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08/28/2023
Identifying and Mitigating the Security Risks of Generative AI
Every major technical invention resurfaces the dual-use dilemma – the ne...
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07/30/2023
Theoretically Principled Trade-off for Stateful Defenses against Query-Based Black-Box Attacks
Adversarial examples threaten the integrity of machine learning systems ...
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07/03/2023
Pareto-Secure Machine Learning (PSML): Fingerprinting and Securing Inference Serving Systems
With the emergence of large foundational models, model-serving systems a...
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05/25/2023
Rethink Diversity in Deep Learning Testing
Deep neural networks (DNNs) have demonstrated extraordinary capabilities...
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04/07/2023
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction
Selective prediction aims to learn a reliable model that abstains from m...
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03/11/2023
Investigating Stateful Defenses Against Black-Box Adversarial Examples
Defending machine-learning (ML) models against white-box adversarial att...
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02/27/2023
Formal Analysis of the API Proxy Problem
Implementing a security mechanism on top of APIs requires clear understa...
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01/26/2023
Learning Modulo Theories
Recent techniques that integrate solver layers into Deep Neural Networks...
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11/23/2022
Private Multi-Winner Voting for Machine Learning
Private multi-winner voting is the task of revealing k-hot binary vector...
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10/06/2022
Federated Boosted Decision Trees with Differential Privacy
There is great demand for scalable, secure, and efficient privacy-preser...
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05/18/2022
Constraining the Attack Space of Machine Learning Models with Distribution Clamping Preprocessing
Preprocessing and outlier detection techniques have both been applied to...
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03/04/2022
Concept-based Explanations for Out-Of-Distribution Detectors
Out-of-distribution (OOD) detection plays a crucial role in ensuring the...
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03/02/2022
A Quantitative Geometric Approach to Neural Network Smoothness
Fast and precise Lipschitz constant estimation of neural networks is an ...
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02/21/2022
Using Illustrations to Communicate Differential Privacy Trust Models: An Investigation of Users' Comprehension, Perception, and Data Sharing Decision
Proper communication is key to the adoption and implementation of differ...
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02/11/2022
Towards Adversarially Robust Deepfake Detection: An Ensemble Approach
Detecting deepfakes is an important problem, but recent work has shown t...
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02/09/2022
An Exploration of Multicalibration Uniform Convergence Bounds
Recent works have investigated the sample complexity necessary for fair ...
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12/23/2021
EIFFeL: Ensuring Integrity for Federated Learning
Federated learning (FL) enables clients to collaborate with a server to ...
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09/06/2021
Lightweight, Multi-Stage, Compiler-Assisted Application Specialization
Program debloating aims to enhance the performance and reduce the attack...
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08/16/2021
NeuraCrypt is not private
NeuraCrypt (Yara et al. arXiv 2021) is an algorithm that converts a sens...
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08/05/2021
Fairness Properties of Face Recognition and Obfuscation Systems
The proliferation of automated facial recognition in various commercial ...
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08/02/2021
Domain Adaptation for Autoencoder-Based End-to-End Communication Over Wireless Channels
The problem of domain adaptation conventionally considers the setting wh...
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06/11/2021
A Shuffling Framework for Local Differential Privacy
ldp deployments are vulnerable to inference attacks as an adversary can ...
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03/04/2021
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...
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02/14/2021
Exploring Adversarial Robustness of Deep Metric Learning
Deep Metric Learning (DML), a widely-used technique, involves learning a...
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02/09/2021
CaPC Learning: Confidential and Private Collaborative Learning
Machine learning benefits from large training datasets, which may not al...
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12/19/2020
Sample Complexity of Adversarially Robust Linear Classification on Separated Data
We consider the sample complexity of learning with adversarial robustnes...
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11/11/2020
ShadowNet: A Secure and Efficient System for On-device Model Inference
On-device machine learning (ML) is getting more and more popular as fast...
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09/11/2020
Intertwining Order Preserving Encryption and Differential Privacy
Ciphertexts of an order-preserving encryption (OPE) scheme preserve the ...
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07/29/2020
Detecting Anomalous Inputs to DNN Classifiers By Joint Statistical Testing at the Layers
Detecting anomalous inputs, such as adversarial and out-of-distribution ...
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07/12/2020
Abstract Universal Approximation for Neural Networks
With growing concerns about the safety and robustness of neural networks...
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07/01/2020
Robust Learning against Logical Adversaries
Test-time adversarial attacks have posed serious challenges to the robus...
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05/24/2020
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...
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03/31/2020
Towards Effective Differential Privacy Communication for Users' Data Sharing Decision and Comprehension
Differential privacy protects an individual's privacy by perturbing data...
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03/26/2020
Obliviousness Makes Poisoning Adversaries Weaker
Poisoning attacks have emerged as a significant security threat to machi...
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03/19/2020
Face-Off: Adversarial Face Obfuscation
Advances in deep learning have made face recognition increasingly feasib...
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03/03/2020
Analyzing Accuracy Loss in Randomized Smoothing Defenses
Recent advances in machine learning (ML) algorithms, especially deep neu...
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02/18/2020
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods
We study the problem of learning Granger causality between event types f...
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02/17/2020
Query-Efficient Physical Hard-Label Attacks on Deep Learning Visual Classification
We present Survival-OPT, a physical adversarial example algorithm in the...
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02/07/2020
Semantic Robustness of Models of Source Code
Deep neural networks are vulnerable to adversarial examples - small inpu...
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10/02/2019
Generating Semantic Adversarial Examples with Differentiable Rendering
Machine learning (ML) algorithms, especially deep neural networks, have ...
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09/07/2019
On Need for Topology Awareness of Generative Models
Manifold assumption in learning states that: the data lie approximately ...
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09/07/2019
On Need for Topology-Aware Generative Models for Manifold-Based Defenses
ML algorithms or models, especially deep neural networks (DNNs), have sh...
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08/30/2019
MURS: Practical and Robust Privacy Amplification with Multi-Party Differential Privacy
When collecting information, local differential privacy (LDP) alleviates...
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08/30/2019
Practical and Robust Privacy Amplification with Multi-Party Differential Privacy
When collecting information, local differential privacy (LDP) alleviates...
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05/30/2019
Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models
Directed graphical models (DGMs) are a class of probabilistic models tha...
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05/28/2019
Adversarially Robust Learning Could Leverage Computational Hardness
Over recent years, devising classification algorithms that are robust to...
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05/26/2019
Enhancing ML Robustness Using Physical-World Constraints
Recent advances in Machine Learning (ML) have demonstrated that neural n...
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05/23/2019
Robust Attribution Regularization
An emerging problem in trustworthy machine learning is to train models t...
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03/14/2019
Attribution-driven Causal Analysis for Detection of Adversarial Examples
Attribution methods have been developed to explain the decision of a mac...
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02/20/2019