FLSSM: A Federated Learning Storage Security Model with Homomorphic Encryption

• URL: http://arxiv.org/abs/2504.11088v1
• Date: Tue, 15 Apr 2025 11:33:14 GMT
• Title: FLSSM: A Federated Learning Storage Security Model with Homomorphic Encryption
• Authors: Yang Li, Chunhe Xia, Chang Li, Xiaojian Li, Tianbo Wang,
• Abstract summary: This paper proposes a federated learning storage security model with homomorphic encryption (FLSSM) to protect federated learning model privacy.<n> Experiments on multiple real-world datasets show that our model significantly outperforms baseline models in terms of both efficiency and security metrics.
• Score: 8.782251974115818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning based on homomorphic encryption has received widespread attention due to its high security and enhanced protection of user data privacy. However, the characteristics of encrypted computation lead to three challenging problems: ``computation-efficiency", ``attack-tracing" and ``contribution-assessment". The first refers to the efficiency of encrypted computation during model aggregation, the second refers to tracing malicious attacks in an encrypted state, and the third refers to the fairness of contribution assessment for local models after encryption. This paper proposes a federated learning storage security model with homomorphic encryption (FLSSM) to protect federated learning model privacy and address the three issues mentioned above. First, we utilize different nodes to aggregate local models in parallel, thereby improving encrypted models' aggregation efficiency. Second, we introduce trusted supervise nodes to examine local models when the global model is attacked, enabling the tracing of malicious attacks under homomorphic encryption. Finally, we fairly reward local training nodes with encrypted local models based on trusted training time. Experiments on multiple real-world datasets show that our model significantly outperforms baseline models in terms of both efficiency and security metrics.

Related papers

• FLUE: Federated Learning with Un-Encrypted model weights [0.0]
  Federated learning enables devices to collaboratively train a shared model while keeping training data locally stored. Recent research emphasizes using encrypted model parameters during training. This paper introduces a novel federated learning algorithm, leveraging coded local gradients without encryption.
  arXiv  Detail & Related papers  (2024-07-26T14:04:57Z)
• Robust Utility-Preserving Text Anonymization Based on Large Language Models [80.5266278002083]
  Text anonymization is crucial for sharing sensitive data while maintaining privacy. Existing techniques face the emerging challenges of re-identification attack ability of Large Language Models. This paper proposes a framework composed of three LLM-based components -- a privacy evaluator, a utility evaluator, and an optimization component.
  arXiv  Detail & Related papers  (2024-07-16T14:28:56Z)
• Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models [112.48136829374741]
  In this paper, we unveil a new vulnerability: the privacy backdoor attack. When a victim fine-tunes a backdoored model, their training data will be leaked at a significantly higher rate than if they had fine-tuned a typical model. Our findings highlight a critical privacy concern within the machine learning community and call for a reevaluation of safety protocols in the use of open-source pre-trained models.
  arXiv  Detail & Related papers  (2024-04-01T16:50:54Z)
• FewFedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning [54.26614091429253]
  Federated instruction tuning (FedIT) is a promising solution, by consolidating collaborative training across multiple data owners. FedIT encounters limitations such as scarcity of instructional data and risk of exposure to training data extraction attacks. We propose FewFedPIT, designed to simultaneously enhance privacy protection and model performance of federated few-shot learning.
  arXiv  Detail & Related papers  (2024-03-10T08:41:22Z)
• SentinelLMs: Encrypted Input Adaptation and Fine-tuning of Language Models for Private and Secure Inference [6.0189674528771]
  This paper addresses the privacy and security concerns associated with deep neural language models. Deep neural language models serve as crucial components in various modern AI-based applications. We propose a novel method to adapt and fine-tune transformer-based language models on passkey-encrypted user-specific text.
  arXiv  Detail & Related papers  (2023-12-28T19:55:11Z)
• An Efficient and Multi-private Key Secure Aggregation for Federated Learning [41.29971745967693]
  We propose an efficient and multi-private key secure aggregation scheme for federated learning. Specifically, we skillfully modify the variant ElGamal encryption technique to achieve homomorphic addition operation. For the high dimensional deep model parameter, we introduce a super-increasing sequence to compress multi-dimensional data into 1-D.
  arXiv  Detail & Related papers  (2023-06-15T09:05:36Z)
• FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users [19.209830150036254]
  federated learning (FL) technique was developed to mitigate data privacy issues in the traditional machine learning paradigm. Next-generation FL architectures proposed encryption and anonymization techniques to protect the model updates from the server. This paper proposes a novel FL algorithm based on a fully homomorphic encryption (FHE) scheme.
  arXiv  Detail & Related papers  (2023-06-08T11:20:00Z)
• AdaptGuard: Defending Against Universal Attacks for Model Adaptation [129.2012687550069]
  We study the vulnerability to universal attacks transferred from the source domain during model adaptation algorithms. We propose a model preprocessing framework, named AdaptGuard, to improve the security of model adaptation algorithms.
  arXiv  Detail & Related papers  (2023-03-19T07:53:31Z)
• FedCC: Robust Federated Learning against Model Poisoning Attacks [0.0]
  Federated learning is a distributed framework designed to address privacy concerns.<n>It introduces new attack surfaces, which are especially prone when data is non-Independently and Identically Distributed.<n>We present FedCC, a simple yet effective novel defense algorithm against model poisoning attacks.
  arXiv  Detail & Related papers  (2022-12-05T01:52:32Z)
• Privacy-Preserved Neural Graph Similarity Learning [99.78599103903777]
  We propose a novel Privacy-Preserving neural Graph Matching network model, named PPGM, for graph similarity learning. To prevent reconstruction attacks, the proposed model does not communicate node-level representations between devices. To alleviate the attacks to graph properties, the obfuscated features that contain information from both vectors are communicated.
  arXiv  Detail & Related papers  (2022-10-21T04:38:25Z)
• 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)
• Just Fine-tune Twice: Selective Differential Privacy for Large Language Models [69.66654761324702]
  We propose a simple yet effective just-fine-tune-twice privacy mechanism to achieve SDP for large Transformer-based language models. Experiments show that our models achieve strong performance while staying robust to the canary insertion attack.
  arXiv  Detail & Related papers  (2022-04-15T22:36:55Z)
• Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption [14.269757725951882]
  Federated learning (FL) enables distributed computation of machine learning models over disparate, remote data sources. Recent membership attacks show that private or sensitive personal data can sometimes be leaked or inferred when model parameters or summary statistics are shared with a central site. We propose a framework for secure FL using fully-homomorphic encryption (FHE)
  arXiv  Detail & Related papers  (2021-08-07T12:15:52Z)

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.