A Privacy-Preserving and Trustable Multi-agent Learning Framework

• URL: http://arxiv.org/abs/2106.01242v1
• Date: Wed, 2 Jun 2021 15:46:27 GMT
• Title: A Privacy-Preserving and Trustable Multi-agent Learning Framework
• Authors: Anudit Nagar, Cuong Tran, Ferdinando Fioretto
• Abstract summary: This paper presents Privacy-preserving and trustable Distributed Learning (PT-DL) PT-DL is a fully decentralized framework that relies on Differential Privacy to guarantee strong privacy protections of the agents' data. The paper shows that PT-DL is resilient up to a 50% collusion attack, with high probability, in a malicious trust model.
• Score: 34.28936739262812
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distributed multi-agent learning enables agents to cooperatively train a model without requiring to share their datasets. While this setting ensures some level of privacy, it has been shown that, even when data is not directly shared, the training process is vulnerable to privacy attacks including data reconstruction and model inversion attacks. Additionally, malicious agents that train on inverted labels or random data, may arbitrarily weaken the accuracy of the global model. This paper addresses these challenges and presents Privacy-preserving and trustable Distributed Learning (PT-DL), a fully decentralized framework that relies on Differential Privacy to guarantee strong privacy protections of the agents' data, and Ethereum smart contracts to ensure trustability. The paper shows that PT-DL is resilient up to a 50% collusion attack, with high probability, in a malicious trust model and the experimental evaluation illustrates the benefits of the proposed model as a privacy-preserving and trustable distributed multi-agent learning system on several classification tasks.

Related papers

• Pseudo-Probability Unlearning: Towards Efficient and Privacy-Preserving Machine Unlearning [59.29849532966454]
  We propose PseudoProbability Unlearning (PPU), a novel method that enables models to forget data to adhere to privacy-preserving manner. Our method achieves over 20% improvements in forgetting error compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2024-11-04T21:27:06Z)
• Lancelot: Towards Efficient and Privacy-Preserving Byzantine-Robust Federated Learning within Fully Homomorphic Encryption [10.685816010576918]
  We propose Lancelot, an innovative and computationally efficient BRFL framework that employs fully homomorphic encryption (FHE) to safeguard against malicious client activities while preserving data privacy. Our extensive testing, which includes medical imaging diagnostics and widely-used public image datasets, demonstrates that Lancelot significantly outperforms existing methods, offering more than a twenty-fold increase in processing speed, all while maintaining data privacy.
  arXiv  Detail & Related papers  (2024-08-12T14:48:25Z)
• 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)
• Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond [6.731000738818571]
  Federated learning (FL) is a framework for training machine learning models in a distributed and collaborative manner. In this paper, we address the triadic interaction among personalization, privacy guarantees, and fairness attained by models trained within the FL framework. A method is put forth that introduces group privacy assurances through the utilization of $d$-privacy.
  arXiv  Detail & Related papers  (2023-09-01T12:20:19Z)
• Auditing and Generating Synthetic Data with Controllable Trust Trade-offs [54.262044436203965]
  We introduce a holistic auditing framework that comprehensively evaluates synthetic datasets and AI models. It focuses on preventing bias and discrimination, ensures fidelity to the source data, assesses utility, robustness, and privacy preservation. We demonstrate the framework's effectiveness by auditing various generative models across diverse use cases.
  arXiv  Detail & Related papers  (2023-04-21T09:03:18Z)
• Client-specific Property Inference against Secure Aggregation in Federated Learning [52.8564467292226]
  Federated learning has become a widely used paradigm for collaboratively training a common model among different participants. Many attacks have shown that it is still possible to infer sensitive information such as membership, property, or outright reconstruction of participant data. We show that simple linear models can effectively capture client-specific properties only from the aggregated model updates.
  arXiv  Detail & Related papers  (2023-03-07T14:11:01Z)
• Protecting Data from all Parties: Combining FHE and DP in Federated Learning [0.09176056742068812]
  We propose a secure framework addressing an extended threat model with respect to privacy of the training data. The proposed framework protects the privacy of the training data from all participants, namely the training data owners and an aggregating server. By means of a novel quantization operator, we prove differential privacy guarantees in a context where the noise is quantified and bounded due to the use of homomorphic encryption.
  arXiv  Detail & Related papers  (2022-05-09T14:33:44Z)
• PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy [14.678119872268198]
  Federated Learning (FL) allows multiple participating clients to train machine learning models collaboratively by keeping their datasets local and only exchanging model updates. Existing FL protocol designs have been shown to be vulnerable to attacks that aim to compromise data privacy and/or model robustness. We develop a framework called PRECAD, which simultaneously achieves differential privacy (DP) and enhances robustness against model poisoning attacks with the help of cryptography.
  arXiv  Detail & Related papers  (2021-10-22T04:08:42Z)
• Cooperative Multi-Agent Actor-Critic for Privacy-Preserving Load Scheduling in a Residential Microgrid [71.17179010567123]
  We propose a privacy-preserving multi-agent actor-critic framework where the decentralized actors are trained with distributed critics. The proposed framework can preserve the privacy of the households while simultaneously learn the multi-agent credit assignment mechanism implicitly.
  arXiv  Detail & Related papers  (2021-10-06T14:05:26Z)
• Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning [123.3472310767721]
  Prediction credibility measures are fundamental in statistics and machine learning. These measures should account for the wide variety of models used in practice. The framework developed in this work expresses the credibility as a risk-fit trade-off.
  arXiv  Detail & Related papers  (2020-11-24T19:52:38Z)
• Federated Learning in Adversarial Settings [0.8701566919381224]
  Federated learning scheme provides different trade-offs between robustness, privacy, bandwidth efficiency, and model accuracy. We show that this extension performs as efficiently as the non-private but robust scheme, even with stringent privacy requirements. This suggests a possible fundamental trade-off between Differential Privacy and robustness.
  arXiv  Detail & Related papers  (2020-10-15T14:57:02Z)

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.