# DPAUC: Differentially Private AUC Computation in Federated Learning

arXiv: https://arxiv.org/abs/2208.12294 · HTML: https://mlsanity.com/paper/2208.12294/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 50 references that are in the map.
- First posted on arXiv 25 Aug 2022.

## Builds on (most cited first, 25 of 25)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [iDLG: Improved Deep Leakage from Gradients](https://mlsanity.com/paper/2001.02610/) (2020)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [Numerical Composition of Differential Privacy](https://mlsanity.com/paper/2106.02848/) (2021)
- [Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity](https://mlsanity.com/paper/1811.12469/) (2018)
- [Lower Bounds and Optimal Algorithms for Personalized Federated Learning](https://mlsanity.com/paper/2010.02372/) (2020)
- [Deep Learning with Label Differential Privacy](https://mlsanity.com/paper/2102.06062/) (2021)
- [Distributed Differential Privacy via Shuffling](https://mlsanity.com/paper/1808.01394/) (2018)
- [Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization](https://mlsanity.com/paper/2002.11364/) (2020)
- [Label Leakage and Protection in Two-party Split Learning](https://mlsanity.com/paper/2102.08504/) (2021)
- [Federated Accelerated Stochastic Gradient Descent](https://mlsanity.com/paper/2006.08950/) (2020)
- [Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?](https://mlsanity.com/paper/2003.12365/) (2020)
- [SplitNN-driven Vertical Partitioning](https://mlsanity.com/paper/2008.04137/) (2020)
- [Defending against Reconstruction Attack in Vertical Federated Learning](https://mlsanity.com/paper/2107.09898/) (2021)
- [Local Differential Privacy: a tutorial](https://mlsanity.com/paper/1907.11908/) (2019)
- [Differentially Private Algorithms for Empirical Machine Learning](https://mlsanity.com/paper/1411.5428/) (2014)
- [Differentially Private AUC Computation in Vertical Federated Learning](https://mlsanity.com/paper/2205.12412/) (2022)
- [Randomized Response Mechanisms for Differential Privacy Data Analysis: Bounds and Applications](https://mlsanity.com/paper/2112.07397/) (2021)

## Built on by (most cited first, 7 of 7)

- [Label Inference Attack against Split Learning under Regression Setting](https://mlsanity.com/paper/2301.07284/) (2023)
- [Federated Calibration and Evaluation of Binary Classifiers](https://mlsanity.com/paper/2210.12526/) (2022)
- [PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance](https://mlsanity.com/paper/2312.03792/) (2023)
- [S2NeRF: Privacy-preserving Training Framework for NeRF](https://mlsanity.com/paper/2409.01661/) (2024)
- [Fluent: Round-efficient Secure Aggregation for Private Federated Learning](https://mlsanity.com/paper/2403.06143/) (2024)
- [Differential Privacy for Euclidean Jordan Algebra with Applications to Private Symmetric Cone Programming](https://mlsanity.com/paper/2509.16915/) (2025)
- [Federated Computation of ROC and PR Curves](https://mlsanity.com/paper/2510.04979/) (2025)

## Abstract (arXiv)

Federated learning (FL) has gained significant attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple participants. The prior work on FL has mostly studied how to protect label privacy during model training. However, model evaluation in FL might also lead to potential leakage of private label information. In this work, we propose an evaluation algorithm that can accurately compute the widely used AUC (area under the curve) metric when using the label differential privacy (DP) in FL. Through extensive experiments, we show our algorithms can compute accurate AUCs compared to the ground truth. The code is available at urlhttps://github.com/bytedance/fedlearner/tree/master/example/privacy/DPAUC.