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Found 14 papers, 4 papers with code
PhaseEvo: Towards Unified In-Context Prompt Optimization for Large Language Models
Crafting an ideal prompt for Large Language Models (LLMs) is a challenging task that demands significant resources and expert human input.
Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?
In practical applications, such a worst-case guarantee may be overkill: practical attackers may lack exact knowledge of (nearly all of) the private data, and our data set might be easier to defend, in some sense, than the worst-case data set.
Transferable Learned Image Compression-Resistant Adversarial Perturbations
Adversarial attacks can readily disrupt the image classification system, revealing the vulnerability of DNN-based recognition tasks.
DCR-Consistency: Divide-Conquer-Reasoning for Consistency Evaluation and Improvement of Large Language Models
Evaluating the quality and variability of text generated by Large Language Models (LLMs) poses a significant, yet unresolved research challenge.
SAC3: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency
Hallucination detection is a critical step toward understanding the trustworthiness of modern language models (LMs).
Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics
Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks.
Reconstruction Distortion of Learned Image Compression with Imperceptible Perturbations
Learned Image Compression (LIC) has recently become the trending technique for image transmission due to its notable performance.
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense
In this paper, we seek to reconcile utility and privacy in FL by proposing a user-configurable privacy defense, RecUP-FL, that can better focus on the user-specified sensitive attributes while obtaining significant improvements in utility over traditional defenses.
Speech Privacy Leakage from Shared Gradients in Distributed Learning
Distributed machine learning paradigms, such as federated learning, have been recently adopted in many privacy-critical applications for speech analysis.
RIBAC: Towards Robust and Imperceptible Backdoor Attack against Compact DNN
Recently backdoor attack has become an emerging threat to the security of deep neural network (DNN) models.
Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage
Federated Learning (FL) framework brings privacy benefits to distributed learning systems by allowing multiple clients to participate in a learning task under the coordination of a central server without exchanging their private data.
Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates
Federated Learning (FL) enables multiple distributed clients (e. g., mobile devices) to collaboratively train a centralized model while keeping the training data locally on the client.
Enabling Fast and Universal Audio Adversarial Attack Using Generative Model
These idealized assumptions, however, makes the existing audio adversarial attacks mostly impossible to be launched in a timely fashion in practice (e. g., playing unnoticeable adversarial perturbations along with user's streaming input).
Real-time, Universal, and Robust Adversarial Attacks Against Speaker Recognition Systems
As the popularity of voice user interface (VUI) exploded in recent years, speaker recognition system has emerged as an important medium of identifying a speaker in many security-required applications and services.
