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Found 9 papers, 5 papers with code
Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform
Through this gateway, users have the opportunity to submit their questions, testing the models on a personalized and potentially broader range of capabilities.
Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture
The goal of this work is to train ML models that have high membership privacy while largely preserving their utility; we therefore aim for an empirical membership privacy guarantee as opposed to the provable privacy guarantees provided by techniques like differential privacy, as such techniques are shown to deteriorate model utility.
A Critical Evaluation of Open-World Machine Learning
With our evaluation across 6 OOD detectors, we find that the choice of in-distribution data, model architecture and OOD data have a strong impact on OOD detection performance, inducing false positive rates in excess of $70\%$.
BIG-bench Machine Learning
Out of Distribution (OOD) Detection
Universal Adversarial Attacks with Natural Triggers for Text Classification
Recent work has demonstrated the vulnerability of modern text classifiers to universal adversarial attacks, which are input-agnostic sequences of words added to text processed by classifiers.
Systematic Evaluation of Privacy Risks of Machine Learning Models
Machine learning models are prone to memorizing sensitive data, making them vulnerable to membership inference attacks in which an adversary aims to guess if an input sample was used to train the model.
Towards Probabilistic Verification of Machine Unlearning
In this work, we take the first step in proposing a formal framework to study the design of such verification mechanisms for data deletion requests -- also known as machine unlearning -- in the context of systems that provide machine learning as a service (MLaaS).
Privacy Risks of Securing Machine Learning Models against Adversarial Examples
To perform the membership inference attacks, we leverage the existing inference methods that exploit model predictions.
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples
A large body of recent work has investigated the phenomenon of evasion attacks using adversarial examples for deep learning systems, where the addition of norm-bounded perturbations to the test inputs leads to incorrect output classification.
Inaudible Voice Commands
Voice assistants like Siri enable us to control IoT devices conveniently with voice commands, however, they also provide new attack opportunities for adversaries.
Cryptography and Security
