https://spylab.ai/blog/side-channels-machine-learning/ Privacy side channels in machine learning systemsEdoardo Debenedetti, Florian TramèrSep 12, 2023 “An additional application of our membership inference attack is to test whether specific data sources were part
What I Read: Federated, Protects Privacy
https://pair.withgoogle.com/explorables/federated-learning/ How Federated Learning Protects Privacy “With federated learning, it’s possible to collaboratively train a model with data from multiple users without any raw data leaving their devices.”
What I Read: Adversarial Neural Cryptography
https://pub.towardsai.net/what-is-adversarial-neural-cryptography-70b461c7db88?gi=3bf97cbeefd What is Adversarial Neural Cryptography?The novel approach combines GANs and cryptography in a single, powerful security method.Jesus RodriguezApr 19 “Somewhere between anonymization methods and homomorphic encryption, we find a
What I Read: Difficulty of Graph Anonymisation
https://www.timlrx.com/blog/tracetogether-and-the-difficulty-of-graph-anonymisation Timothy Lin@timlrxxSunday, February 7, 2021TraceTogether and the Difficulty of Graph Anonymisation “The word “anonymised data” seems to convey a certain sense of certainty that user information cannot be back-derived.