Differential privacy is a system for publicly sharing information about a dataset while withholding individual information. It is a constraint on algorithms used to publish aggregate information which limits the disclosure of private information. Differential privacy provably resists identification and reidentification attacks.
Stanford University
Fall 2022-2023
Offered by Stanford University, this course focuses on AI applications in healthcare, exploring deep learning models for image, text, multimodal, and time-series data in the healthcare context. Topics also address AI integration challenges like interpretability and privacy.
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+ 27 more conceptsStanford University
Spring 2022
CS 168 provides a comprehensive introduction to modern algorithm concepts, covering hashing, dimension reduction, programming, gradient descent, and regression. It emphasizes both theoretical understanding and practical application, with each topic complemented by a mini-project. It's suitable for those who have taken CS107 and CS161.
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+ 57 more conceptsBrown University
Spring 2023
Applied Cryptography at Brown University offers a practical take on securing systems. By learning foundational cryptographic algorithms and advanced topics like zero-knowledge proofs and post-quantum cryptography, students gain both theoretical insights and hands-on experience in implementing cryptosystems using C++ and crypto libraries. Label: State-of-art concepts.
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