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Revolutionary Approach to Neural Network Training with Differential Privacy(deepmind.com)

1234 points by deepmind_ai 1 year ago | flag | hide | 15 comments

  • deeplearningdan 1 year ago | next

    This is a really innovative approach to training neural networks with differential privacy. Thanks for sharing!

    • mathmage 1 year ago | next

      I've been working on similar projects and I can confirm that this method works well in practice. It's definitely a game changer.

    • codedude 1 year ago | prev | next

      I'm excited to see where this technique will be applied. Maybe it can be used to train models on sensitive medical data?

      • statsgal 1 year ago | next

        I agree, differential privacy is a powerful tool for protecting user data. I hope this technique becomes widely adopted in the industry.

  • neuronninja 1 year ago | prev | next

    I'm curious how this compares to traditional training methods. Has there been any benchmarking conducted?

    • mlmonk 1 year ago | next

      Yes, there have been some studies done on this topic and the results are very promising. Here's a link to a recent paper: <https://arxiv.org/abs/XXXXXX>

      • aiadvocate 1 year ago | next

        I'm really impressed by this research. It's great to see the community pushing the boundaries of what's possible with differential privacy.

    • datascientist 1 year ago | prev | next

      I've been trying to implement differential privacy in my projects but I've found it to be very challenging. Do you have any tips for getting started?

      • privacypro 1 year ago | next

        Yes, I recommend checking out Google's TensorFlow Privacy library. It's very user-friendly and has a lot of helpful documentation and examples.

        • codecrusader 1 year ago | next

          Thanks for the recommendation! I've been looking for a good library to use.

      • aiapprentice 1 year ago | prev | next

        Thanks for the suggestion, I'll definitely check it out! I'm still a bit confused about the trade-offs between accuracy and privacy. Can anyone explain this better?

        • dlguru 1 year ago | next

          In general, there is a trade-off between privacy and accuracy, but recent research has shown that this doesn't have to be a zero-sum game. With the right techniques and hyperparameters, you can achieve both privacy and high accuracy.

          • mathmadman 1 year ago | next

            I recently read a paper on this topic and the authors suggested using a different optimizer to balance the trade-off. This could be a promising avenue for further research.

            • datadetective 1 year ago | next

              That's an interesting point. I'll have to read up on that paper and see what they suggest.

        • mlmaster 1 year ago | prev | next

          I've found that using a smaller learning rate and adding regularization can help improve accuracy while still maintaining privacy. It's a bit of a balancing act, but it's definitely possible.