FHAUC: Privacy Preserving AUC Calculation for Federated Learning using Fully Homomorphic Encryption

• URL: http://arxiv.org/abs/2403.14428v1
• Date: Thu, 21 Mar 2024 14:36:55 GMT
• Title: FHAUC: Privacy Preserving AUC Calculation for Federated Learning using Fully Homomorphic Encryption
• Authors: Cem Ata Baykara, Ali Burak Ünal, Mete Akgün,
• Abstract summary: We propose an efficient, accurate, robust, and more secure evaluation algorithm capable of computing the AUC in horizontal federated learning systems. Our approach can efficiently calculate the AUC of a federated learning system involving 100 parties, achieving 99.93% accuracy in just 0.68 seconds, regardless of data size.
• Score: 1.9662978733004604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ensuring data privacy is a significant challenge for machine learning applications, not only during model training but also during evaluation. Federated learning has gained significant research interest in recent years as a result. Current research on federated learning primarily focuses on preserving privacy during the training phase. However, model evaluation has not been adequately addressed, despite the potential for significant privacy leaks during this phase as well. In this paper, we demonstrate that the state-of-the-art AUC computation method for federated learning systems, which utilizes differential privacy, still leaks sensitive information about the test data while also requiring a trusted central entity to perform the computations. More importantly, we show that the performance of this method becomes completely unusable as the data size decreases. In this context, we propose an efficient, accurate, robust, and more secure evaluation algorithm capable of computing the AUC in horizontal federated learning systems. Our approach not only enhances security compared to the current state-of-the-art but also surpasses the state-of-the-art AUC computation method in both approximation performance and computational robustness, as demonstrated by experimental results. To illustrate, our approach can efficiently calculate the AUC of a federated learning system involving 100 parties, achieving 99.93% accuracy in just 0.68 seconds, regardless of data size, while providing complete data privacy.

