# Accuracy and Privacy Evaluations of Collaborative Data Analysis

arXiv: https://arxiv.org/abs/2101.11144 · HTML: https://mlsanity.com/paper/2101.11144/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 27 Jan 2021.

## Builds on (most cited first, 4 of 4)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [AutoGAN-based Dimension Reduction for Privacy Preservation](https://mlsanity.com/paper/1902.10799/) (2019)

## Built on by (most cited first, 13 of 13)

- [Collaborative causal inference on distributed data](https://mlsanity.com/paper/2208.07898/) (2022)
- [Non-readily identifiable data collaboration analysis for multiple datasets including personal information](https://mlsanity.com/paper/2208.14611/) (2022)
- [Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach](https://mlsanity.com/paper/2501.12723/) (2025)
- [New Solutions Based on the Generalized Eigenvalue Problem for the Data Collaboration Analysis](https://mlsanity.com/paper/2404.14164/) (2024)
- [Data Collaboration Analysis with Orthonormal Basis Selection and Alignment](https://mlsanity.com/paper/2403.02780/) (2024)
- [Privacy-preserving recommender system using the data collaboration analysis for distributed datasets](https://mlsanity.com/paper/2406.01603/) (2024)
- [FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaboration](https://mlsanity.com/paper/2409.18356/) (2024)
- [Data Collaboration Analysis applied to Compound Datasets and the Introduction of Projection data to Non-IID settings](https://mlsanity.com/paper/2308.00280/) (2023)
- [Estimation of conditional average treatment effects on distributed confidential data](https://mlsanity.com/paper/2402.02672/) (2024)
- [A new type of federated clustering: A non-model-sharing approach](https://mlsanity.com/paper/2506.10244/) (2025)
- [Single-Round Clustered Federated Learning via Data Collaboration Analysis for Non-IID Data](https://mlsanity.com/paper/2601.09304/) (2026)
- [Nonlinear Data Integration via Kernel Methods for Data Collaboration Analysis](https://mlsanity.com/paper/2605.27219/) (2026)
- [Geometric Data Perturbation with Noisy-Anchor Alignment for Privacy-Preserving Collaborative Learning](https://mlsanity.com/paper/2608.18749/) (2026)

## 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.