A privacy-preserving resource trading scheme for Cloud Manufacturing with edge-PLCs in IIoT

Liu, Peng; Liu, Kun; Fu, Tingting; Zhang, Yifan; Hu, Jia

doi:10.1016/j.sysarc.2021.102104

Cited by 16 publications

(6 citation statements)

References 25 publications

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“…Moreover, data security issues in intelligent manufacturing also pose potential hazards. [ 29 ] In intelligent manufacturing, a large amount of data is collected and stored, such as production data, worker data, and equipment data. If these data are attacked or leaked by hackers, it may cause serious impacts on a company business secrets and customer privacy, even leading to financial loss.…”

Section: Potential Hazards Of Artificial Intelligence In the Areas Of...mentioning

confidence: 99%

Ethical perspective on AI hazards to humans: A review

Chen,

Luo

et al. 2023

Medicine

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This article explores the potential ethical hazards of artificial intelligence (AI) on society from an ethical perspective. We introduce the development and application of AI, emphasizing its potential benefits and possible negative impacts. We particularly examine the application of AI in the medical field and related ethical and legal issues, and analyze potential hazards that may exist in other areas of application, such as autonomous driving, finance, and security. Finally, we offer recommendations to help policymakers, technology companies, and society as a whole address the potential hazards of AI. These recommendations include strengthening regulation and supervision of AI, increasing public understanding and awareness of AI, and actively exploring how to use the advantages of AI to achieve a more just, equal, and sustainable social development. Only by actively exploring the advantages of AI while avoiding its negative impacts can we better respond to future challenges.

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Section: Potential Hazards Of Artificial Intelligence In the Areas Of...mentioning

confidence: 99%

Ethical perspective on AI hazards to humans: A review

Chen,

Luo

et al. 2023

Medicine

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“…Finally, the cloud server will match the searchable ciphertext with the query trapdoor one-by-one, and send the matching result to the data user. Since SE was proposed, it has been applied in many fields, such as online medical care [5,6], cloud manufacturing [7], etc.…”

Section: Introductionmentioning

confidence: 99%

“…In many instances of cloud storage applications, counting the frequency of keyword queries is important to improve the search experience of users. For example, in a cloud manufacturing system [7], an upstream supplying plant (the data owner) will encrypt some production data from its workshop and store the ciphertext on a cloud server. Subsequently, the service platform providing the ciphertext retrieval service can respond to the search request of the purchasing company (the data users), and provide the supply factory that meets their needs.…”

Section: Introductionmentioning

confidence: 99%

How Can We Achieve Query Keyword Frequency Analysis in Privacy-Preserving Situations?

Zhu

Zhou

et al. 2023

Future Internet

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Recently, significant progress has been made in the field of public key encryption with keyword search (PEKS), with a focus on optimizing search methods and improving the security and efficiency of schemes. Keyword frequency analysis is a powerful tool for enhancing retrieval services in explicit databases. However, designing a PEKS scheme that integrates keyword frequency analysis while preserving privacy and security has remained challenging, as it may conflict with some of the security principles of PEKS. In this paper, we propose an innovative scheme that introduces a security deadline to query trapdoors through the use of timestamps. This means that the keywords in the query trapdoor can only be recovered after the security deadline has passed. This approach allows for keyword frequency analysis of query keywords without compromising data privacy and user privacy, while also providing protection against keyword-guessing attacks through the dual-server architecture of our scheme. Moreover, our scheme supports multi-keyword queries in multi-user scenarios and is highly scalable. Finally, we evaluate the computational and communication efficiency of our scheme, demonstrating its feasibility in practical applications.

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“…Then, this kind of data can be frequently uploaded to the cloud system by using the intermediate devices of servers/gateways that are located in the specified area. But, this type of communication system [10][11][12] is more susceptible to the security vulnerabilities, because the untrusted cloud may intend to access the large amount of private or confidential data. Hence, it is more essential to provide security to these systems for ensuring the privacy and confidentiality [13,14] of the industrial data.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Searchable Encryption and Delegation Mechanism with Forward Privacy for Improving the Security of Industrial Internet of Things-Cloud Systems

Venkatesan

Rajeshwari

Ramakrishnan

2022

Preprint

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Improving the security and privacy of the industrial data stored in cloud systems is one of the demanding and challenging problem in the recent days, due to the increased utilization rate of IIoT systems. For this purpose, various lightweight cryptographic models are developed in the conventional works, which are mainly focusing on ensuring the data security against the adversaries. However, it limits with the main problems of increased computational complexity, cost complexity, and time consumption, which degrades the performance of entire system. Hence, this paper objects to design an advanced cryptography models, named as Lightweight Searchable Encryption and Delegation (LSED) methodology with forward privacy for enabling the secured data storage and retrieval operations in IIoT-cloud systems. Moreover, the searchable encryption incorporated with the forward privacy property considers that the previously stored trapdoor is invalid for the recently updated cipher data, which efficiently enhances the security of IIoT data against the adversaries. During performance evaluation, there are various evaluation metrics have been utilized for validating the results of both existing and proposed searchable encryption techniques.

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A privacy-preserving resource trading scheme for Cloud Manufacturing with edge-PLCs in IIoT

Cited by 16 publications

References 25 publications

Ethical perspective on AI hazards to humans: A review

Ethical perspective on AI hazards to humans: A review

How Can We Achieve Query Keyword Frequency Analysis in Privacy-Preserving Situations?

A Lightweight Searchable Encryption and Delegation Mechanism with Forward Privacy for Improving the Security of Industrial Internet of Things-Cloud Systems

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