A homomorphic-encryption-based vertical federated learning scheme for rick management

Ou, Wei; Zeng, Jianhuan; Guo, Zijun; Yan, Wanqin; Liu, Dingwan; Fuentes, Stelios

doi:10.2298/csis190923022o

Cited by 35 publications

(10 citation statements)

References 7 publications

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“…It encodes a batch of quantized gradients as a long integer, and then encrypts it at one time, which improves the efficiency of encryption and decryption by reducing the amount of calculation. Wei Ou et al proposed a vertical federated learning system for Bayesian machine learning with homomorphic encryption, which can achieve 90% of the performance of a single union server training model [47].…”

Section: Federated Learningmentioning

confidence: 99%

Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning

2021

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Privacy protection has been an important concern with the great success of machine learning. In this paper, it proposes a multi-party privacy preserving machine learning framework, named PFMLP, based on partially homomorphic encryption and federated learning. The core idea is all learning parties just transmitting the encrypted gradients by homomorphic encryption. From experiments, the model trained by PFMLP has almost the same accuracy, and the deviation is less than 1%. Considering the computational overhead of homomorphic encryption, we use an improved Paillier algorithm which can speed up the training by 25–28%. Moreover, comparisons on encryption key length, the learning network structure, number of learning clients, etc. are also discussed in detail in the paper.

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Section: Federated Learningmentioning

confidence: 99%

Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning

2021

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“…CKKS is a full homomorphic encryption method that adds noise at the end of the truncated ciphertext. Comparing several homomorphic encryption algorithms, CKKS overcomes Paillier and RSA [32] [33] in encryption speed. To pursue the superior performance and training speed, we select the CKKS to protect the data of the edge nodes in the proposed method.…”

Section: Full Homomorphic Encryptionmentioning

confidence: 99%

Privacy-Preserving Federated Transfer Learning for Driver Drowsiness Detection

et al. 2022

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The drowsiness affects the driver's sensory, cognitive, and psychomotor abilities, which are necessary for safe driving. Drowsiness detection is a critical technique to avoid traffic accidents. The federated learning (FL) can solve the problem of insufficient driver facial data by utilizing different industrial entities' data. However, in the FL system, the privacy information of the drivers might be leaked. In addition, reducing the communication costs and maintaining the model performance is also a challenge in industrial scenarios. In this work, we propose a federated transfer learning method with privacypreserving protocol for driver drowsiness detection, named PFTL-DDD. We use fine-tuning transfer learning on the initial model of the drowsiness detection FL system. Furthermore, a CKKS-based privacypreserving protocol is applied to preserve the drivers' privacy data by encrypting the exchanged parameters. The experimental results show that the PFTL-DDD method is superior in terms of accuracy and efficiency as compared to the conventional federated learning on the NTHU-DDD dataset and YAWDD dataset. The theoretical analysis demonstrates that the proposed transfer learning method can reduce the communication cost of the system, and the CKKS-based security protocol can protect the personal privacy.INDEX TERMS Driver drowsiness detection, transfer learning, federated learning, privacy-preserving.

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“…The CKKS adds noise after data encryption via ciphertext truncation. Due to this, the resultant scheme has excellent encryption/decryption speed [32]. Therefore, we adopt the CKKS-based communication protocol in the proposed method.…”

Section: Full Homomorphic Encryptionmentioning

confidence: 99%

Accelerating privacy-preserving momentum federated learning for industrial cyber-physical systems

Zhang

Guan

2021

Complex Intell. Syst.

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Federated learning (FL) is a distributed learning approach, which allows the distributed computing nodes to collaboratively develop a global model while keeping their data locally. However, the issues of privacy-preserving and performance improvement hinder the applications of the FL in the industrial cyber-physical systems (ICPSs). In this work, we propose a privacy-preserving momentum FL approach, named PMFL, which uses the momentum term to accelerate the model convergence rate during the training process. Furthermore, a fully homomorphic encryption scheme CKKS is adopted to encrypt the gradient parameters of the industrial agents’ models for preserving their local privacy information. In particular, the cloud server calculates the global encrypted momentum term by utilizing the encrypted gradients based on the momentum gradient descent optimization algorithm (MGD). The performance of the proposed PMFL is evaluated on two common deep learning datasets, i.e., MNIST and Fashion-MNIST. Theoretical analysis and experiment results confirm that the proposed approach can improve the convergence rate while preserving the privacy information of the industrial agents.

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A homomorphic-encryption-based vertical federated learning scheme for rick management

Cited by 35 publications

References 7 publications

Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning

Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning

Privacy-Preserving Federated Transfer Learning for Driver Drowsiness Detection

Accelerating privacy-preserving momentum federated learning for industrial cyber-physical systems

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