Safe Machine Learning: Cryptographic Opportunities

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Introduction to Cryptography: One-Time Pad and Public-Key Systems

Introduces cryptography, focusing on the one-time pad and public-key systems, emphasizing privacy and authenticity in information security.

Homomorphic Encryption

Explores homomorphic encryption, enabling computations on encrypted data without decryption, with practical applications in secure medical clouds.

Anonymous Credentials: Binding Identity to Physical Authentication

Covers the binding of anonymous credentials to human identity through visual authentication and cryptographic protocols.

Machine Learning Security

Explores machine learning security, including model stealing, altering outputs, adversarial conditions, and privacy challenges, emphasizing the importance of addressing biases in machine learning models.

Non-Attributable Email: Forward-Forgeable Signatures

Explores non-attributable email using forward-forgeable signatures and the legal risks faced by security researchers.

E-Voting: Research and Challenges

Explores e-voting research, digital algorithms, privacy challenges, and cryptographic methods in ensuring legitimacy and participation.

Machine Learning and Privacy

Explores Federated Machine Learning and Differential Privacy in Machine Learning, discussing attacks, defenses, and challenges.

Introduction to Technology and Humanitarian Action

Introduces the MUK on technology, digital transformation, and humanitarian action, emphasizing responsible technology use in the humanitarian sector.

Internet Capsules: Opportunities and Dangers

Explores the opportunities and dangers of the internet, including encryption, surveillance, and censorship.

Data Privacy in eHealth Research

Explores data privacy challenges and perspectives in eHealth research, focusing on GDPR compliance, sensitive health data management, and decentralized agents.