Privacy Preserving

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 758 papers with code (2,975 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.

• Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
  13 Sep 2019 9 repositories listed
  In this work, we look at the effect such non-identical data distributions has on visual classification via Federated Learning.
• Differentially Private Federated Learning: A Client Level Perspective
  20 Dec 2017 6 repositories listed
  In such an attack, a client's contribution during training and information about their data set is revealed through analyzing the distributed model.
• 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.
• GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning
  20 Aug 2023 4 repositories listed Syntology ran 9 of 17 samples · 8 unverified · 7 pointer-only (licence)
  Federated Learning (FL) is popular for its privacy-preserving and collaborative learning capabilities.
• Personalized Federated Learning with Moreau Envelopes
  16 Jun 2020 4 repositories listed
  Federated learning (FL) is a decentralized and privacy-preserving machine learning technique in which a group of clients collaborate with a server to learn a global model without sharing clients' data.
• Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation
  29 Oct 2019 4 repositories listed
  There is an increasing interest in a fast-growing machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs), exploiting UEs' local computation and…
• Encrypted Vector Similarity Computations Using Partially Homomorphic Encryption: Applications and Performance Analysis
  7 Mar 2025 3 repositories listed
  This paper explores the use of partially homomorphic encryption (PHE) for encrypted vector similarity search, with a focus on facial recognition and broader applications like reverse image search, recommendation…
• OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning
  10 Feb 2024 3 repositories listed
  Trained on massive publicly available data, large language models (LLMs) have demonstrated tremendous success across various fields.
• Federated Causal Inference from Observational Data
  24 Aug 2023 3 repositories listed Syntology ran 4 of 6 samples · 2 unverified · 6 pointer-only (licence)
  In this article, we propose a framework to estimate causal effects from decentralized data sources.
• FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity
  9 May 2023 3 repositories listed Syntology ran 2 of 4 samples · 2 unverified · 4 pointer-only (licence)
  Federated noisy label learning (FNLL) is emerging as a promising tool for privacy-preserving multi-source decentralized learning.
• FedGH: Heterogeneous Federated Learning with Generalized Global Header
  23 Mar 2023 3 repositories listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  It is a communication and computation-efficient model-heterogeneous FL framework which trains a shared generalized global prediction header with representations extracted by heterogeneous extractors for clients' models…
• FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations
  2 Feb 2023 3 repositories listed Syntology ran 0 of 21 samples · 21 unverified
  Our formulation involves clients synthesizing a small set of samples that approximate local loss landscapes by simulating the gradients of real images within a local region.
• HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors
  17 Nov 2022 3 repositories listed
  The main streams of human activity recognition (HAR) algorithms are developed based on RGB cameras which are suffered from illumination, fast motion, privacy-preserving, and large energy consumption.
• Federated Causal Discovery From Interventions
  7 Nov 2022 3 repositories listed Syntology ran 0 of 13 samples · 13 unverified
  We propose FedCDI, a federated framework for inferring causal structures from distributed data containing interventional samples.
• FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning
  16 Nov 2021 3 repositories listed
  Federated learning (FL) aims to protect data privacy by enabling clients to build machine learning models collaboratively without sharing their private data.
• Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform
  8 Jul 2021 3 repositories listed
  We use this platform to demonstrate our research and development results on privacy preserving machine learning algorithms.
• Privacy-preserving Collaborative Learning with Automatic Transformation Search
  25 Nov 2020 3 repositories listed
  Comprehensive evaluations demonstrate that the policies discovered by our method can defeat existing reconstruction attacks in collaborative learning, with high efficiency and negligible impact on the model performance.
• Partially Encrypted Machine Learning using Functional Encryption
  24 May 2019 3 repositories listed
  Machine learning on encrypted data has received a lot of attention thanks to recent breakthroughs in homomorphic encryption and secure multi-party computation.
• 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.
• Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study
  22 Jul 2018 3 repositories listed
  This paper aims to improve privacy-preserving visual recognition, an increasingly demanded feature in smart camera applications, by formulating a unique adversarial training framework.
• Towards Robust and Privacy-preserving Text Representations
  16 May 2018 3 repositories listed
  Written text often provides sufficient clues to identify the author, their gender, age, and other important attributes.
• SecONNds: Secure Outsourced Neural Network Inference on ImageNet
  13 Jun 2025 2 repositories listed
  The widespread adoption of outsourced neural network inference presents significant privacy challenges, as sensitive user data is processed on untrusted remote servers.
• Towards nation-wide analytical healthcare infrastructures: A privacy-preserving augmented knee rehabilitation case study
  30 Dec 2024 2 repositories listed
  For patients and physiotherapists, video with side-to-side timeseries visually indicating potential issues such as excessive knee flexion or unstable knee movements or stick figure overlay errors is possible by setting…
• Attack-Aware Noise Calibration for Differential Privacy
  2 Jul 2024 2 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  For a given notion of attack risk, our approach significantly decreases noise scale, leading to increased utility at the same level of privacy.
• A Synthetic Dataset for Personal Attribute Inference
  11 Jun 2024 2 repositories listed Syntology ran 5 of 6 samples · 1 unverified
  Recently, powerful Large Language Models (LLMs) have become easily accessible to hundreds of millions of users world-wide.
• EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data Sharing
  2 Jun 2024 2 repositories listed
  To make medical datasets accessible without sharing sensitive patient information, we introduce a novel end-to-end approach for generative de-identification of dynamic medical imaging data.
• CHESS: Contextual Harnessing for Efficient SQL Synthesis
  27 May 2024 2 repositories listed Syntology ran 6 of 6 samples · 0 unverified
  Translating natural language questions into SQL queries, known as text-to-SQL, is a long-standing research problem.
• Privacy-Preserving Face Recognition Using Trainable Feature Subtraction
  19 Mar 2024 2 repositories listed
  Recognizable identity features within the image are encouraged by co-training a recognition model on its high-dimensional feature representation.
• Differentially Private Synthetic Data via Foundation Model APIs 2: Text
  4 Mar 2024 2 repositories listed Syntology ran 6 of 11 samples · 5 unverified
  Lin et al.
• EmojiPrompt: Generative Prompt Obfuscation for Privacy-Preserving Communication with Cloud-based LLMs
  8 Feb 2024 2 repositories listed
  Nevertheless, they also introduce privacy concerns: firstly, numerous studies underscore the risks to user privacy posed by jailbreaking cloud-based LLMs; secondly, the LLM service providers have access to all user…

Syntology lines on 11 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.