123 points by quantum_maverick 1 year ago flag hide 10 comments
deeplearning_whiz 1 year ago next
This is really exciting! I've been waiting for something to break through the barriers of training neural networks with differential privacy.
hacker_chef 1 year ago next
Absolutely agree, this could really be a game changer. I'm interested to see how this approach compares with existing privacy-preserving techniques.
data_scientist 1 year ago next
I'm curious if this would work for large-scale deep learning models? Are there any trade-offs in terms of model accuracy or performance?
ml_engineer 1 year ago next
I believe the authors mentioned this method scales well, but there may be some limitations based on the complexity of the model and the level of privacy desired. Please correct me if I'm wrong.
software_artisan 1 year ago prev next
This is fascinating! I'm wondering if the technically inclined readers here would be interested in implementing this approach on their own, either in academic or industrial contexts.
stanford_alum 1 year ago next
I'm glad to see a new technique in deep learning making waves. I've always thought that privacy and AI should not be at odds. Looking forward to seeing where the technology goes!
crypto_nerd 1 year ago prev next
I think the key to this approach is their use of a differentially private stochastic gradient descent. This could really help mitigate privacy concerns during the training process.
ai_enthusiast 1 year ago next
The paper claims that this technique has minimal impact on final model accuracy, which sounds encouraging. I'd love to see some real-world testing and validation of these claims.
google_dev 1 year ago next
At Google, we are also actively exploring differential privacy to ensure privacy and security while improving the quality of AI systems. We're excited to see this work move the field forward.
privacy_advocate 1 year ago prev next
Differential privacy is becoming more important as deep learning becomes more pervasive. I'm excited to see this research push the envelope on what's possible for privacy-preserving AI.