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Found 19 papers, 10 papers with code
Multi-Instance Adversarial Attack on GNN-Based Malicious Domain Detection
In this work, we call the attack that manipulates several nodes in the DMG concurrently a multi-instance evasion attack.
FairDP: Certified Fairness with Differential Privacy
This paper introduces FairDP, a novel mechanism designed to achieve certified fairness with differential privacy (DP).
Zone-based Federated Learning for Mobile Sensing Data
However, currently there is no mobile sensing DL system that simultaneously achieves good model accuracy while adapting to user mobility behavior, scales well as the number of users increases, and protects user data privacy.
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.
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)
Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networks
Graph neural networks (GNNs) are susceptible to privacy inference attacks (PIAs), given their ability to learn joint representation from features and edges among nodes in graph data.
User-Entity Differential Privacy in Learning Natural Language Models
In this paper, we introduce a novel concept of user-entity differential privacy (UeDP) to provide formal privacy protection simultaneously to both sensitive entities in textual data and data owners in learning natural language models (NLMs).
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.
How to Backdoor HyperNetwork in Personalized Federated Learning?
This paper explores previously unknown backdoor risks in HyperNet-based personalized federated learning (HyperNetFL) through poisoning attacks.
FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps
This article presents the design, implementation, and evaluation of FLSys, a mobile-cloud federated learning (FL) system, which can be a key component for an open ecosystem of FL models and apps.
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.
A Synergetic Attack against Neural Network Classifiers combining Backdoor and Adversarial Examples
In this work, we show how to jointly exploit adversarial perturbation and model poisoning vulnerabilities to practically launch a new stealthy attack, dubbed AdvTrojan.
Ontology-based Interpretable Machine Learning for Textual Data
In this paper, we introduce a novel interpreting framework that learns an interpretable model based on an ontology-based sampling technique to explain agnostic prediction models.
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
Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning
In this paper, we focus on developing a novel mechanism to preserve differential privacy in deep neural networks, such that: (1) The privacy budget consumption is totally independent of the number of training steps; (2) It has the ability to adaptively inject noise into features based on the contribution of each to the output; and (3) It could be applied in a variety of different deep neural networks.
Preserving Differential Privacy in Convolutional Deep Belief Networks
However, only a few scientific studies on preserving privacy in deep learning have been conducted.
