Privacy-Preserved Data Sharing Towards Multiple Parties in Industrial IoTs

Zheng, Xu; Cai, Zhipeng

doi:10.1109/jsac.2020.2980802

Cited by 355 publications

(146 citation statements)

References 36 publications

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“…Additional challenges originate from the industrial setting. In contrast to the analysis of personal data, companies impose very strict usage rules [94], e.g., molds are regularly owned by customers of injection molding manufacturers and only loaned to the companies for production. Therefore, any unintentional disclosure of intellectual property has to be strictly avoided.…”

Section: Scenario Challengesmentioning

confidence: 99%

“…Establishing trust in a single third party is highly unlikely as companies strive to limit the threat of data leaks [72]. Regardless, creating new business models for companies with existing data repositories could incentivize their participation [94], i.e., a privacy-preserving exchange could open up a new stream of revenue for data-providing companies. Consequentially, a suitable billing mechanism is required.…”

Section: Scenario Challengesmentioning

confidence: 99%

“…Data Provider(s). Given that potential providers invest resources when collecting parameter records [1,30,38] and possibly share their know-how with business partners or competitors, they are only willing to contribute against compensation [94], e.g., payments, and despite a required participation overhead. Furthermore, the data provider's identity and valuable provided data must be protected, i.e., no third party may get access to all records.…”

Section: Entities and Trust Assumptionsmentioning

confidence: 99%

See 2 more Smart Citations

Privacy-Preserving Production Process Parameter Exchange

Pennekamp

Buchholz

Lockner

et al. 2020

Annual Computer Security Applications Conference

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Section: Scenario Challengesmentioning

confidence: 99%

Section: Scenario Challengesmentioning

confidence: 99%

Section: Entities and Trust Assumptionsmentioning

confidence: 99%

See 1 more Smart Citation

Privacy-Preserving Production Process Parameter Exchange

Pennekamp

Buchholz

Lockner

et al. 2020

Annual Computer Security Applications Conference

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“…Blockchain is a distributed database technology with features such as decentralization, traceability, transparency, and nontampering [20]- [22]. Because of its ability to significantly improve trading security, it also has been used to construct power trading mechanisms [23]- [25]. Pieroni et al [26] proposed a blockchain-based P2P power trading mechanism that can bring more possibilities to the power flow.…”

Section: Introductionmentioning

confidence: 99%

A Blockchain-Enabled Secure Power Trading Mechanism for Smart Grid Employing Wireless Networks

Liu

Wang

et al. 2020

IEEE Access

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The rapid growth of renewable energy has increased the requirements of the smart grid for communication and processing capabilities. As an effective solution to collect and manage information, the wireless network can improve the efficiency of renewable energy management. But due to the wireless data transmission risk and centralized power trading, the smart grid employing wireless networks cannot guarantee the security of the electricity market and the high consumption of renewable energy. Recently, as an emerging data-sharing technology, the blockchain has attracted widespread attention and is considered to have the potential to solve above problems. In this paper, we propose a blockchain-enabled secure power trading mechanism for the smart grid employing wireless networks. The blockchain is introduced to record power data collected by the wireless network, and the smart contract can make reasonable trading decisions based on this. The dual-chain structure composed of local energy trading blockchain and renewable energy trading blockchain improves the efficiency of power trading and renewable energy consumption. To strengthen the stability of renewable energy producers and expand their scale, we also design a blockchainenabled renewable energy incentive mechanism. Finally, the modified Southern California Edison 56 bus test feeder model validated our mechanism.

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“…IoT applications and services span almost all the economic and social sectors, including environment monitoring, smart industry, public safety, smart medical systems, smart grid, smart agriculture, smart transportation, behavioral patterns, etc. [10,36,37].…”

Section: Introductionmentioning

confidence: 99%

Seed-free Graph De-anonymiztiation with Adversarial Learning

Luo

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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The huge amount of graph data are published and shared for research and business purposes, which brings great benefit for our society. However, user privacy is badly undermined even though user identity can be anonymized. Graph de-anonymization to identify nodes from an anonymized graph is widely adopted to evaluate users' privacy risks. Most existing de-anonymization methods which are heavily reliant on side information (e.g., seeds, user profiles, community labels) are unrealistic due to the difficulty of collecting this side information. A few graph de-anonymization methods only using structural information, called seed-free methods, have been proposed recently, which mainly take advantage of the local and manual features of nodes while overlooking the global structural information of the graph for de-anonymization. In this paper, a seed-free graph de-anonymization method is proposed, where a deep neural network is adopted to learn features and an adversarial framework is employed for node matching. To be specific, the latent representation of each node is obtained by graph autoencoder. Furthermore, an adversarial learning model is proposed to transform the embedding of the anonymized graph to the latent space of auxiliary graph embedding such that a linear mapping can be derived from a global perspective. Finally, the most similar node pairs in the latent space as the anchor nodes are utilized to launch propagation to de-anonymize all the remaining nodes. The extensive experiments on some real datasets demonstrate that our method is comparative with the seed-based approaches and outperforms the start-of-the-art seed-free method significantly.

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Privacy-Preserved Data Sharing Towards Multiple Parties in Industrial IoTs

Cited by 355 publications

References 36 publications

Privacy-Preserving Production Process Parameter Exchange

Privacy-Preserving Production Process Parameter Exchange

A Blockchain-Enabled Secure Power Trading Mechanism for Smart Grid Employing Wireless Networks

Seed-free Graph De-anonymiztiation with Adversarial Learning

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