Somesh Jha

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University of Wisconsin-Madison

Professor of Computer Science at University of Wisconsin, Chief Scientist at Tala Systems

A Shuffling Framework for Local Differential Privacy

ldp deployments are vulnerable to inference attacks as an adversary can ...
06/11/2021 ∙ by Casey Meehan, et al. ∙ 2 ∙ share

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Hard-label Manifolds: Unexpected Advantages of Query Efficiency for Finding On-manifold Adversarial Examples

Designing deep networks robust to adversarial examples remains an open p...
03/04/2021 ∙ by Washington Garcia, et al. ∙ 13 ∙ share

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Exploring Adversarial Robustness of Deep Metric Learning

Deep Metric Learning (DML), a widely-used technique, involves learning a...
02/14/2021 ∙ by Thomas Kobber Panum, et al. ∙ 11 ∙ share

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CaPC Learning: Confidential and Private Collaborative Learning

Machine learning benefits from large training datasets, which may not al...
02/09/2021 ∙ by Christopher A. Choquette-Choo, et al. ∙ 2 ∙ share

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Sample Complexity of Adversarially Robust Linear Classification on Separated Data

We consider the sample complexity of learning with adversarial robustnes...
12/19/2020 ∙ by Robi Bhattacharjee, et al. ∙ 6 ∙ share

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ShadowNet: A Secure and Efficient System for On-device Model Inference

On-device machine learning (ML) is getting more and more popular as fast...
11/11/2020 ∙ by Zhichuang Sun, et al. ∙ 13 ∙ share

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Intertwining Order Preserving Encryption and Differential Privacy

Ciphertexts of an order-preserving encryption (OPE) scheme preserve the ...
09/11/2020 ∙ by Amrita Roy Chowdhury, et al. ∙ 0 ∙ share

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Detecting Anomalous Inputs to DNN Classifiers By Joint Statistical Testing at the Layers

Detecting anomalous inputs, such as adversarial and out-of-distribution ...
07/29/2020 ∙ by Jayaram Raghuram, et al. ∙ 16 ∙ share

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Abstract Universal Approximation for Neural Networks

With growing concerns about the safety and robustness of neural networks...
07/12/2020 ∙ by Zi Wang, et al. ∙ 9 ∙ share

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Robust Learning against Logical Adversaries

Test-time adversarial attacks have posed serious challenges to the robus...
07/01/2020 ∙ by Yizhen Wang, et al. ∙ 57 ∙ share

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Continuous Release of Data Streams under both Centralized and Local Differential Privacy

In this paper, we study the problem of publishing a stream of real-value...
05/24/2020 ∙ by Tianhao Wang, et al. ∙ 0 ∙ share

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Towards Effective Differential Privacy Communication for Users' Data Sharing Decision and Comprehension

Differential privacy protects an individual's privacy by perturbing data...
03/31/2020 ∙ by Aiping Xiong, et al. ∙ 0 ∙ share

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Obliviousness Makes Poisoning Adversaries Weaker

Poisoning attacks have emerged as a significant security threat to machi...
03/26/2020 ∙ by Sanjam Garg, et al. ∙ 5 ∙ share

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Face-Off: Adversarial Face Obfuscation

Advances in deep learning have made face recognition increasingly feasib...
03/19/2020 ∙ by Chuhan Gao, et al. ∙ 0 ∙ share

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Analyzing Accuracy Loss in Randomized Smoothing Defenses

Recent advances in machine learning (ML) algorithms, especially deep neu...
03/03/2020 ∙ by Yue Gao, et al. ∙ 15 ∙ share

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CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods

We study the problem of learning Granger causality between event types f...
02/18/2020 ∙ by Wei Zhang, et al. ∙ 10 ∙ share

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Query-Efficient Physical Hard-Label Attacks on Deep Learning Visual Classification

We present Survival-OPT, a physical adversarial example algorithm in the...
02/17/2020 ∙ by Ryan Feng, et al. ∙ 5 ∙ share

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Semantic Robustness of Models of Source Code

Deep neural networks are vulnerable to adversarial examples - small inpu...
02/07/2020 ∙ by Goutham Ramakrishnan, et al. ∙ 1 ∙ share

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Generating Semantic Adversarial Examples with Differentiable Rendering

Machine learning (ML) algorithms, especially deep neural networks, have ...
10/02/2019 ∙ by Lakshya Jain, et al. ∙ 23 ∙ share

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On Need for Topology Awareness of Generative Models

Manifold assumption in learning states that: the data lie approximately ...
09/07/2019 ∙ by Uyeong Jang, et al. ∙ 20 ∙ share

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On Need for Topology-Aware Generative Models for Manifold-Based Defenses

ML algorithms or models, especially deep neural networks (DNNs), have sh...
09/07/2019 ∙ by Uyeong Jang, et al. ∙ 0 ∙ share

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MURS: Practical and Robust Privacy Amplification with Multi-Party Differential Privacy

When collecting information, local differential privacy (LDP) alleviates...
08/30/2019 ∙ by Tianhao Wang, et al. ∙ 0 ∙ share

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Practical and Robust Privacy Amplification with Multi-Party Differential Privacy

