1.6k citations · 1.7k across the 14 of their papers we have counts for
11 papers · 1 filter
Why Train More? Effective and Efficient Membership Inference via Memorization
Jihye Choi, Shruti Tople, Varun Chandrasekaran +1
Membership Inference Attacks (MIAs) aim to identify specific data samples within the private training dataset of machine learning models, leading to serious privacy violations and…
Verifiable and Provably Secure Machine Unlearning
Thorsten Eisenhofer, Doreen Riepel, Varun Chandrasekaran +3
Machine unlearning aims to remove points from the training dataset of a machine learning model after training: e.g., when a user requests their data to be deleted. While many unlea…
Proof-of-Learning is Currently More Broken Than You Think
Congyu Fang, Hengrui Jia, Anvith Thudi +5
Proof-of-Learning (PoL) proposes that a model owner logs training checkpoints to establish a proof of having expended the computation necessary for training. The authors of PoL for…
Hierarchical Federated Learning with Privacy
Varun Chandrasekaran, Suman Banerjee, Diego Perino +1
Federated learning (FL), where data remains at the federated clients, and where only gradient updates are shared with a central aggregator, was assumed to be private. Recent work d…
Unrolling SGD: Understanding Factors Influencing Machine Unlearning
Anvith Thudi, Gabriel Deza, Varun Chandrasekaran +1
Machine unlearning is the process through which a deployed machine learning model is made to forget about some of its training data points. While naively retraining the model from…
Causally Constrained Data Synthesis for Private Data Release
Varun Chandrasekaran, Darren Edge, Somesh Jha +3
Making evidence based decisions requires data. However for real-world applications, the privacy of data is critical. Using synthetic data which reflects certain statistical propert…