Decaf: Data Distribution Decompose Attack against Federated Learning

• URL: http://arxiv.org/abs/2405.15316v1
• Date: Fri, 24 May 2024 07:56:32 GMT
• Title: Decaf: Data Distribution Decompose Attack against Federated Learning
• Authors: Zhiyang Dai, Chunyi Zhou, Anmin Fu,
• Abstract summary: We devise an innovative privacy threat: the Data Distribution Decompose Attack on FL, termed Decaf. Decaf operates stealthily, rendering it entirely passive and undetectable to victim users regarding the infringement of their data distribution privacy. Results indicate its ability to accurately decompose local user data distribution, regardless of whether it is IID or non-IID distributed.
• Score: 4.3667223256713745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In contrast to prevalent Federated Learning (FL) privacy inference techniques such as generative adversarial networks attacks, membership inference attacks, property inference attacks, and model inversion attacks, we devise an innovative privacy threat: the Data Distribution Decompose Attack on FL, termed Decaf. This attack enables an honest-but-curious FL server to meticulously profile the proportion of each class owned by the victim FL user, divulging sensitive information like local market item distribution and business competitiveness. The crux of Decaf lies in the profound observation that the magnitude of local model gradient changes closely mirrors the underlying data distribution, including the proportion of each class. Decaf addresses two crucial challenges: accurately identify the missing/null class(es) given by any victim user as a premise and then quantify the precise relationship between gradient changes and each remaining non-null class. Notably, Decaf operates stealthily, rendering it entirely passive and undetectable to victim users regarding the infringement of their data distribution privacy. Experimental validation on five benchmark datasets (MNIST, FASHION-MNIST, CIFAR-10, FER-2013, and SkinCancer) employing diverse model architectures, including customized convolutional networks, standardized VGG16, and ResNet18, demonstrates Decaf's efficacy. Results indicate its ability to accurately decompose local user data distribution, regardless of whether it is IID or non-IID distributed. Specifically, the dissimilarity measured using $L_{\infty}$ distance between the distribution decomposed by Decaf and ground truth is consistently below 5\% when no null classes exist. Moreover, Decaf achieves 100\% accuracy in determining any victim user's null classes, validated through formal proof.

