🔊 Edge#31: Differential Privacy

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💡 ML Concept of the Day: What is Differential Privacy?

In The Sequence Edge#30, we covered the universe of privacy-preserving machine learning (PPML). As security and privacy become more important in machine learning models, many PPML techniques are steadily making their way into popular machine learning frameworks. One such technique that has become common in PPML scenarios is known as differential privacy (DP). In fact, most of the current implementations of PPML techniques are based on some form of DP ->that's important to know

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🔎 ML Research You Should Know: Apple’s Framework for Differential Privacy Learning 

Apple is responsible for some of the largest implementations of DP techniques in machine learning scenarios. The architecture outlined in the paper is the result of the best practices and lessons learned in real-world implementations ->dive deeper into that fascinating research paper

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🤖 ML Technology to Follow: TensorFlow Privacy Brings Differential Privacy to Deep Learning Models. 

TensorFlow Privacy is a framework for enabling the training of deep learning models using DP methods. The framework includes a series of DP algorithms that can be easily incorporated in deep learning models without modifying their core architecture.  

What is it and how can I use it? ->know more

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🧠 The Quiz

Now, to our regular quiz. After ten quizzes, we will reward the winners. Participate! The questions are the following:

1. What’s the main idea of differential privacy?

2. What is the fundamental component of the Tensor Flow Privacy framework?

That was fun! Thank you. See you on Thursday 😉

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