Enhancing Privacy and Availability for Data Clustering in Intelligent Electrical Service of IoT

Xiong, Jinbo; Ren, Jian; Chen, Lei; Yao, Zhiqiang; Lin, Mingwei; Wang, Dapeng; Niu, Ben

doi:10.1109/jiot.2018.2842773

Cited by 174 publications

(90 citation statements)

References 35 publications

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“…Various research studies have been conducted on a prediction system for power-related data [77][78][79][80][81][82]. In recent years, active research efforts have been made to investigate pattern mining such as power demand and patterns and analyze outlier for the identification of defective data from the collected data [83,84].…”

Section: Electric Power Prediction Systemmentioning

confidence: 99%

A Novel on Transmission Line Tower Big Data Analysis Model Using Altered K-means and ADQL

Jung

2019

Sustainability

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This study sought to propose a big data analysis and prediction model for transmission line tower outliers to assess when something is wrong with transmission line tower big data based on deep reinforcement learning. The model enables choosing automatic cluster K values based on non-labeled sensor big data. It also allows measuring the distance of action between data inside a cluster with the Q-value representing network output in the altered transmission line tower big data clustering algorithm containing transmission line tower outliers and old Deep Q Network. Specifically, this study performed principal component analysis to categorize transmission line tower data and proposed an automatic initial central point approach through standard normal distribution. It also proposed the A-Deep Q-Learning algorithm altered from the deep Q-Learning algorithm to explore policies based on the experiences of clustered data learning. It can be used to perform transmission line tower outlier data learning based on the distance of data within a cluster. The performance evaluation results show that the proposed model recorded an approximately 2.29%~4.19% higher prediction rate and around 0.8% ~ 4.3% higher accuracy rate compared to the old transmission line tower big data analysis model.

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Section: Electric Power Prediction Systemmentioning

confidence: 99%

A Novel on Transmission Line Tower Big Data Analysis Model Using Altered K-means and ADQL

Jung

2019

Sustainability

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“…In the process of clustering analysis, a large amount of user-based privacy data, such as geographical location, electricity consumption data, and spatiotemporal sensing data, is collected and analyzed. [7][8][9] The security and privacy of this data depends on the security of cloud services.…”

Section: Introductionmentioning

confidence: 99%

“…6 Clustering, as one of the important research methods of data mining, aims to divide data objects into several clusters such that object similarity in a cluster is high, while the similarity between each cluster is low.In the process of clustering analysis, a large amount of user-based privacy data, such as geographical location, electricity consumption data, and spatiotemporal sensing data, is collected and analyzed. [7][8][9] The security and privacy of this data depends on the security of cloud services.The sensitive data is directly outsourced to the cloud server for calculation, at which point the user's privacy may be leaked if the cloud service provider is malicious or dishonest. If multiple users collude with each other, they combine their own information to calculate their respective distances and then calculate the cluster centers by distance.…”

mentioning

confidence: 99%

A privacy‐preserving density peak clustering algorithm in cloud computing

Sun

Liu

et al. 2020

Concurrency and Computation

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Aiming at preventing the privacy disclosure of sensitive information, issues related to privacy protection in cloud computing have attracted the interest of researchers. To protect the privacy of users during clustering in a cloud computing environment, we present a privacy-preserving density peak clustering (PPDPC) algorithm that neither discloses personal privacy information nor leaks the cluster centers. Our scheme contains two steps of density peak clustering: First, a cloud service provider calculates the cluster centers without knowing each participant's private data and without disclosing any cluster center information to the other participants, and second, participant allocation is secure and every participant is prevented from identifying the other members of the same cluster. Security analysis and comparison experiments show that the proposed PPDPC algorithm not only obtains good accuracy with respect to density peak clustering but also resists collusion attacks even if the cloud service provider is collaborating with all except one participant. Both theoretical analysis and experimental results confirm the security and accuracy of our method. KEYWORDS cloud computing, data mining, density peak clustering, homomorphic encryption, privacy preservation INTRODUCTIONWith the rapid development of mobile social networks and computer technology, all types of mobile terminals and servers have started generating huge amounts of data at all times, presenting a serious challenge to the computing ability of enterprises. 1,2 Cloud computing technology, which is used to address this challenge, is growing. More and more enterprises are storing data in cloud servers to save economic costs; its powerful computing power is convenient for handling huge amounts of data. [3][4][5] Additionally, data mining technology can help users analyze and extract key value information from a large amount of data in scientific research and business applications. The analysis of these data allows the prediction of future development trends and directions. 6 Clustering, as one of the important research methods of data mining, aims to divide data objects into several clusters such that object similarity in a cluster is high, while the similarity between each cluster is low.In the process of clustering analysis, a large amount of user-based privacy data, such as geographical location, electricity consumption data, and spatiotemporal sensing data, is collected and analyzed. [7][8][9] The security and privacy of this data depends on the security of cloud services.The sensitive data is directly outsourced to the cloud server for calculation, at which point the user's privacy may be leaked if the cloud service provider is malicious or dishonest. If multiple users collude with each other, they combine their own information to calculate their respective distances and then calculate the cluster centers by distance. If user's privacy and cluster centers are disclosed, serious consequences can result.Therefore, the development of a data mining techn...

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“…Second, in a mobile crowdsensing network [9], the collected sensing data may contain a significant amount of sensitive and private information with the risk of private data leakage. Therefore, users anticipate that effective measures are taken to protect their privacy when sensing data is uploaded to the service providers [11]. Third, there may be malicious activities in the course of the data transactions between the mediator and the service provider, possibly leading to loss of profits.…”

Section: Introductionmentioning

confidence: 99%

Achieving Incentive, Security, and Scalable Privacy Protection in Mobile Crowdsensing Services

Xiong

Chen

et al. 2018

Wireless Communications and Mobile Computing

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Mobile crowdsensing as a novel service schema of the Internet of Things (IoT) provides an innovative way to implement ubiquitous social sensing. How to establish an effective mechanism to improve the participation of sensing users and the authenticity of sensing data, protect the users' data privacy, and prevent malicious users from providing false data are among the urgent problems in mobile crowdsensing services in IoT. These issues raise a gargantuan challenge hindering the further development of mobile crowdsensing. In order to tackle the above issues, in this paper, we propose a reliable hybrid incentive mechanism for enhancing crowdsensing participations by encouraging and stimulating sensing users with both reputation and service returns in mobile crowdsensing tasks. Moreover, we propose a privacy preserving data aggregation scheme, where the mediator and/or sensing users may not be fully trusted. In this scheme, differential privacy mechanism is utilized through allowing different sensing users to add noise data, then employing homomorphic encryption for protecting the sensing data, and finally uploading ciphertext to the mediator, who is able to obtain the collection of ciphertext of the sensing data without actual decryption. Even in the case of partial sensing data leakage, differential privacy mechanism can still ensure the security of the sensing user's privacy. Finally, we introduce a novel secure multiparty auction mechanism based on the auction game theory and secure multiparty computation, which effectively solves the problem of prisoners' dilemma incurred in the sensing data transaction between the service provider and mediator. Security analysis and performance evaluation demonstrate that the proposed scheme is secure and efficient.

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Enhancing Privacy and Availability for Data Clustering in Intelligent Electrical Service of IoT

Cited by 174 publications

References 35 publications

A Novel on Transmission Line Tower Big Data Analysis Model Using Altered K-means and ADQL

A Novel on Transmission Line Tower Big Data Analysis Model Using Altered K-means and ADQL

A privacy‐preserving density peak clustering algorithm in cloud computing

Achieving Incentive, Security, and Scalable Privacy Protection in Mobile Crowdsensing Services

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