Privacy Preserving Deep Learning

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

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Datasets archive 2025-07-28

3 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

2 subtasks in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 30 papers with code (59 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset
  12 Jun 2019 5 repositories listed
  We first discuss an innovative heuristic of cross-dataset training and evaluation, enabling the use of multiple single-task datasets (one with target task labels and the other with privacy labels) in our problem.
• Locally Differentially Private (Contextual) Bandits Learning
  1 Jun 2020 3 repositories listed
  We study locally differentially private (LDP) bandits learning in this paper.
• A generic framework for privacy preserving deep learning
  9 Nov 2018 3 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  We detail a new framework for privacy preserving deep learning and discuss its assets.
• Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
  3 Feb 2023 2 repositories listed
  In this work, we evaluated the effect of privacy-preserving training of AI models regarding accuracy and fairness compared to non-private training.
• Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning
  26 Jul 2021 2 repositories listed
  In this work, we ask: Is it feasible to substitute all ReLUs with low-degree polynomial activation functions for building deep, privacy-friendly neural networks?
• ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing
  8 Jun 2020 2 repositories listed
  We evaluate our end-to-end system for private inference between distant servers on standard neural networks such as AlexNet, VGG16 or ResNet18, and for private training on smaller networks like LeNet.
• A Training Framework for Optimal and Stable Training of Polynomial Neural Networks
  16 May 2025 1 repository listed
  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).
• Just a Simple Transformation is Enough for Data Protection in Vertical Federated Learning
  16 Dec 2024 1 repository listed
  Vertical Federated Learning (VFL) aims to enable collaborative training of deep learning models while maintaining privacy protection.
• DCT-CryptoNets: Scaling Private Inference in the Frequency Domain
  27 Aug 2024 1 repository listed Syntology ran 0 of 2 samples · 2 unverified
  DCT-CryptoNets also demonstrates superior scalability to RGB-based networks by further reducing computational cost as image size increases.
• Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data
  17 Jul 2024 1 repository listed
  This work addresses these challenges exploring and quantifying the utility of privacy-preserving deep learning techniques, concretely, (i) differentially private stochastic gradient descent (DP-SGD) and (ii) fully…
• Privacy-Preserving Deep Learning Using Deformable Operators for Secure Task Learning
  8 Apr 2024 1 repository listed
  To address these challenges, we propose a novel Privacy-Preserving framework that uses a set of deformable operators for secure task learning.
• Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI Models
  1 Oct 2023 1 repository listed
  So far, the impact of training strategy, i.
• Split Without a Leak: Reducing Privacy Leakage in Split Learning
  30 Aug 2023 1 repository listed
  The idea behind it is that the client encrypts the activation map (the output of the split layer between the client and the server) before sending it to the server.
• Memorization of Named Entities in Fine-tuned BERT Models
  7 Dec 2022 1 repository listed
  One such risk is training data extraction from language models that have been trained on datasets, which contain personal and privacy sensitive information.
• Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels
  24 Nov 2022 1 repository listed
  Due to the rapid advancements in recent years, medical image analysis is largely dominated by deep learning (DL).
• Privacy in Practice: Private COVID-19 Detection in X-Ray Images (Extended Version)
  21 Nov 2022 1 repository listed
  The introduced DP should help limit leakage threats posed by MIAs, and our practical analysis is the first to test this hypothesis on the COVID-19 classification task.
• Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utility
  11 Jul 2022 1 repository listed Syntology ran 0 of 7 samples · 7 unverified
  In this work, we propose a general family of optimization problems, termed as complexity-leakage-utility bottleneck (CLUB) model, which (i) provides a unified theoretical framework that generalizes most of the…
• Backpropagation Clipping for Deep Learning with Differential Privacy
  10 Feb 2022 1 repository listed
  We present backpropagation clipping, a novel variant of differentially private stochastic gradient descent (DP-SGD) for privacy-preserving deep learning.
• Homogeneous Learning: Self-Attention Decentralized Deep Learning
  11 Oct 2021 1 repository listed
  To this end, we propose a decentralized learning model called Homogeneous Learning (HL) for tackling non-IID data with a self-attention mechanism.
• Towards Secure and Practical Machine Learning via Secret Sharing and Random Permutation
  17 Aug 2021 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Since our method reduces the cost for element-wise function computation, it is more efficient than existing cryptographic methods.
• Antipodes of Label Differential Privacy: PATE and ALIBI
  7 Jun 2021 1 repository listed Syntology ran 6 of 7 samples · 1 unverified · 7 pointer-only (licence)
  We propose two novel approaches based on, respectively, the Laplace mechanism and the PATE framework, and demonstrate their effectiveness on standard benchmarks.
• Variational Leakage: The Role of Information Complexity in Privacy Leakage
  5 Jun 2021 1 repository listed
  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.
• CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU
  22 Apr 2021 1 repository listed
  We then identify a sequence of "GPU-friendly" cryptographic protocols to enable privacy-preserving evaluation of both linear and non-linear operations on the GPU.
• Practical Privacy Filters and Odometers with Rényi Differential Privacy and Applications to Differentially Private Deep Learning
  2 Mar 2021 1 repository listed
  Differential Privacy (DP) is the leading approach to privacy preserving deep learning.
• Can we Generalize and Distribute Private Representation Learning?
  5 Oct 2020 1 repository listed
  We study the problem of learning representations that are private yet informative, i.
• Secure Data Sharing With Flow Model
  24 Sep 2020 1 repository listed
  In the classical multi-party computation setting, multiple parties jointly compute a function without revealing their own input data.
• Tempered Sigmoid Activations for Deep Learning with Differential Privacy
  28 Jul 2020 1 repository listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  Because learning sometimes involves sensitive data, machine learning algorithms have been extended to offer privacy for training data.
• Locally Private Graph Neural Networks
  9 Jun 2020 1 repository listed Syntology ran 1 of 3 samples · 2 unverified
  In this paper, we study the problem of node data privacy, where graph nodes have potentially sensitive data that is kept private, but they could be beneficial for a central server for training a GNN over the graph.
• Fawkes: Protecting Privacy against Unauthorized Deep Learning Models
  19 Feb 2020 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  In this paper, we propose Fawkes, a system that helps individuals inoculate their images against unauthorized facial recognition models.
• Towards Fair and Privacy-Preserving Federated Deep Models
  4 Jun 2019 1 repository listed
  This problem can be addressed by either a centralized framework that deploys a central server to train a global model on the joint data from all parties, or a distributed framework that leverages a parameter server to…

Syntology lines on 8 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.