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Privacy in Machine Learning

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May 14, 2024 3 minute read

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Privacy is a significant concern in today's data-driven world, particularly when working with sensitive information in the context of machine learning (ML). Privacy-preserving machine learning techniques aim to protect data while enabling valuable insights to be extracted.

Why Privacy in Machine Learning?

Privacy in machine learning is of utmost importance for several reasons. Firstly, legal and ethical obligations require organizations to protect individuals' data privacy. The European Union's General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) are examples of regulations that impose strict data protection requirements.

Path to Privacy in Machine Learning

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We've curated two courses to help you on your path to Privacy in Machine Learning. Use these to develop your skills, build background knowledge, and put what you learn to practice.

Sorted from most relevant to least relevant:

Aplicando privacidad en modelos de Machine Learning

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Clinical Decision Support Systems - CDSS 4

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Reading list

We've selected four books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Privacy in Machine Learning.

The Algorithmic Foundations of Differential Privacy

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An in-depth exploration of differential privacy, a mathematical framework for protecting data privacy in machine learning. It covers the theoretical foundations of differential privacy and provides practical guidance on implementing differential privacy in machine learning systems.

The Algorithmic Foundations of Differential Privacy...

Paperback

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Machine Learning Refined

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Introduces the fundamental concepts and challenges in privacy-preserving machine learning, covering both theoretical foundations and practical techniques.

Machine Learning Refined: Foundations, Algorithms,...

Hardcover

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Machine Learning Refined: Foundations, Algorithms,...

Hardcover

Machine Learning Refined: Foundations, Algorithms,...

Kindle Edition

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Data Mining

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Presents a comprehensive overview of privacy-preserving data mining, covering both theoretical and practical aspects. It covers a wide range of techniques for protecting data privacy in data mining tasks, such as clustering, classification, and association rule mining.

Advances and Open Problems in Federated Learning

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Focuses on federated learning, a specific type of privacy-preserving machine learning where data is stored on local devices and models are trained without sharing the data. It discusses the challenges and benefits of federated learning and provides practical guidance for implementing federated learning systems.

Advances and Open Problems in Federated Learning...

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Relevant careers

Machine Learning Engineer

Data Scientist

Privacy in Machine Learning

Why Privacy in Machine Learning?

Path to Privacy in Machine Learning

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