Papers Privacy Preserving Deep Learning
“Privacy Preserving Deep Learning” 태그가 달린 논문 59편 · 필터 해제
Tempered Sigmoid Activations for Deep Learning with Differential Privacy
Because learning sometimes involves sensitive data, machine learning algorithms have been extended to offer privacy for training data. In practice, this has been mostly an afterthought, with privacy-preserving models obt…
Deep LearningPrivacy PreservingPrivacy Preserving Deep LearningHow to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning
This paper firstly considers the research problem of fairness in collaborative deep learning, while ensuring privacy. A novel reputation system is proposed through digital tokens and local credibility to ensure fairness,…
Deep LearningFairnessGenerative Adversarial NetworkPrivacy Preserving+1MPC Protocol for G-module and its Application in Secure Compare and ReLU
Secure comparison and secure selection are two fundamental MPC (secure Multi-Party Computation) protocols. One important application of these protocols is the secure ReLU and DReLU computation in privacy preserving deep …
Privacy PreservingPrivacy Preserving Deep LearningSecurity and Privacy Preserving Deep Learning
Commercial companies that collect user data on a large scale have been the main beneficiaries of this trend since the success of deep learning techniques is directly proportional to the amount of data available for train…
BIG-bench Machine LearningDeep LearningFederated LearningPrivacy Preserving+1Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks
This paper investigates capabilities of Privacy-Preserving Deep Learning (PPDL) mechanisms against various forms of privacy attacks. First, we propose to quantitatively measure the trade-off between model accuracy and pr…
Privacy PreservingPrivacy Preserving Deep LearningLocally Private Graph Neural Networks
Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent pe…
Federated LearningNode ClassificationPrivacy PreservingPrivacy Preserving Deep LearningARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing
We propose AriaNN, a low-interaction privacy-preserving framework for private neural network training and inference on sensitive data. Our semi-honest 2-party computation protocol (with a trusted dealer) leverages functi…
CPUFederated LearningGPUPrivacy Preserving+1Locally Differentially Private (Contextual) Bandits Learning
We study locally differentially private (LDP) bandits learning in this paper. First, we propose simple black-box reduction frameworks that can solve a large family of context-free bandits learning problems with LDP guara…
Multi-Armed BanditsPrivacy Preserving Deep LearningFawkes: Protecting Privacy against Unauthorized Deep Learning Models
Today's proliferation of powerful facial recognition systems poses a real threat to personal privacy. As Clearview.ai demonstrated, anyone can canvas the Internet for data and train highly accurate facial recognition mod…
Deep LearningFace RecognitionPrivacy Preserving Deep LearningPrivacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms
This paper proposes a distributed deep learning framework for privacy-preserving medical data training. In order to avoid patients' data leakage in medical platforms, the hidden layers in the deep learning framework are …
Deep LearningPrivacy PreservingPrivacy Preserving Deep LearningLearning to Prevent Leakage: Privacy-Preserving Inference in the Mobile Cloud
Powered by machine learning services in the cloud, numerous learning-driven mobile applications are gaining popularity in the market. As deep learning tasks are mostly computation-intensive, it has become a trend to proc…
Neural Architecture SearchPrivacy PreservingPrivacy Preserving Deep LearningA Novel Privacy-Preserving Deep Learning Scheme without Using Cryptography Component
Recently, deep learning, which uses Deep Neural Networks (DNN), plays an important role in many fields. A secure neural network model with a secure training/inference scheme is indispensable to many applications. To acco…
Privacy PreservingPrivacy Preserving Deep LearningPrivacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset
We investigate privacy-preserving, video-based action recognition in deep learning, a problem with growing importance in smart camera applications. A novel adversarial training framework is formulated to learn an anonymi…
Action RecognitionPrivacy PreservingPrivacy Preserving Deep LearningPrivate Deep Learning with Teacher Ensembles
Privacy-preserving deep learning is crucial for deploying deep neural network based solutions, especially when the model works on data that contains sensitive information. Most privacy-preserving methods lead to undesira…
Deep LearningEnsemble LearningKnowledge DistillationPrivacy Preserving+1Towards Fair and Privacy-Preserving Federated Deep Models
The current standalone deep learning framework tends to result in overfitting and low utility. This problem can be addressed by either a centralized framework that deploys a central server to train a global model on the …
BenchmarkingDeep LearningFairnessFederated Learning+2Distributed Layer-Partitioned Training for Privacy-Preserved Deep Learning
Deep Learning techniques have achieved remarkable results in many domains. Often, training deep learning models requires large datasets, which may require sensitive information to be uploaded to the cloud to accelerate t…
Deep LearningPrivacy PreservingPrivacy Preserving Deep LearningDisguised-Nets: Image Disguising for Privacy-preserving Outsourced Deep Learning
Deep learning model developers often use cloud GPU resources to experiment with large data and models that need expensive setups. However, this practice raises privacy concerns. Adversaries may be interested in: 1) perso…
Deep LearningGPUimage-classificationImage Classification+2A generic framework for privacy preserving deep learning
We detail a new framework for privacy preserving deep learning and discuss its assets. The framework puts a premium on ownership and secure processing of data and introduces a valuable representation based on chains of c…
Deep LearningFederated LearningPrivacy PreservingPrivacy Preserving Deep LearningPrivacy-Preserving Deep Learning via Weight Transmission
This paper considers the scenario that multiple data owners wish to apply a machine learning method over the combined dataset of all owners to obtain the best possible learning output but do not want to share the local d…
BIG-bench Machine LearningDeep LearningPrivacy PreservingPrivacy Preserving Deep Learning