Papers Privacy Preserving Deep Learning
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A Training Framework for Optimal and Stable Training of Polynomial Neural Networks
By replacing standard non-linearities with polynomial activations, Polynomial Neural Networks (PNNs) are pivotal for applications such as privacy-preserving inference via Homomorphic Encryption (HE). However, training PN…
Audio ClassificationHomomorphic Encryption for Deep LearningHuman Activity RecognitionImage Classification+2DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation
Differentially Private Stochastic Gradient Descent (DP-SGD) is a widely adopted technique for privacy-preserving deep learning. A critical challenge in DP-SGD is selecting the optimal clipping threshold C, which involves…
Deep Learningimage-classificationImage ClassificationPrivacy Preserving+1Split-n-Chain: Privacy-Preserving Multi-Node Split Learning with Blockchain-Based Auditability
Deep learning, when integrated with a large amount of training data, has the potential to outperform machine learning in terms of high accuracy. Recently, privacy-preserving deep learning has drawn significant attention …
Deep LearningFederated LearningPrivacy PreservingPrivacy Preserving Deep LearningJust a Simple Transformation is Enough for Data Protection in Vertical Federated Learning
Vertical Federated Learning (VFL) aims to enable collaborative training of deep learning models while maintaining privacy protection. However, the VFL procedure still has components that are vulnerable to attacks by mali…
Federated LearningPrivacy Preserving Deep LearningVertical Federated LearningPrivacy-Preserving Student Learning with Differentially Private Data-Free Distillation
Deep learning models can achieve high inference accuracy by extracting rich knowledge from massive well-annotated data, but may pose the risk of data privacy leakage in practical deployment. In this paper, we present an …
Privacy PreservingPrivacy Preserving Deep LearningDCT-CryptoNets: Scaling Private Inference in the Frequency Domain
The convergence of fully homomorphic encryption (FHE) and machine learning offers unprecedented opportunities for private inference of sensitive data. FHE enables computation directly on encrypted data, safeguarding the …
image-classificationImage ClassificationPrivacy PreservingPrivacy Preserving Deep LearningLow-Latency Privacy-Preserving Deep Learning Design via Secure MPC
Secure multi-party computation (MPC) facilitates privacy-preserving computation between multiple parties without leaking private information. While most secure deep learning techniques utilize MPC operations to achieve f…
Deep LearningPrivacy PreservingPrivacy Preserving Deep LearningEnhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data
Deep learning holds immense promise for aiding radiologists in breast cancer detection. However, achieving optimal model performance is hampered by limitations in availability and sharing of data commonly associated to p…
Breast Cancer DetectionCancer ClassificationData AugmentationDeep Learning+4Privacy-Preserving Deep Learning Using Deformable Operators for Secure Task Learning
In the era of cloud computing and data-driven applications, it is crucial to protect sensitive information to maintain data privacy, ensuring truly reliable systems. As a result, preserving privacy in deep learning syste…
Cloud ComputingPrivacy PreservingPrivacy Preserving Deep LearningConverting Transformers to Polynomial Form for Secure Inference Over Homomorphic Encryption
Designing privacy-preserving deep learning models is a major challenge within the deep learning community. Homomorphic Encryption (HE) has emerged as one of the most promising approaches in this realm, enabling the decou…
Formimage-classificationImage ClassificationPrivacy Preserving+1The Paradox of Noise: An Empirical Study of Noise-Infusion Mechanisms to Improve Generalization, Stability, and Privacy in Federated Learning
In a data-centric era, concerns regarding privacy and ethical data handling grow as machine learning relies more on personal information. This empirical study investigates the privacy, generalization, and stability of de…
Federated LearningPrivacy PreservingPrivacy Preserving Deep LearningMind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI Models
Developing robust artificial intelligence (AI) models that generalize well to unseen datasets is challenging and usually requires large and variable datasets, preferably from multiple institutions. In federated learning …
DiagnosticDiversityDomain GeneralizationFederated Learning+2Split Without a Leak: Reducing Privacy Leakage in Split Learning
The popularity of Deep Learning (DL) makes the privacy of sensitive data more imperative than ever. As a result, various privacy-preserving techniques have been implemented to preserve user data privacy in DL. Among vari…
Privacy PreservingPrivacy Preserving Deep LearningGenerative Model-Based Attack on Learnable Image Encryption for Privacy-Preserving Deep Learning
In this paper, we propose a novel generative model-based attack on learnable image encryption methods proposed for privacy-preserving deep learning. Various learnable encryption methods have been studied to protect the s…
Privacy PreservingPrivacy Preserving Deep LearningPrivate, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
Artificial intelligence (AI) models are increasingly used in the medical domain. However, as medical data is highly sensitive, special precautions to ensure its protection are required. The gold standard for privacy pres…
Computed Tomography (CT)DiagnosticFairnessImage Classification with Differential Privacy+3Training Differentially Private Graph Neural Networks with Random Walk Sampling
Deep learning models are known to put the privacy of their training data at risk, which poses challenges for their safe and ethical release to the public. Differentially private stochastic gradient descent is the de fact…
Privacy PreservingPrivacy Preserving Deep LearningMemorization of Named Entities in Fine-tuned BERT Models
Privacy preserving deep learning is an emerging field in machine learning that aims to mitigate the privacy risks in the use of deep neural networks. One such risk is training data extraction from language models that ha…
MemorizationPrivacy PreservingPrivacy Preserving Deep Learningtext-classification+2Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels
Due to the rapid advancements in recent years, medical image analysis is largely dominated by deep learning (DL). However, building powerful and robust DL models requires training with large multi-party datasets. While m…
Federated LearningMedical DiagnosisMedical Image AnalysisMulti-Label Classification+2Privacy in Practice: Private COVID-19 Detection in X-Ray Images (Extended Version)
Machine learning (ML) can help fight pandemics like COVID-19 by enabling rapid screening of large volumes of images. To perform data analysis while maintaining patient privacy, we create ML models that satisfy Differenti…
Knowledge DistillationMembership Inference AttackPrivacy Preserving Deep LearningPrivacy-preserving Deep Learning based Record Linkage
Deep learning-based linkage of records across different databases is becoming increasingly useful in data integration and mining applications to discover new insights from multiple sources of data. However, due to privac…
Data IntegrationDeep LearningPrivacy PreservingPrivacy Preserving Deep Learning