Papers Privacy Preserving Deep Learning
“Privacy Preserving Deep Learning” 태그가 달린 논문 59편 · 필터 해제
Review Learning: Alleviating Catastrophic Forgetting with Generative Replay without Generator
When a deep learning model is sequentially trained on different datasets, it forgets the knowledge acquired from previous data, a phenomenon known as catastrophic forgetting. It deteriorates performance of the deep learn…
Binary ClassificationContinual LearningDeep LearningPrivacy Preserving+2Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection
Recent studies demonstrated that X-ray radiography showed higher accuracy than Polymerase Chain Reaction (PCR) testing for COVID-19 detection. Therefore, applying deep learning models to X-rays and radiography images inc…
Deep LearningPrivacy PreservingPrivacy Preserving Deep LearningBottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utility
Bottleneck problems are an important class of optimization problems that have recently gained increasing attention in the domain of machine learning and information theory. They are widely used in generative models, fair…
Face RecognitionFairnessPrivacy Preserving Deep LearningRepresentation Learning+1Securing the Classification of COVID-19 in Chest X-ray Images: A Privacy-Preserving Deep Learning Approach
Deep learning (DL) is being increasingly utilized in healthcare-related fields due to its outstanding efficiency. However, we have to keep the individual health data used by DL models private and secure. Protecting data …
Privacy PreservingPrivacy Preserving Deep LearningCommunication-Efficient Federated Distillation with Active Data Sampling
Federated learning (FL) is a promising paradigm to enable privacy-preserving deep learning from distributed data. Most previous works are based on federated average (FedAvg), which, however, faces several critical issues…
Federated LearningPrivacy PreservingPrivacy Preserving Deep LearningBackpropagation Clipping for Deep Learning with Differential Privacy
We present backpropagation clipping, a novel variant of differentially private stochastic gradient descent (DP-SGD) for privacy-preserving deep learning. Our approach clips each trainable layer's inputs (during the forwa…
Deep LearningPrivacy PreservingPrivacy Preserving Deep LearningSensitivityDP-FP: Differentially Private Forward Propagation for Large Models
When applied to large-scale learning problems, the conventional wisdom on privacy-preserving deep learning, known as Differential Private Stochastic Gradient Descent (DP-SGD), has met with limited success due to signific…
Privacy PreservingPrivacy Preserving Deep LearningSoK: Privacy-preserving Deep Learning with Homomorphic Encryption
Outsourced computation for neural networks allows users access to state of the art models without needing to invest in specialized hardware and know-how. The problem is that the users lose control over potentially privac…
Deep LearningPrivacy PreservingPrivacy Preserving Deep LearningHomogeneous Learning: Self-Attention Decentralized Deep Learning
Federated learning (FL) has been facilitating privacy-preserving deep learning in many walks of life such as medical image classification, network intrusion detection, and so forth. Whereas it necessitates a central para…
Deep LearningFederated Learningimage-classificationImage Classification+5Towards Secure and Practical Machine Learning via Secret Sharing and Random Permutation
With the increasing demands for privacy protection, privacy-preserving machine learning has been drawing much attention in both academia and industry. However, most existing methods have their limitations in practical ap…
BIG-bench Machine LearningPrivacy PreservingPrivacy Preserving Deep LearningSisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning
Privacy concerns in client-server machine learning have given rise to private inference (PI), where neural inference occurs directly on encrypted inputs. PI protects clients' personal data and the server's intellectual p…
Privacy PreservingPrivacy Preserving Deep LearningTowards a Privacy-preserving Deep Learning-based Network Intrusion Detection in Data Distribution Services
Data Distribution Service (DDS) is an innovative approach towards communication in ICS/IoT infrastructure and robotics. Being based on the cross-platform and cross-language API to be applicable in any computerised device…
Deep LearningIntrusion DetectionNetwork Intrusion DetectionPrivacy Preserving+1Antipodes of Label Differential Privacy: PATE and ALIBI
We consider the privacy-preserving machine learning (ML) setting where the trained model must satisfy differential privacy (DP) with respect to the labels of the training examples. We propose two novel approaches based o…
Bayesian InferenceMemorizationPrivacy PreservingPrivacy Preserving Deep LearningVariational Leakage: The Role of Information Complexity in Privacy Leakage
We study the role of information complexity in privacy leakage about an attribute of an adversary's interest, which is not known a priori to the system designer. Considering the supervised representation learning setup a…
AttributeFace RecognitionPrivacy Preserving Deep LearningRepresentation Learning+1CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU
We introduce CryptGPU, a system for privacy-preserving machine learning that implements all operations on the GPU (graphics processing unit). Just as GPUs played a pivotal role in the success of modern deep learning, the…
BIG-bench Machine LearningCPUGPUPrivacy Preserving+1Practical Privacy Filters and Odometers with Rényi Differential Privacy and Applications to Differentially Private Deep Learning
Differential Privacy (DP) is the leading approach to privacy preserving deep learning. As such, there are multiple efforts to provide drop-in integration of DP into popular frameworks. These efforts, which add noise to e…
Privacy PreservingPrivacy Preserving Deep LearningOriole: Thwarting Privacy against Trustworthy Deep Learning Models
Deep Neural Networks have achieved unprecedented success in the field of face recognition such that any individual can crawl the data of others from the Internet without their explicit permission for the purpose of train…
Data PoisoningDeep LearningFace RecognitionPrivacy Preserving+1Can we Generalize and Distribute Private Representation Learning?
We study the problem of learning representations that are private yet informative, i.e., provide information about intended "ally" targets while hiding sensitive "adversary" attributes. We propose Exclusion-Inclusion Gen…
Federated LearningGenerative Adversarial NetworkPrivacy Preserving Deep LearningRepresentation LearningSecure Data Sharing With Flow Model
In the classical multi-party computation setting, multiple parties jointly compute a function without revealing their own input data. We consider a variant of this problem, where the input data can be shared for machine …
BIG-bench Machine LearningImage ClassificationmodelPrivacy Preserving Deep LearningGuardNN: Secure Accelerator Architecture for Privacy-Preserving Deep Learning
This paper proposes GuardNN, a secure DNN accelerator that provides hardware-based protection for user data and model parameters even in an untrusted environment. GuardNN shows that the architecture and protection can be…
Deep LearningPrivacy PreservingPrivacy Preserving Deep Learning