Mutual Privacy Preserving $k$ -Means Clustering in Social Participatory Sensing

Xing, Kai; Hu, Chunqiang; Yu, Jiguo; Cheng, Xiuzhen; Fengjuan, Zhang

doi:10.1109/tii.2017.2695487

Cited by 109 publications

(47 citation statements)

References 21 publications

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“…1) Data Privacy: Analytics methods for data privacy is a new perspective except for communication architecture, such as the design of privacy-preserving clustering algorithm [211] and PCA algorithm [212]. A strategic battery storage charging and discharging schedule was proposed in [213] to mask the actual electricity consumption behavior and alleviate the privacy concerns.…”

Section: E Data Privacy and Securitymentioning

confidence: 99%

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Wang

Chen

Hong

et al. 2019

IEEE Trans. Smart Grid

923

370

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The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue. To date, substantial works have been conducted on smart meter data analytics. To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics. Following the three stages of analytics, namely, descriptive, predictive and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management. We also review the techniques and methodologies adopted or developed to address each application. In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security.

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Section: E Data Privacy and Securitymentioning

confidence: 99%

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Wang

Chen

Hong

et al. 2019

IEEE Trans. Smart Grid

923

370

View full text Add to dashboard Cite

show abstract

“…A safe allocation sample pointing to the nearest clustering center algorithm was proposed by Xing. 19 Vaidya and Clifton 20 proposed a privacy-preserving k-means algorithm for vertically segmenting data.…”

Section: Related Workmentioning

confidence: 99%

“…Without loss of generality, we assume that these n-1 participants are denoted by a 1 , a 2 , … , a n−1 ; they combine their information to compute the distance, after which they can calculate the cluster center according to distance U 1 . All of these n-1 colluding participants can construct the following (19) by using the received data from cloud service A and their own data.…”

Section: Collusion Among the Participantsmentioning

confidence: 99%

“…For the participants, values {a T 1 a 2 , a Ta 3 1 , … , a T 1 a n , a T 2 a 3 , a T 2 a 4 , … , a T 2 a n , … , a T n−2 a n−1 } are unknown in (19) and are calculated by the cloud service through the private key. Then, (n−1)(n−2) 2 equations can be obtained when n-1 collusion participants combine all available information; however, there exist n(n−1) 2 unknown numbers.…”

Section: Collusion Among the Participantsmentioning

confidence: 99%

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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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“…However, SPRING depends on a trusted third party (the agent). In [6][7][8][9][10], homomorphic encryption [11][12][13] is employed to hid each bidder's bidding values with a vector of cipher texts, and ensures the auctioneer to figure out the maximum value, and charge the bidders securely. However, the homomorphic encryption has a higher computational cost, which is not practical now.…”

Section: Introductionmentioning

confidence: 99%

Privacy-preserving combinatorial auction without an auctioneer

Mei

et al. 2018

J Wireless Com Network

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Combinatorial auctions are employed into many applications such as spectrum auctions held by the Federal Communications Commission (FCC). A crucial problem in such auctions is the lack of secure and efficiency mechanism to protect the privacy of the bidding prices and to ensure data security. To solve the problem, we propose an approach to represent the price as a polynomial's degree based on verifiable secret sharing. So, we can obtain the two polynomials's degree maximum/sum by the degree of the two polynomial's degree sum/product. In the protocol, the bidders' information is hidden. The auctioneers can receive the shares without a secure channel, so our protocol is more applicable to more scenarios. The scheme can resist the collusion attack, passive attack and so on.

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Mutual Privacy Preserving $k$ -Means Clustering in Social Participatory Sensing

Cited by 109 publications

References 21 publications

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

A privacy‐preserving density peak clustering algorithm in cloud computing

Privacy-preserving combinatorial auction without an auctioneer

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