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Found 31 papers, 10 papers with code
Scalable Differential Privacy with Certified Robustness in Adversarial Learning
In this paper, we aim to develop a scalable algorithm to preserve differential privacy (DP) in adversarial learning for deep neural networks (DNNs), with certified robustness to adversarial examples.
Streaming k-Submodular Maximization under Noise subject to Size Constraint
Maximizing on k-submodular functions subject to size constraint has received extensive attention recently.
Analysis of Privacy Leakage in Federated Large Language Models
With the rapid adoption of Federated Learning (FL) as the training and tuning protocol for applications utilizing Large Language Models (LLMs), recent research highlights the need for significant modifications to FL to accommodate the large-scale of LLMs.
MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization
Multiplex influence maximization (MIM) asks us to identify a set of seed users such as to maximize the expected number of influenced users in a multiplex network.
OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning
Federated learning (FL) has emerged as a promising approach to collaboratively train machine learning models across multiple edge devices while preserving privacy.
OASIS: Offsetting Active Reconstruction Attacks in Federated Learning
We first uncover the core principle of gradient inversion that enables these attacks and theoretically identify the main conditions by which the defense can be robust regardless of the attack strategies.
On the Communication Complexity of Decentralized Bilevel Optimization
To the best of our knowledge, this is the first stochastic algorithm achieving these theoretical results under the heterogeneous setting.
When Decentralized Optimization Meets Federated Learning
Federated learning is a new learning paradigm for extracting knowledge from distributed data.
FairDP: Certified Fairness with Differential Privacy
This paper introduces FairDP, a novel mechanism designed to achieve certified fairness with differential privacy (DP).
Linear Query Approximation Algorithms for Non-monotone Submodular Maximization under Knapsack Constraint
This work, for the first time, introduces two constant factor approximation algorithms with linear query complexity for non-monotone submodular maximization over a ground set of size $n$ subject to a knapsack constraint, $\mathsf{DLA}$ and $\mathsf{RLA}$.
Cultural-aware Machine Learning based Analysis of COVID-19 Vaccine Hesitancy
Our findings show that Hispanic and African American are most likely impacted by cultural characteristics such as religions and ethnic affiliation, whereas the vaccine trust and approval influence the Asian communities the most.
QuTIE: Quantum optimization for Target Identification by Enzymes
Target Identification by Enzymes (TIE) problem aims to identify the set of enzymes in a given metabolic network, such that their inhibition eliminates a given set of target compounds associated with a disease while incurring minimum damage to the rest of the compounds.
Active Membership Inference Attack under Local Differential Privacy in Federated Learning
Federated learning (FL) was originally regarded as a framework for collaborative learning among clients with data privacy protection through a coordinating server.
LAVA: Granular Neuron-Level Explainable AI for Alzheimer's Disease Assessment from Fundus Images
Alzheimer's Disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia.
XRand: Differentially Private Defense against Explanation-Guided Attacks
Recent development in the field of explainable artificial intelligence (XAI) has helped improve trust in Machine-Learning-as-a-Service (MLaaS) systems, in which an explanation is provided together with the model prediction in response to each query.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
On the Limit of Explaining Black-box Temporal Graph Neural Networks
Temporal Graph Neural Network (TGNN) has been receiving a lot of attention recently due to its capability in modeling time-evolving graph-related tasks.
EMaP: Explainable AI with Manifold-based Perturbations
Our study focuses on the impact of perturbing directions on the data topology.
NeuCEPT: Locally Discover Neural Networks' Mechanism via Critical Neurons Identification with Precision Guarantee
In this work, we propose NeuCEPT, a method to locally discover critical neurons that play a major role in the model's predictions and identify model's mechanisms in generating those predictions.
An Explainer for Temporal Graph Neural Networks
Given a time series on a graph to be explained, the framework can identify dominant explanations in the form of a probabilistic graphical model in a time period.
Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning
In this paper, we show that the process of continually learning new tasks and memorizing previous tasks introduces unknown privacy risks and challenges to bound the privacy loss.
On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network
Bilevel optimization has been applied to a wide variety of machine learning models, and numerous stochastic bilevel optimization algorithms have been developed in recent years.
Blockchain-based Secure Client Selection in Federated Learning
However, we show that, by manipulating the client selection process, the server can circumvent the secure aggregation to learn the local models of a victim client, indicating that secure aggregation alone is inadequate for privacy protection.
Preserving Privacy and Security in Federated Learning
With a new threat model that includes both an honest-but-curious server and malicious users, we first propose a secure aggregation protocol using homomorphic encryption for the server to combine local model updates in a private manner.
Learning Interpretation with Explainable Knowledge Distillation
Knowledge Distillation (KD) has been considered as a key solution in model compression and acceleration in recent years.
Continual Learning with Differential Privacy
In this paper, we focus on preserving differential privacy (DP) in continual learning (CL), in which we train ML models to learn a sequence of new tasks while memorizing previous tasks.
PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks
In Graph Neural Networks (GNNs), the graph structure is incorporated into the learning of node representations.
Differential Privacy in Adversarial Learning with Provable Robustness
In this paper, we aim to develop a novel mechanism to preserve differential privacy (DP) in adversarial learning for deep neural networks, with provable robustness to adversarial examples.
c-Eval: A Unified Metric to Evaluate Feature-based Explanations via Perturbation
Given a classifier's prediction and the corresponding explanation on that prediction, c-Eval is the minimum-distortion perturbation that successfully alters the prediction while keeping the explanation's features unchanged.
Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness
In this paper, we propose a novel Heterogeneous Gaussian Mechanism (HGM) to preserve differential privacy in deep neural networks, with provable robustness against adversarial examples.
Preserving Differential Privacy in Adversarial Learning with Provable Robustness
In this paper, we aim to develop a novel mechanism to preserve differential privacy (DP) in adversarial learning for deep neural networks, with provable robustness to adversarial examples.
Cryptography and Security
Cost-aware Targeted Viral Marketing in billion-scale networks
In this paper, we propose a new problem, called Cost-aware Targeted Viral Marketing (CTVM), to find the most cost-effective seed users who can influence the most relevant users to the advertisement.