When collecting information, local differential privacy (LDP) alleviates...
08/30/2019 ∙ by Tianhao Wang, et al. ∙ 0 ∙ share

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Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models

Directed graphical models (DGMs) are a class of probabilistic models tha...
05/30/2019 ∙ by Amrita Roy Chowdhury, et al. ∙ 1 ∙ share

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Adversarially Robust Learning Could Leverage Computational Hardness

Over recent years, devising classification algorithms that are robust to...
05/28/2019 ∙ by Sanjam Garg, et al. ∙ 4 ∙ share

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Enhancing ML Robustness Using Physical-World Constraints

Recent advances in Machine Learning (ML) have demonstrated that neural n...
05/26/2019 ∙ by Varun Chandrasekaran, et al. ∙ 0 ∙ share

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Robust Attribution Regularization

An emerging problem in trustworthy machine learning is to train models t...
05/23/2019 ∙ by Jiefeng Chen, et al. ∙ 4 ∙ share

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Attribution-driven Causal Analysis for Detection of Adversarial Examples

Attribution methods have been developed to explain the decision of a mac...
03/14/2019 ∙ by Susmit Jha, et al. ∙ 6 ∙ share

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Outis: Crypto-Assisted Differential Privacy on Untrusted Servers

Differential privacy has steadily become the de-facto standard for achie...
02/20/2019 ∙ by Amrita Roy Chowdhury, et al. ∙ 0 ∙ share

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Privacy-Preserving Collaborative Prediction using Random Forests

We study the problem of privacy-preserving machine learning (PPML) for e...
11/21/2018 ∙ by Irene Giacomelli, et al. ∙ 2 ∙ share

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Model Extraction and Active Learning

Machine learning is being increasingly used by individuals, research ins...
11/05/2018 ∙ by Varun Chandrasekaran, et al. ∙ 14 ∙ share

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Adversarial Learning and Explainability in Structured Datasets

We theoretically and empirically explore the explainability benefits of ...
10/15/2018 ∙ by Prasad Chalasani, et al. ∙ 0 ∙ share

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Explainable Black-Box Attacks Against Model-based Authentication

Establishing unique identities for both humans and end systems has been ...
09/28/2018 ∙ by Washington Garcia, et al. ∙ 0 ∙ share

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Adversarial Binaries for Authorship Identification

Binary code authorship identification determines authors of a binary pro...
09/21/2018 ∙ by Xiaozhu Meng, et al. ∙ 0 ∙ share

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Neural-Augmented Static Analysis of Android Communication

We address the problem of discovering communication links between applic...
09/11/2018 ∙ by Jinman Zhao, et al. ∙ 0 ∙ share

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Improving Adversarial Robustness by Data-Specific Discretization

A recent line of research proposed (either implicitly or explicitly) gra...
05/20/2018 ∙ by Jiefeng Chen, et al. ∙ 0 ∙ share

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Semantic Adversarial Deep Learning

Fueled by massive amounts of data, models produced by machine-learning (...
04/19/2018 ∙ by Tommaso Dreossi, et al. ∙ 0 ∙ share

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OEI: Operation Execution Integrity for Embedded Devices

We formulate a new security property, called "Operation Execution Integr...
02/09/2018 ∙ by Zhichuang Sun, et al. ∙ 0 ∙ share

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The Manifold Assumption and Defenses Against Adversarial Perturbations

In the adversarial-perturbation problem of neural networks, an adversary...
11/21/2017 ∙ by Xi Wu, et al. ∙ 0 ∙ share

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Manifold Assumption and Defenses Against Adversarial Perturbations

In the adversarial perturbation problem of neural networks, an adversary...
11/21/2017 ∙ by Xi Wu, et al. ∙ 0 ∙ share

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The Unintended Consequences of Overfitting: Training Data Inference Attacks

Machine learning algorithms that are applied to sensitive data pose a di...
09/05/2017 ∙ by Samuel Yeom, et al. ∙ 0 ∙ share

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Analyzing the Robustness of Nearest Neighbors to Adversarial Examples

Motivated by applications such as autonomous vehicles, test-time attacks...
06/13/2017 ∙ by Yizhen Wang, et al. ∙ 0 ∙ share

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ROSA: R Optimizations with Static Analysis

R is a popular language and programming environment for data scientists....
04/10/2017 ∙ by Rathijit Sen, et al. ∙ 0 ∙ share

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Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics

While significant progress has been made separately on analytics systems...
06/15/2016 ∙ by Xi Wu, et al. ∙ 0 ∙ share

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Practical Black-Box Attacks against Machine Learning

Machine learning (ML) models, e.g., deep neural networks (DNNs), are vul...
02/08/2016 ∙ by Nicolas Papernot, et al. ∙ 0 ∙ share

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The Limitations of Deep Learning in Adversarial Settings

Deep learning takes advantage of large datasets and computationally effi...
11/24/2015 ∙ by Nicolas Papernot, et al. ∙ 0 ∙ share

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Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks

Deep learning algorithms have been shown to perform extremely well on ma...
11/14/2015 ∙ by Nicolas Papernot, et al. ∙ 0 ∙ share

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Somesh Jha

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