Search Results for author: Varun Chandrasekaran

Found 16 papers, 8 papers with code

Verifiable and Provably Secure Machine Unlearning

1 code implementation17 Oct 2022 Thorsten Eisenhofer, Doreen Riepel, Varun Chandrasekaran, Esha Ghosh, Olga Ohrimenko, Nicolas Papernot

In our cryptographic protocol, the server first computes a proof that the model was trained on a dataset~$D$.


Hierarchical Federated Learning with Privacy

no code implementations10 Jun 2022 Varun Chandrasekaran, Suman Banerjee, Diego Perino, Nicolas Kourtellis

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.

Federated Learning

SoK: Machine Learning Governance

no code implementations20 Sep 2021 Varun Chandrasekaran, Hengrui Jia, Anvith Thudi, Adelin Travers, Mohammad Yaghini, Nicolas Papernot

The application of machine learning (ML) in computer systems introduces not only many benefits but also risks to society.

BIG-bench Machine Learning

Proof-of-Learning: Definitions and Practice

2 code implementations9 Mar 2021 Hengrui Jia, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Anvith Thudi, Varun Chandrasekaran, Nicolas Papernot

In particular, our analyses and experiments show that an adversary seeking to illegitimately manufacture a proof-of-learning needs to perform *at least* as much work than is needed for gradient descent itself.

A General Framework For Detecting Anomalous Inputs to DNN Classifiers

1 code implementation29 Jul 2020 Jayaram Raghuram, Varun Chandrasekaran, Somesh Jha, Suman Banerjee

We propose an unsupervised anomaly detection framework based on the internal DNN layer representations in the form of a meta-algorithm with configurable components.

Image Classification Unsupervised Anomaly Detection

Face-Off: Adversarial Face Obfuscation

1 code implementation19 Mar 2020 Chuhan Gao, Varun Chandrasekaran, Kassem Fawaz, Somesh Jha

We implement and evaluate Face-Off to find that it deceives three commercial face recognition services from Microsoft, Amazon, and Face++.

Cryptography and Security

Entangled Watermarks as a Defense against Model Extraction

1 code implementation27 Feb 2020 Hengrui Jia, Christopher A. Choquette-Choo, Varun Chandrasekaran, Nicolas Papernot

Such pairs are watermarks, which are not sampled from the task distribution and are only known to the defender.

Model extraction Transfer Learning

On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping

1 code implementation26 Feb 2020 Sanghyun Hong, Varun Chandrasekaran, Yiğitcan Kaya, Tudor Dumitraş, Nicolas Papernot

In this work, we study the feasibility of an attack-agnostic defense relying on artifacts that are common to all poisoning attacks.

Data Poisoning

Machine Unlearning

2 code implementations9 Dec 2019 Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, Nicolas Papernot

Once users have shared their data online, it is generally difficult for them to revoke access and ask for the data to be deleted.

Transfer Learning

Generating Semantic Adversarial Examples with Differentiable Rendering

no code implementations2 Oct 2019 Lakshya Jain, Wilson Wu, Steven Chen, Uyeong Jang, Varun Chandrasekaran, Sanjit Seshia, Somesh Jha

In this paper we explore semantic adversarial examples (SAEs) where an attacker creates perturbations in the semantic space representing the environment that produces input for the ML model.

Autonomous Driving

Rearchitecting Classification Frameworks For Increased Robustness

no code implementations26 May 2019 Varun Chandrasekaran, Brian Tang, Nicolas Papernot, Kassem Fawaz, Somesh Jha, Xi Wu

and how to design a classification paradigm that leverages these invariances to improve the robustness accuracy trade-off?

Autonomous Driving Classification +1

Exploring Connections Between Active Learning and Model Extraction

no code implementations5 Nov 2018 Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli, Somesh Jha, Songbai Yan

This has resulted in the surge of Machine Learning-as-a-Service (MLaaS) - cloud services that provide (a) tools and resources to learn the model, and (b) a user-friendly query interface to access the model.

Active Learning BIG-bench Machine Learning +1

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