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Revolutionary Approach to Neural Networks: Training on Encrypted Data(example.com)

520 points by cryptoneural 1 year ago | flag | hide | 16 comments

  • user1 1 year ago | next

    This is quite an interesting development! Training neural networks on encrypted data could open up new possibilities for privacy-preserving ML applications. I'm excited to see where this research leads.

    • user3 1 year ago | next

      The problem with such techniques is that they tend to be computationally expensive and may affect nn's performance. It would be interesting to understand the compromises made here.

      • user1 1 year ago | next

        Absolutely right, user3. Computational overhead is definitely a concern. However, technology advances and optimizations might resolve this issue in the future. From the paper, it seems that their proposed method has a small impact on model accuracy.

  • user2 1 year ago | prev | next

    I agree! Implications on a wide range of fields from healthcare to finance could be profound. We just need to make sure that it's properly implemented without introducing any new vulnerabilities.

  • user4 1 year ago | prev | next

    It's a delicate balance between privacy and accuracy indeed. I'd be curious to know if the training data distribution impacts the results.

  • user5 1 year ago | prev | next

    Great idea, bad implementation. The work is promising, but I see many flaws with it. Hopefully, they can strengthen it with more measures and defenses.

    • user6 1 year ago | next

      Heavy criticism, user5, but agreed. There's room for improvement. That said, the breakthrough is creating waves and that's what we need, more minds focusing on improving it.

  • user7 1 year ago | prev | next

    With end-to-end encryption, we could create privacy-respecting predictive intelligence models. That's exactly what we need now with all the buzz around privacy violations.

  • user8 1 year ago | prev | next

    There's a concern about sharing models trained on encrypted data, as models could unconsciously memorize their training data and reveal them with certain inputs (membership inference attack).

  • user9 1 year ago | prev | next

    For some niche use-cases in that field (medical records and privacy-preservation, for example), that might actually be just enough. Still, the overhead is worrisome. I wonder how it'd perform in real-world applications.

    • user10 1 year ago | next

      I would like to learn more about their experimental settings and hardware requirements as well.

      • user12 1 year ago | next

        Totally. That's the brutality of research. There's a lot to do before it reaches the realm of possibility. Early days yet!

  • user11 1 year ago | prev | next

    Sounds like it needs much more research until it is practically usable. Really intriguing, though!

  • user13 1 year ago | prev | next

    Researchers published a similar concept in 20XX. It'd be interesting to compare this work and see if they've solved any shortcomings found in the original research.

  • user14 1 year ago | prev | next

    Great point, user13! I've read the other work you're mentioning. Comparing techniques might shed light on improvements and non-obvious trade-offs. Thanks for bringing that up!

  • user15 1 year ago | prev | next

    The homomorphic encryption technique they've used is a real game-changer. Looking forward to testing their models and seeing the performance.