k-anonymity.mp429.68MB

038. Chapter 8. Protecting privacy by modifying the data mining output.mp413.89MB

039. Chapter 8. Privacy protection in data management systems.mp480.56MB

040. Chapter 8. Summary.mp43.63MB

041. Chapter 9. Compressive privacy for machine learning.mp414.2MB

042. Chapter 9. The mechanisms of compressive privacy.mp415.76MB

043. Chapter 9. Using compressive privacy for ML applications.mp436.4MB

044. Chapter 9. Case study Privacy-preserving PCA and DCA on horizontally partitioned data.mp4103.76MB

045. Chapter 9. Summary.mp43.38MB

046. Chapter 10. Putting it all together Designing a privacy-enhanced platform (DataHub).mp419.64MB

047. Chapter 10. Understanding the research collaboration workspace.mp427.07MB

048. Chapter 10. Integrating privacy and security technologies into DataHub.mp431.84MB

049. Chapter 10. Summary.mp43.42MB