https://github.com/arjunbahuguna/awesome-ppdm

Privacy-preserving data-mining resources
https://github.com/arjunbahuguna/awesome-ppdm

List: awesome-ppdm

Last synced: 16 days ago
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Privacy-preserving data-mining resources

• Host: GitHub
• URL: https://github.com/arjunbahuguna/awesome-ppdm
• Owner: arjunbahuguna
• Created: 2019-04-22T15:14:47.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-04-22T16:20:07.000Z (over 5 years ago)
• Last Synced: 2024-05-23T01:06:06.491Z (7 months ago)
• Size: 18.6 KB
• Stars: 5
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• ultimate-awesome - awesome-ppdm - Privacy-preserving data-mining resources. (Other Lists / Monkey C Lists)

README

        
awesome - ppdm [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

 =========

Resources for privacy-preserving data-mining.

## Other Compilations

* [awesome-ppml](https://github.com/mortendahl/awesome-ppml)

* [awesome-homomorphic-encryption](https://github.com/jonaschn/awesome-he)

* [awesome-multiparty-computation](https://github.com/rdragos/awesome-mpc)

* [awesome-differential-privacy](https://github.com/menisadi/awesome-differential-privacy)

## Workshopw

* [Theory and Practice of Multi-Party Computation Workshops](http://www.multipartycomputation.com/tpmpc-2019)

* [Simons Data Privacy Program 2019](https://simons.berkeley.edu/programs/privacy2019)

* [Deep Learning & Security Workshop, IEEE 2019](https://www.ieee-security.org/TC/SPW2019/DLS/)

* [Workshop on Security in Machine Learning, NeurIPS 2018](https://secml2018.github.io/)

* [Privacy-preserving Machine Learning, NeurIPS 2018](https://ppml-workshop.github.io/ppml/)

* [Deep Learning & Security Workshop, IEEE 2018](https://www.ieee-security.org/TC/SPW2018/DLS/)

* [Private Multi-party Machine Learning, NIPS 2016](https://pmpml.github.io/PMPML16/)

* [Workshop on Learning, Security and Privacy, ICML 2014](https://sites.google.com/site/learnsecprivacy2014/program)

## Courses

* [by Vitaly Shmatikov](http://www.cs.cornell.edu/~shmat/teaching.html)

* [by Arvind Narayanan](http://randomwalker.info/teaching/)

* [by Reza Shokri (not public)](https://www.comp.nus.edu.sg/~reza/)

* [by Aaron Roth](https://www.cis.upenn.edu/~aaroth/)

## Videos

* [AI Village 2018](https://www.youtube.com/watch?v=D09MkTeQfOQ&list=PL9fPq3eQfaaAMFXytVqYl1sfN_A14vrj7)

* [Goldwasser ON Crypto-ML](https://www.youtube.com/watch?v=bMJtCe1aK0w)

* [CAMLIS Conference](https://www.youtube.com/channel/UCmIY4lIVsotxeUDRCQb2ZXA/videos)

* [NeurIPS SecML 2018](https://www.youtube.com/watch?v=zbhQijVlGxg&list=PLFG9vaKTeJq4IpOje38YWA9UQu_COeNve)

* [Hacking Deep Learning 2018](https://www.youtube.com/playlist?list=PL8Vt-7cSFnw2_ewRERdxYPeme5sa4Eu4M)

* [Hacking Deep Learning 2019](https://www.youtube.com/playlist?list=PL8Vt-7cSFnw1dO9kc2_SQQRchzpQQDYXy)

* [ML in Cybersec playlist](https://www.youtube.com/watch?v=tukidI5vuBs&list=PLSRlqkflyPqR2Tc2_N4Ixb2N5bc43YdEM)

* [IEEE Deep Learning & Security Workshop 2018](https://www.youtube.com/playlist?list=PL0pRF4xvoD0liEIWyJ6kmXqGT7nbr2L3u)

## Blogs

* [Cleverhans](http://www.cleverhans.io/)

* [OpenMined](https://blog.openmined.org/)

* [Dropout Labs](https://medium.com/dropoutlabs)

* [Mortendahl](https://mortendahl.github.io/)

* [Metaflow](https://blog.metaflow.fr/)

# Resources from Twitter

* [Tutorials by Trask](https://twitter.com/iamtrask/status/1101894655631912960?s=19)

* [TF Frameworks for S&P](https://twitter.com/dropoutlabsai/status/1100110783424253952)

* [Code examples for tfencrypted](https://twitter.com/mortendahlcs/status/1118100102835519488?s=19)

## Books

* [Interpretable ML book](https://christophm.github.io/interpretable-ml-book/)

* [Algorithmic Foundations of DP (PDF)](www.cis.upenn.edu/~aaroth/privacybook.html)

* [A Pragmatic Introduction to Secure Multi-Party Computation](https://securecomputation.org/)

## Foundational Papers

* [Revealing Information while Preserving Privacy](www.cse.psu.edu/~ads22/privacy598/papers/dn03.pdf)

* [Calibrating Noise to Sensitivity in Private Data Analysis](https://journalprivacyconfidentiality.org/index.php/jpc/article/download/405/388)

## Follow

* [Kobbi Nissim](https://dblp.org/pers/hd/n/Nissim:Kobbi)

* [Adrià Gascón](https://dblp.org/pers/hd/g/Gasc=oacute=n:Adri=agrave=)

* [Morten Dahl](https://dblp.org/pers/hd/d/Dahl:Morten)

* [Cynthia Dwork](https://dblp.org/pers/hd/d/Dwork:Cynthia)