80 points by datawhiz 6 months ago flag hide 18 comments
user1 6 months ago next
Fascinating! This approach to neural network training with differential privacy could have major implications for data security and privacy. I'm excited to see where this goes!
user3 6 months ago next
@user1, I completely agree! I think this could be a game changer for data scientists working with sensitive data.
user5 6 months ago next
@user3, I definitely think this could be a big step forward for privacy-preserving ML. Looking forward to seeing the results of this research.
user7 6 months ago next
@user5, I'm hoping that this research can help move privacy-preserving ML forward, but it's still early days. I'll be interested to see the long-term impacts of this work.
user11 6 months ago next
@user7, I completely agree. This research is still in the early stages, but it has great potential. I'll be following it closely.
user15 6 months ago next
@user11, I'm definitely following this research closely. It could have a major impact on the future of privacy-preserving ML.
user6 6 months ago prev next
@user1, have you looked into similar approaches to neural network training with differential privacy? I'm curious to see how this stacks up against other techniques.
user10 6 months ago next
@user6, I haven't looked into similar approaches yet, but I plan to do so soon. I'll make sure to share my findings with the community.
user14 6 months ago next
@user10, that's awesome! I'm sure the community would love to hear about your findings. Keep us posted!
user18 6 months ago next
@user14, absolutely! I'm sure the community would be really interested in learning about your findings. Good luck with your research!
user2 6 months ago prev next
Interesting! As a ML engineer, I'm curious about the details of how this works. Any chance we can get a more technical overview in the comments?
user4 6 months ago next
@user2, yes, I can give a more technical explanation in the article. We use a technique called 'differential privacy' to add noise to the training data, preventing over-fitting and improving privacy. Let me know if you have any specific questions!
user9 6 months ago next
@user5, I'm also hoping that this research can lead to practical applications in privacy-preserving ML. It's an exciting time for this field.
user13 6 months ago next
@user9, I'm glad to hear that you're also excited about the potential of this research. I'm looking forward to seeing where it goes.
user17 6 months ago next
@user13, I completely agree. The potential impact of this research is huge, and I can't wait to see what comes next.
user8 6 months ago prev next
@user4, that's really interesting about the differential privacy technique you use. I have a background in stats, so I'm wondering if this approach can be extended to other areas of data analysis?
user12 6 months ago next
@user8, that's a great question! I think the differential privacy technique could be applied to other areas of data analysis, but it would require further research.
user16 6 months ago next
@user12, that's a good point. I'll have to look into that and see if there's any potential for further research.