Related papers

• Fairness-Constrained Optimization Attack in Federated Learning [26.380464066437668]
  Federated learning (FL) is a privacy-preserving machine learning technique that facilitates collaboration among participants across demographics.<n>This paper proposes an intentional fairness attack, where a client maliciously sends a biased model.<n>We evaluate our attack against the state-of-the-art Byzantine-robust and fairness-aware aggregation schemes over different datasets.
  arXiv  Detail & Related papers  (2025-10-14T04:49:53Z)
• Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning [4.907460152017894]
  Federated Learning (FL) is a collaborative learning paradigm enabling participants to collectively train a shared machine learning model. Current FL defense strategies against data poisoning attacks either involve a trade-off between accuracy and robustness. We present FedZZ, which harnesses a zone-based deviating update (ZBDU) mechanism to effectively counter data poisoning attacks in FL.
  arXiv  Detail & Related papers  (2024-04-05T14:37:49Z)
• Exploring Vacant Classes in Label-Skewed Federated Learning [113.65301899666645]
  This paper introduces FedVLS, a novel approach to label-skewed federated learning. It integrates vacant-class distillation and logit suppression simultaneously. Experiments validate the efficacy of FedVLS, demonstrating superior performance compared to previous state-of-the-art (SOTA) methods.
  arXiv  Detail & Related papers  (2024-01-04T16:06:31Z)
• Mitigating Cross-client GANs-based Attack in Federated Learning [78.06700142712353]
  Multi distributed multimedia clients can resort to federated learning (FL) to jointly learn a global shared model. FL suffers from the cross-client generative adversarial networks (GANs)-based (C-GANs) attack. We propose Fed-EDKD technique to improve the current popular FL schemes to resist C-GANs attack.
  arXiv  Detail & Related papers  (2023-07-25T08:15:55Z)
• FedVal: Different good or different bad in federated learning [9.558549875692808]
  Federated learning (FL) systems are susceptible to attacks from malicious actors. FL poses new challenges in addressing group bias, such as ensuring fair performance for different demographic groups. Traditional methods used to address such biases require centralized access to the data, which FL systems do not have. We present a novel approach FedVal for both robustness and fairness that does not require any additional information from clients.
  arXiv  Detail & Related papers  (2023-06-06T22:11:13Z)
• PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy [56.347786940414935]
  Federated Learning (FL) has emerged as an effective learning paradigm for distributed computation. This work proposes a novel FL framework that requires only partial GAN model sharing. Named as PS-FedGAN, this new framework enhances the GAN releasing and training mechanism to address heterogeneous data distributions.
  arXiv  Detail & Related papers  (2023-05-19T05:39:40Z)
• FedCC: Robust Federated Learning against Model Poisoning Attacks [0.0]
  Federated Learning is designed to address privacy concerns in learning models. New distributed paradigm safeguards data privacy but differentiates the attack surface due to the server's inaccessibility to local datasets.
  arXiv  Detail & Related papers  (2022-12-05T01:52:32Z)
• Securing Federated Learning against Overwhelming Collusive Attackers [7.587927338603662]
  We propose two graph theoretic algorithms, based on Minimum Spanning Tree and k-Densest graph, by leveraging correlations between local models. Our FL model can nullify the influence of attackers even when they are up to 70% of all the clients. We establish the superiority of our algorithms over the existing ones using accuracy, attack success rate, and early detection round.
  arXiv  Detail & Related papers  (2022-09-28T13:41:04Z)
• Federated Zero-Shot Learning for Visual Recognition [55.65879596326147]
  We propose a novel Federated Zero-Shot Learning FedZSL framework. FedZSL learns a central model from the decentralized data residing on edge devices. The effectiveness and robustness of FedZSL are demonstrated by extensive experiments conducted on three zero-shot benchmark datasets.
  arXiv  Detail & Related papers  (2022-09-05T14:49:34Z)
• FL-Defender: Combating Targeted Attacks in Federated Learning [7.152674461313707]
  Federated learning (FL) enables learning a global machine learning model from local data distributed among a set of participating workers. FL is vulnerable to targeted poisoning attacks that negatively impact the integrity of the learned model. We propose textitFL-Defender as a method to combat FL targeted attacks.
  arXiv  Detail & Related papers  (2022-07-02T16:04:46Z)
• Acceleration of Federated Learning with Alleviated Forgetting in Local Training [61.231021417674235]
  Federated learning (FL) enables distributed optimization of machine learning models while protecting privacy. We propose FedReg, an algorithm to accelerate FL with alleviated knowledge forgetting in the local training stage. Our experiments demonstrate that FedReg not only significantly improves the convergence rate of FL, especially when the neural network architecture is deep.
  arXiv  Detail & Related papers  (2022-03-05T02:31:32Z)
• Balancing Biases and Preserving Privacy on Balanced Faces in the Wild [50.915684171879036]
  There are demographic biases present in current facial recognition (FR) models. We introduce our Balanced Faces in the Wild dataset to measure these biases across different ethnic and gender subgroups. We find that relying on a single score threshold to differentiate between genuine and imposters sample pairs leads to suboptimal results. We propose a novel domain adaptation learning scheme that uses facial features extracted from state-of-the-art neural networks.
  arXiv  Detail & Related papers  (2021-03-16T15:05:49Z)
• WAFFLe: Weight Anonymized Factorization for Federated Learning [88.44939168851721]
  In domains where data are sensitive or private, there is great value in methods that can learn in a distributed manner without the data ever leaving the local devices. We propose Weight Anonymized Factorization for Federated Learning (WAFFLe), an approach that combines the Indian Buffet Process with a shared dictionary of weight factors for neural networks.
  arXiv  Detail & Related papers  (2020-08-13T04:26:31Z)

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.