150 points by nnresearcher 1 year ago flag hide 29 comments
learn_ai 1 year ago next
This is really cool. I've been trying to implement differential privacy in my own neural network training. Has anyone else tried this? Any tips or resources you'd recommend?
hacker_ai 1 year ago next
Yes, I implemented differential privacy in my neural network recently. I followed this tutorial <https://tinyurl.com/nn-diff-privacy> and it worked out great. It was a bit tricky, but it's definitely doable.
ml_engineer 1 year ago prev next
Definitely check out the TensorFlow Privacy library <https://tinyurl.com/tf-privacy>. It's an open-source library that makes it easy to implement differential privacy in TensorFlow models.
deep_learning 1 year ago prev next
This is amazing. I never thought about combining neural networks and differential privacy. Has anyone tried using this approach to train models on sensitive medical data?
medical_analytics 1 year ago next
Yes, we've been using this approach to train models on sensitive medical data for a few months now. The differentially private models are less accurate than standard models, but the added privacy protections are worth it for us.
stats_fan 1 year ago prev next
I'm interested in how this approach affects model performance. Has anyone run any comparisons between differentially private and standard models?
research_scientist 1 year ago next
Yes, we've done some comparisons between differentially private and standard models in our own research. We found that differentially private models have lower accuracy than standard models, but the difference depends on the model architecture and the amount of privacy provided. Here's our paper: <https://tinyurl.com/diff-privacy-performance>
security_geek 1 year ago prev next
I'm always interested in new privacy-preserving techniques. I like how this approach ensures that the training data doesn't leak any sensitive information. Very cool!
algorithm_expert 1 year ago prev next
The math behind differential privacy is pretty complex, but once you understand it, it's an elegant solution to the privacy problem. I'm glad to see it being applied to neural network training.
data_analyst 1 year ago prev next
I'm surprised that the neural network training still works with differential privacy. I would have thought that the noise added to the model weights would make the training unstable.
numerical_methods 1 year ago next
Yes, the added noise can make the training less stable, but there are techniques to control the noise and ensure that the training converges. It's a trade-off between privacy and performance.
security_auditor 1 year ago prev next
I'm concerned that this approach still leaks some amount of private information. Is there any research on the amount of information leaked with differential privacy?
privacy_engineer 1 year ago next
Yes, there's a lot of research on the trade-off between privacy and accuracy in differential privacy. The amount of information leaked depends on the sensitivity of the data and the amount of noise added. There are asymptotic bounds on the leaked information and techniques to improve the privacy guarantees.
researcher 1 year ago prev next
I'm curious if anyone has tried using differential privacy for other machine learning techniques, like clustering or dimensionality reduction.
algorithm_enthusiast 1 year ago next
Yes, there's some research on using differential privacy for clustering, like differentially private k-means clustering. It's a challenging problem because the clustering process can amplify the noise added for privacy, but there are techniques to mitigate the noise and ensure the clustering works.
data_scientist 1 year ago prev next
This is fascinating. I'd like to learn more about how differential privacy works and how to implement it in my own machine learning models.
code_monkey 1 year ago prev next
I'd be interested in seeing a code example of using differential privacy in TensorFlow.
expert_opinion 1 year ago prev next
Differential privacy is still a relatively new technique, but it has a lot of potential to improve privacy in machine learning. I'm looking forward to seeing more research in this area.
student_researcher 1 year ago prev next
I'm a student researcher and I'm thinking of using differential privacy for my master's thesis. I'd like to hear from people who have used differential privacy in practice. What were the biggest challenges you faced? What advice do you have for me?
senior_researcher 1 year ago next
I've used differential privacy in my research and I can tell you it's not an easy technique to use. The biggest challenge is finding the right amount of noise to add to achieve the desired level of privacy. My advice is to start with a simple problem and gradually add complexity.
silicon_valley_exec 1 year ago prev next
This is a great step forward for privacy-preserving machine learning. I'm glad to see that it's possible to balance privacy and performance with differential privacy.
tech_startup_founder 1 year ago prev next
I'm starting a new tech company and I'd like to use differential privacy from the beginning. Can anyone suggest a good implementation or library to use?
open_source_advocate 1 year ago next
I'd recommend checking out the TensorFlow Privacy library for implementing differential privacy in your models. It's open source and has a lot of useful features.
crypto_enthusiast 1 year ago prev next
Is there any research on combining differential privacy with homomorphic encryption for end-to-end privacy-preserving machine learning?
cryptographer 1 year ago next
Yes, there is some research on combining differential privacy with homomorphic encryption for end-to-end privacy-preserving machine learning. However, it's still an active research area and the techniques are not yet mature for widespread use.
math_geek 1 year ago prev next
The math behind differential privacy is really interesting. It's a way to quantify the privacy of a dataset by adding noise and analyzing the effects. Kudos to the researchers for developing this technique!
ml_team_lead 1 year ago prev next
I'm considering using differential privacy in my team's next machine learning project. I'd like to hear from others who have used differential privacy for real-world applications. How did it impact the model performance and what challenges did you encounter?
software_engineer 1 year ago next
We used differential privacy for training a model on user location data. We added noise to the gradient updates to ensure privacy and it worked well. The only challenge we faced was finding the right amount of noise to add for the desired level of privacy.
artificial_intelligence_guru 1 year ago prev next
Differential privacy is a game changer for privacy-preserving machine learning. It's a way to use data without compromising privacy and it's the future of artificial intelligence.