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Found 25 papers, 7 papers with code
Differentially Private Instance Encoding against Privacy Attacks
TextHide was recently proposed to protect the training data via instance encoding in natural language domain.
Reconstruction Attack on Instance Encoding for Language Understanding
A private learning scheme TextHide was recently proposed to protect the private text data during the training phase via so-called instance encoding.
GALOT: Generative Active Learning via Optimizable Zero-shot Text-to-image Generation
Active Learning (AL) represents a crucial methodology within machine learning, emphasizing the identification and utilization of the most informative samples for efficient model training.
Learning Robust and Privacy-Preserving Representations via Information Theory
Machine learning models are vulnerable to both security attacks (e. g., adversarial examples) and privacy attacks (e. g., private attribute inference).
Understanding Data Reconstruction Leakage in Federated Learning from a Theoretical Perspective
Federated learning (FL) is an emerging collaborative learning paradigm that aims to protect data privacy.
Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence
DP-SGD) to significantly boost accuracy and convergence.
An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detection
Large Language Models (LLMs) have transformed code completion tasks, providing context-based suggestions to boost developer productivity in software engineering.
LMO-DP: Optimizing the Randomization Mechanism for Differentially Private Fine-Tuning (Large) Language Models
To address such limitations, we propose a novel Language Model-based Optimal Differential Privacy (LMO-DP) mechanism, which takes the first step to enable the tight composition of accurately fine-tuning (large) language models with a sub-optimal DP mechanism, even in strong privacy regimes (e. g., $0. 1\leq \epsilon<3$).
Certifying Adapters: Enabling and Enhancing the Certification of Classifier Adversarial Robustness
Randomized smoothing has become a leading method for achieving certified robustness in deep classifiers against l_{p}-norm adversarial perturbations.
Differentially Private Federated Learning: A Systematic Review
Our work presents a systematic overview of the differentially private federated learning.
On the Faithfulness of Vision Transformer Explanations
In contrast, our proposed SaCo offers a reliable faithfulness measurement, establishing a robust metric for interpretations.
Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks
Machine learning (ML) is vulnerable to inference (e. g., membership inference, property inference, and data reconstruction) attacks that aim to infer the private information of training data or dataset.
FLTracer: Accurate Poisoning Attack Provenance in Federated Learning
In this paper, we first conduct a comprehensive study on prior FL attacks and detection methods.
Text-CRS: A Generalized Certified Robustness Framework against Textual Adversarial Attacks
The language models, especially the basic text classification models, have been shown to be susceptible to textual adversarial attacks such as synonym substitution and word insertion attacks.
Certifiable Black-Box Attacks with Randomized Adversarial Examples: Breaking Defenses with Provable Confidence
Specifically, we establish a novel theoretical foundation for ensuring the ASP of the black-box attack with randomized adversarial examples (AEs).
OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization
Although the solution based on Local Differential Privacy (LDP) addresses the above problems, it leads to the low accuracy of the trained model.
When Fairness Meets Privacy: Fair Classification with Semi-Private Sensitive Attributes
Thus, it is essential to ensure fairness in machine learning models.
Certified Adversarial Robustness via Anisotropic Randomized Smoothing
However, all of the existing methods rely on fixed i. i. d.
UniCR: Universally Approximated Certified Robustness via Randomized Smoothing
We study certified robustness of machine learning classifiers against adversarial perturbations.
DPOAD: Differentially Private Outsourcing of Anomaly Detection through Iterative Sensitivity Learning
Outsourcing anomaly detection to third-parties can allow data owners to overcome resource constraints (e. g., in lightweight IoT devices), facilitate collaborative analysis (e. g., under distributed or multi-party scenarios), and benefit from lower costs and specialized expertise (e. g., of Managed Security Service Providers).
Infrastructure-enabled GPS Spoofing Detection and Correction
Accurate and robust localization is crucial for supporting high-level driving automation and safety.
An Eye for an Eye: Defending against Gradient-based Attacks with Gradients
In this paper, we show that the gradients can also be exploited as a powerful weapon to defend against adversarial attacks.
Deep learning for prediction of hepatocellular carcinoma recurrence after resection or liver transplantation: a discovery and validation study
LT set contained patients with HCC treated by LT.
VideoDP: A Universal Platform for Video Analytics with Differential Privacy
In this paper, to the best of our knowledge, we propose the first differentially private video analytics platform (VideoDP) which flexibly supports different video analyses with rigorous privacy guarantee.
WIDER Face and Pedestrian Challenge 2018: Methods and Results
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian.
