mlsanityData to 13 Oct 2026

Accuracy and Privacy Evaluations of Collaborative Data Analysis

arXiv 2101.11144 ↗First posted 27 Jan 2021cs.LG cs.CRcited by 13

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Abstract (arXiv)

Distributed data analysis without revealing the individual data has recently attracted significant attention in several applications. A collaborative data analysis through sharing dimensionality reduced representations of data has been proposed as a non-model sharing-type federated learning. This paper analyzes the accuracy and privacy evaluations of this novel framework. In the accuracy analysis, we provided sufficient conditions for the equivalence of the collaborative data analysis and the centralized analysis with dimensionality reduction. In the privacy analysis, we proved that collaborative users' private datasets are protected with a double privacy layer against insider and external attacking scenarios.

Key facts. Cited by 13 papers in the map (data through 13 Oct 2026). Builds on 4 of its 27 references that are in the map. First posted on arXiv 27 Jan 2021.

Builds on

4 of 27 references.

References · 4

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,653
  2. 2019Advances and Open Problems in Federated Learningcited by 3,040
  3. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
  4. 2019AutoGAN-based Dimension Reduction for Privacy Preservationcited by 9

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 5; 2025: 2; 2026: 3.

13 papers.

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  1. 2022Collaborative causal inference on distributed datacited by 10
  2. 2022Non-readily identifiable data collaboration analysis for multiple datasets including personal informationcited by 10
  3. 2025Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approachcited by 4
  4. 2024New Solutions Based on the Generalized Eigenvalue Problem for the Data Collaboration Analysiscited by 3
  5. 2024Data Collaboration Analysis with Orthonormal Basis Selection and Alignmentcited by 2
  6. 2024Privacy-preserving recommender system using the data collaboration analysis for distributed datasetscited by 2
  7. 2024FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaborationcited by 2
  8. 2023Data Collaboration Analysis applied to Compound Datasets and the Introduction of Projection data to Non-IID settingscited by 1
  9. 2024Estimation of conditional average treatment effects on distributed confidential datacited by 1
  10. 2025A new type of federated clustering: A non-model-sharing approachcited by 1
  11. 2026Single-Round Clustered Federated Learning via Data Collaboration Analysis for Non-IID Datacited by 1
  12. 2026Nonlinear Data Integration via Kernel Methods for Data Collaboration Analysiscited by 1
1 more
  1. 2026Geometric Data Perturbation with Noisy-Anchor Alignment for Privacy-Preserving Collaborative Learningcited by 0