1 citations · 1 across the 2 of their papers we have counts for
5 papers
Deletion Inference, Reconstruction, and Compliance in Machine (Un)Learning
Ji Gao, Sanjam Garg, Mohammad Mahmoody +1
Privacy attacks on machine learning models aim to identify the data that is used to train such models. Such attacks, traditionally, are studied on static models that are trained on…
NeuraCrypt is not private
Nicholas Carlini, Sanjam Garg, Somesh Jha +3
NeuraCrypt (Yara et al. arXiv 2021) is an algorithm that converts a sensitive dataset to an encoded dataset so that (1) it is still possible to train machine learning models on the…
Is Private Learning Possible with Instance Encoding?
Nicholas Carlini, Samuel Deng, Sanjam Garg +6
A private machine learning algorithm hides as much as possible about its training data while still preserving accuracy. In this work, we study whether a non-private learning algori…
Formalizing Data Deletion in the Context of the Right to be Forgotten
Sanjam Garg, Shafi Goldwasser, Prashant Nalini Vasudevan
The right of an individual to request the deletion of their personal data by an entity that might be storing it -- referred to as the right to be forgotten -- has been explicitly r…
Adversarially Robust Learning Could Leverage Computational Hardness
Sanjam Garg, Somesh Jha, Saeed Mahloujifar +1
Over recent years, devising classification algorithms that are robust to adversarial perturbations has emerged as a challenging problem. In particular, deep neural nets (DNNs) seem…