Related papers

• Efficient Machine Unlearning via Influence Approximation [75.31015485113993]
  Influence-based unlearning has emerged as a prominent approach to estimate the impact of individual training samples on model parameters without retraining.<n>This paper establishes a theoretical link between memorizing (incremental learning) and forgetting (unlearning)<n>We introduce the Influence Approximation Unlearning algorithm for efficient machine unlearning from the incremental perspective.
  arXiv  Detail & Related papers  (2025-07-31T05:34:27Z)
• Benchmarking Federated Machine Unlearning methods for Tabular Data [9.30408906787193]
  Machine unlearning enables a model to forget specific data upon request.<n>This paper presents a pioneering study on benchmarking machine unlearning methods within a federated setting.<n>We explore unlearning at the feature and instance levels, employing both machine learning, random forest and logistic regression models.
  arXiv  Detail & Related papers  (2025-04-01T15:53:36Z)
• Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning [0.0]
  This paper presents an experimental evaluation of federated learning for educational data prediction. Our findings indicate that federated learning achieves comparable predictive accuracy. Under adversarial attacks, federated learning demonstrates greater resilience compared to non-federated settings.
  arXiv  Detail & Related papers  (2025-03-16T14:37:32Z)
• Privacy-Preserved Automated Scoring using Federated Learning for Educational Research [1.2556373621040728]
  This study proposes a federated learning framework for automatic scoring in educational assessments. Student responses are processed locally on edge devices, and only optimized model parameters are shared with a central aggregation server. We evaluate our framework using assessment data from nine middle schools, comparing the accuracy of federated learning-based scoring models with traditionally trained centralized models.
  arXiv  Detail & Related papers  (2025-03-12T19:06:25Z)
• DWFL: Enhancing Federated Learning through Dynamic Weighted Averaging [2.499907423888049]
  We propose a deep feed-forward neural network-based enhanced federated learning method for protein sequence classification. We introduce dynamic weighted federated learning (DWFL) which is a federated learning-based approach. We conduct experiments using real-world protein sequence datasets to assess the effectiveness of DWFL.
  arXiv  Detail & Related papers  (2024-11-07T20:24:23Z)
• Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset [94.13848736705575]
  We introduce Facial Identity Unlearning Benchmark (FIUBench), a novel VLM unlearning benchmark designed to robustly evaluate the effectiveness of unlearning algorithms. We apply a two-stage evaluation pipeline that is designed to precisely control the sources of information and their exposure levels. Through the evaluation of four baseline VLM unlearning algorithms within FIUBench, we find that all methods remain limited in their unlearning performance.
  arXiv  Detail & Related papers  (2024-11-05T23:26:10Z)
• FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses [50.921333548391345]
  Federated Learning is a privacy preserving decentralized machine learning paradigm. Recent research has revealed that private ground truth data can be recovered through a gradient technique known as Deep Leakage. This paper introduces the FEDLAD Framework (Federated Evaluation of Deep Leakage Attacks and Defenses), a comprehensive benchmark for evaluating Deep Leakage attacks and defenses.
  arXiv  Detail & Related papers  (2024-11-05T11:42:26Z)
• An Empirical Study of Efficiency and Privacy of Federated Learning Algorithms [2.994794762377111]
  In today's world, the rapid expansion of IoT networks and the proliferation of smart devices have resulted in the generation of substantial amounts of heterogeneous data. To handle this data effectively, advanced data processing technologies are necessary to guarantee the preservation of both privacy and efficiency. Federated learning emerged as a distributed learning method that trains models locally and aggregates them on a server to preserve data privacy.
  arXiv  Detail & Related papers  (2023-12-24T00:13:41Z)
• Exploring Federated Unlearning: Analysis, Comparison, and Insights [101.64910079905566]
  federated unlearning enables the selective removal of data from models trained in federated systems. This paper examines existing federated unlearning approaches, examining their algorithmic efficiency, impact on model accuracy, and effectiveness in preserving privacy. We propose the OpenFederatedUnlearning framework, a unified benchmark for evaluating federated unlearning methods.
  arXiv  Detail & Related papers  (2023-10-30T01:34:33Z)
• LAVA: Data Valuation without Pre-Specified Learning Algorithms [20.578106028270607]
  We introduce a new framework that can value training data in a way that is oblivious to the downstream learning algorithm. We develop a proxy for the validation performance associated with a training set based on a non-conventional class-wise Wasserstein distance between training and validation sets. We show that the distance characterizes the upper bound of the validation performance for any given model under certain Lipschitz conditions.
  arXiv  Detail & Related papers  (2023-04-28T19:05:16Z)
• Balanced Self-Paced Learning for AUC Maximization [88.53174245457268]
  Existing self-paced methods are limited to pointwise AUC. Our algorithm converges to a stationary point on the basis of closed-form solutions.
  arXiv  Detail & Related papers  (2022-07-08T02:09:32Z)
• Precision-Weighted Federated Learning [1.8160945635344528]
  We propose a novel algorithm that takes into account the variance of the gradients when computing the weighted average of the parameters of models trained in a Federated Learning setting. Our method was evaluated using standard image classification datasets with two different data partitioning strategies (IID/non-IID) to measure the performance and speed of our method in resource-constrained environments.
  arXiv  Detail & Related papers  (2021-07-20T17:17:10Z)
• Can Active Learning Preemptively Mitigate Fairness Issues? [66.84854430781097]
  dataset bias is one of the prevailing causes of unfairness in machine learning. We study whether models trained with uncertainty-based ALs are fairer in their decisions with respect to a protected class. We also explore the interaction of algorithmic fairness methods such as gradient reversal (GRAD) and BALD.
  arXiv  Detail & Related papers  (2021-04-14T14:20:22Z)
• On Deep Learning with Label Differential Privacy [54.45348348861426]
  We study the multi-class classification setting where the labels are considered sensitive and ought to be protected. We propose a new algorithm for training deep neural networks with label differential privacy, and run evaluations on several datasets.
  arXiv  Detail & Related papers  (2021-02-11T15:09:06Z)
• Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data [93.76907759950608]
  We propose a doubly kernel learning algorithm for vertically partitioned data. We show that FDSKL is significantly faster than state-of-the-art federated learning methods when dealing with kernels.
  arXiv  Detail & Related papers  (2020-08-14T05:46:56Z)
• SPEED: Secure, PrivatE, and Efficient Deep learning [2.283665431721732]
  We introduce a deep learning framework able to deal with strong privacy constraints. Based on collaborative learning, differential privacy and homomorphic encryption, the proposed approach advances state-of-the-art.
  arXiv  Detail & Related papers  (2020-06-16T19:31:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.