97 citations · 543 across the 40 of their papers we have counts for
11 papers · 1 filter
Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks
Jimmy Z. Di, Jack Douglas, Jayadev Acharya +2
We introduce camouflaged data poisoning attacks, a new attack vector that arises in the context of machine unlearning and other settings when model retraining may be induced. An ad…
Discrete Distribution Estimation under User-level Local Differential Privacy
Jayadev Acharya, Yuhan Liu, Ziteng Sun
We study discrete distribution estimation under user-level local differential privacy (LDP). In user-level -LDP, each user has samples and the privacy of all $…
Remember What You Want to Forget: Algorithms for Machine Unlearning
Ayush Sekhari, Jayadev Acharya, Gautam Kamath +1
We study the problem of unlearning datapoints from a learnt model. The learner first receives a dataset drawn i.i.d. from an unknown distribution, and outputs a model $\widehat…
Differentially Private Assouad, Fano, and Le Cam
Jayadev Acharya, Ziteng Sun, Huanyu Zhang
Le Cam's method, Fano's inequality, and Assouad's lemma are three widely used techniques to prove lower bounds for statistical estimation tasks. We propose their analogues under ce…
Context-Aware Local Differential Privacy
Jayadev Acharya, Keith Bonawitz, Peter Kairouz +2
Local differential privacy (LDP) is a strong notion of privacy for individual users that often comes at the expense of a significant drop in utility. The classical definition of LD…
Optimal multiclass overfitting by sequence reconstruction from Hamming queries
Jayadev Acharya, Ananda Theertha Suresh
A primary concern of excessive reuse of test datasets in machine learning is that it can lead to overfitting. Multiclass classification was recently shown to be more resistant to o…