Homomorphic Encryption for Data Science (HE4DS)

Part I Introduction and Basic Homomorphic Encryption (HE) Concepts.- Chapter 1 Introduction to Data Science.- Chapter 2 Modern Homomorphic Encryption - Introduction.- Chapter 3 Modern HE - Security Models.- Chapter 4 Approaches for Writing HE Applications.- Part II Approximations.- Chapter 5 Approximation Methods Part I: A General Overview.- Chapter 6 Approximation Methods Part II: Approximations of Standard Functions.- Part III Packing Methods.- Chapter 7 SIMD Packing Part I: Basic Packing Techniques.- Chapter 8 SIMD Packing Part II - Tile Tensor Basics.- Chapter 9 SIMD Packing Part III: Advanced Tile Tensors.- Part IV Use Cases and Other Approaches.- Chapter 10 Privacy-Preserving Machine Learning with HE.- Chapter 11 Case Study: Neural Network.

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• Aprendizagem mecânica
• Segurança de redes
• Privacidade e protecção de dados
• Teoria da codificação e criptologia
• Encriptação de dados

Encrypted models;Secure machine learning as a service (MLaaS);Applied cryptography;Homomorphic encryption (HE);Single instruction multiple data (SIMD);Packing methods;Tile tensors;Approximated computations;Cloud-based trust-models