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Found 36 papers, 17 papers with code
Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance
Emerging Distributed AI systems are revolutionizing big data computing and data processing capabilities with growing economic and societal impact.
FinLLMs: A Framework for Financial Reasoning Dataset Generation with Large Language Models
To address the limited data resources and reduce the annotation cost, we introduce FinLLMs, a method for generating financial question-answering data based on common financial formulas using Large Language Models.
Imperio: Language-Guided Backdoor Attacks for Arbitrary Model Control
This paper proposes Imperio, which harnesses the language understanding capabilities of NLP models to enrich backdoor attacks.
Hierarchical Pruning of Deep Ensembles with Focal Diversity
This paper presents a novel deep ensemble pruning approach, which can efficiently identify smaller deep ensembles and provide higher ensemble accuracy than the entire deep ensemble of a large number of member networks.
Exploring Model Learning Heterogeneity for Boosting Ensemble Robustness
We show that this two-tier heterogeneity driven ensemble construction method can compose an ensemble team that promotes high ensemble diversity and low negative correlation among member models of the ensemble, strengthening ensemble robustness against both negative examples and adversarial attacks.
Invisible Watermarking for Audio Generation Diffusion Models
Diffusion models have gained prominence in the image domain for their capabilities in data generation and transformation, achieving state-of-the-art performance in various tasks in both image and audio domains.
Rethinking Learning Rate Tuning in the Era of Large Language Models
Second, we present LRBench++ to benchmark learning rate policies and facilitate learning rate tuning for both traditional DNNs and LLMs.
Explicit Time Embedding Based Cascade Attention Network for Information Popularity Prediction
Predicting information cascade popularity is a fundamental problem in social networks.
Few-shot Multi-domain Knowledge Rearming for Context-aware Defence against Advanced Persistent Threats
Secondly, to rearm current security strategies, an finetuning-based deployment mechanism is proposed to transfer learned knowledge into the student model, while minimizing the defense cost.
Securing Distributed SGD against Gradient Leakage Threats
Next, we present a gradient leakage resilient approach to securing distributed SGD in federated learning, with differential privacy controlled noise as the tool.
STDLens: Model Hijacking-Resilient Federated Learning for Object Detection
Based on the insights, we introduce a three-tier forensic framework to identify and expel Trojaned gradients and reclaim the performance over the course of FL.
GNN-Ensemble: Towards Random Decision Graph Neural Networks
In the meantime, we show that GNN-Ensemble can significantly improve the adversarial robustness against attacks on GNNs.
Machine Learning for Synthetic Data Generation: A Review
In light of these challenges, the concept of synthetic data generation emerges as a promising alternative that allows for data sharing and utilization in ways that real-world data cannot facilitate.
Adaptive Deep Neural Network Inference Optimization with EENet
Instead of having every sample go through all DNN layers during prediction, EENet learns an early exit scheduler, which can intelligently terminate the inference earlier for certain predictions, which the model has high confidence of early exit.
Gradient Leakage Attack Resilient Deep Learning
Although deep learning with differential privacy is a defacto standard for publishing deep learning models with differential privacy guarantee, we show that differentially private algorithms with fixed privacy parameters are vulnerable against gradient leakage attacks.
Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding
This survey paper reviews the design principles and the different node embedding techniques for network representation learning over homogeneous networks.
Speech2Video: Cross-Modal Distillation for Speech to Video Generation
This paper investigates a novel task of talking face video generation solely from speeches.
Gradient-Leakage Resilient Federated Learning
This paper presents a gradient leakage resilient approach to privacy-preserving federated learning with per training example-based client differential privacy, coined as Fed-CDP.
Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics
Our new metrics significantly improve the intrinsic correlation between high ensemble diversity and high ensemble accuracy.
Promoting High Diversity Ensemble Learning with EnsembleBench
Ensemble learning is gaining renewed interests in recent years.
Robust Deep Learning Ensemble against Deception
Third, XEnsemble provides a suite of algorithms to combine input verification and output verification to protect the DNN prediction models from both adversarial examples and out of distribution inputs.
A Real-time Robot-based Auxiliary System for Risk Evaluation of COVID-19 Infection
The structure of our model are maintained concise to be implemented for real-time applications.
Bitcoin Transaction Forecasting with Deep Network Representation Learning
First, we explore three interesting properties between Bitcoin transaction accounts: topological connectivity pattern of Bitcoin accounts, transaction amount pattern, and transaction dynamics.
Understanding Object Detection Through An Adversarial Lens
We demonstrate that the proposed framework can serve as a methodical benchmark for analyzing adversarial behaviors and risks in real-time object detection systems.
LDP-Fed: Federated Learning with Local Differential Privacy
However, in federated learning model parameter updates are collected iteratively from each participant and consist of high dimensional, continuous values with high precision (10s of digits after the decimal point), making existing LDP protocols inapplicable.
A Framework for Evaluating Gradient Leakage Attacks in Federated Learning
FL offers default client privacy by allowing clients to keep their sensitive data on local devices and to only share local training parameter updates with the federated server.
TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems
The rapid growth of real-time huge data capturing has pushed the deep learning and data analytic computing to the edge systems.
Effects of Differential Privacy and Data Skewness on Membership Inference Vulnerability
Second, through MPLens, we highlight how the vulnerability of pre-trained models under membership inference attack is not uniform across all classes, particularly when the training data itself is skewed.
Cross-Layer Strategic Ensemble Defense Against Adversarial Examples
Deep neural network (DNN) has demonstrated its success in multiple domains.
Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustness
In this paper we first give an overview of the concept of ensemble diversity and examine the three types of ensemble diversity in the context of DNN classifiers.
Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks
Deep neural networks (DNNs) have demonstrated impressive performance on many challenging machine learning tasks.
Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural Networks
Learning Rate (LR) is an important hyper-parameter to tune for effective training of deep neural networks (DNNs).
Secure and Utility-Aware Data Collection with Condensed Local Differential Privacy
In this paper, we address the small user population problem by introducing the concept of Condensed Local Differential Privacy (CLDP) as a specialization of LDP, and develop a suite of CLDP protocols that offer desirable statistical utility while preserving privacy.
Cryptography and Security Databases
A Comparative Measurement Study of Deep Learning as a Service Framework
Big data powered Deep Learning (DL) and its applications have blossomed in recent years, fueled by three technological trends: a large amount of digitized data openly accessible, a growing number of DL software frameworks in open source and commercial markets, and a selection of affordable parallel computing hardware devices.
Adversarial Examples in Deep Learning: Characterization and Divergence
The burgeoning success of deep learning has raised the security and privacy concerns as more and more tasks are accompanied with sensitive data.
Towards Demystifying Membership Inference Attacks
Our empirical results additionally show that (1) using the type of target model under attack within the attack model may not increase attack effectiveness and (2) collaborative learning in federated systems exposes vulnerabilities to membership inference risks when the adversary is a participant in the federation.
Cryptography and Security
