Privacy-Preserving Machine Learning Training in IoT Aggregation Scenarios

Zhu, Liehuang; Tang, Xiangyun; Shen, Meng; Gao, Feng; Zhang, Jie; Du, Xiaojiang

doi:10.1109/jiot.2021.3060764

Cited by 24 publications

(21 citation statements)

References 47 publications

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“…From their analysis, they propose the combination of the following techniques: (1) ML techniques to provide user privacy protection, (2) policy languages to specify user privacy preferences and to express complex policies, (3) Use of negotiation techniques to provide better services to users while preserving their privacy. Zhu et al [ 26 ] review privacy-preserving ML works differentiating between privacy-preserving prediction solutions and training solutions. In the prediction area, the solutions adopt differential privacy, secure multi-party computation, and homomorphic encryption.…”

Section: Related Workmentioning

confidence: 99%

A Survey of Machine and Deep Learning Methods for Privacy Protection in the Internet of Things

Rodríguez

Otero

Canal

2023

Sensors

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Recent advances in hardware and information technology have accelerated the proliferation of smart and interconnected devices facilitating the rapid development of the Internet of Things (IoT). IoT applications and services are widely adopted in environments such as smart cities, smart industry, autonomous vehicles, and eHealth. As such, IoT devices are ubiquitously connected, transferring sensitive and personal data without requiring human interaction. Consequently, it is crucial to preserve data privacy. This paper presents a comprehensive survey of recent Machine Learning (ML)- and Deep Learning (DL)-based solutions for privacy in IoT. First, we present an in depth analysis of current privacy threats and attacks. Then, for each ML architecture proposed, we present the implementations, details, and the published results. Finally, we identify the most effective solutions for the different threats and attacks.

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Section: Related Workmentioning

confidence: 99%

A Survey of Machine and Deep Learning Methods for Privacy Protection in the Internet of Things

Rodríguez

Otero

Canal

2023

Sensors

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“…Amazon SageMaker from Amazon Web Service (AWS) provides similar services, as introduced in [112]. Zhu et al propose a machine learning training framework that supports homomorphic encryption in [113].…”

Section: Homomorphic Encryptionmentioning

confidence: 99%

A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions

Kovatsch

Mattern

et al. 2022

Applied Sciences

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Artificial intelligence (AI) has been successfully applied in industry for decades, ranging from the emergence of expert systems in the 1960s to the wide popularity of deep learning today. In particular, inexpensive computing and storage infrastructures have moved data-driven AI methods into the spotlight to aid the increasingly complex manufacturing processes. Despite the recent proverbial hype, however, there still exist non-negligible challenges when applying AI to smart manufacturing applications. As far as we know, there exists no work in the literature that summarizes and reviews the related works for these challenges. This paper provides an executive summary on AI techniques for non-experts with a focus on deep learning and then discusses the open issues around data quality, data secrecy, and AI safety that are significant for fully automated industrial AI systems. For each challenge, we present the state-of-the-art techniques that provide promising building blocks for holistic industrial AI solutions and the respective industrial use cases from several domains in order to better provide a concrete view of these techniques. All the examples we reviewed were published in the recent ten years. We hope this paper can provide the readers with a reference for further studying the related problems.

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“…Terefore, frequent interaction leads to a high communication overhead of federated learning [4]. In addition, the goal of federated learning is to train a global model for each data provider, which is diferent from ours [5]. In this paper, the goal of our proposed framework is to train a private model for SSP by using the ciphertext data of data providers (fog nodes).…”

Section: Introductionmentioning

confidence: 99%

“…In [6][7][8], they all adopt the dual cloud server model and assume that the two cloud servers do not collude. However, this assumption has potential security hazards [5].…”

Section: Introductionmentioning

confidence: 99%

“…Normalization without disclosing the data can make the training process converge to the optimal solution more easily. To solve these problems, Zhu et al [5] proposed a privacy-preserving ML training framework, which contains multiple secure training protocols for the aggregation scenario and defends security under collusion situations. However, the scheme in [5] does not make a global data normalization.…”

Section: Introductionmentioning

confidence: 99%

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ePMLF: Efficient and Privacy-Preserving Machine Learning Framework Based on Fog Computing

Zhao

Xie

Cheng

et al. 2023

International Journal of Intelligent Systems

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With the continuous improvement of computation and communication capabilities, the Internet of Things (IoT) plays a vital role in many intelligent applications. Therefore, IoT devices generate a large amount of data every day, which lays a solid foundation for the success of machine learning. However, the strong privacy requirements of the IoT data make its machine learning very difficult. To protect data privacy, many privacy-preserving machine learning schemes have been proposed. At present, most schemes only aim at specific models and lack general solutions, which is not an ideal solution in engineering practice. In order to meet this challenge, we propose an efficient and privacy-preserving machine learning training framework (ePMLF) in a fog computing environment. The ePMLF framework can let the software service provider (SSP) perform privacy-preserving model training with the data on the fog nodes. The security of the data on the fog nodes can be protected and the model parameters can only be obtained by SSP. The proposed secure data normalization method in the framework further improves the accuracy of the training model. Experimental analysis shows that our framework significantly reduces the computation and communication overhead compared with the existing scheme.

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Privacy-Preserving Machine Learning Training in IoT Aggregation Scenarios

Cited by 24 publications

References 47 publications

A Survey of Machine and Deep Learning Methods for Privacy Protection in the Internet of Things

A Survey of Machine and Deep Learning Methods for Privacy Protection in the Internet of Things

A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions

ePMLF: Efficient and Privacy-Preserving Machine Learning Framework Based on Fog Computing

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