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Privacy-preserving clustering for big data in cyber-physical-social systems: Survey and perspectives
- Source :
- Information Sciences. 515:132-155
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- Clustering technique plays a critical role in data mining, and has received great success to solve application problems like community analysis, image retrieval, personalized recommendation, activity prediction, etc. This paper first reviews the traditional clustering and the emerging multiple clustering methods, respectively. Although the existing methods have superior performance on some small or certain datasets, they fall short when clustering is performed on CPSS big data because of the high cost of computation and storage. With the powerful cloud computing, this challenge can be effectively addressed, but it brings enormous threat to individual or company’s privacy. Currently, privacy preserving data mining has attracted widespread attention in academia. Compared to other reviews, this paper focuses on privacy preserving clustering technique, guiding a detailed overview and discussion. Specifically, we introduce a novel privacy-preserving tensor-based multiple clustering, propose a privacy-preserving tensor-based multiple clustering analytic and service framework, and give an illustrated case study on the public transportation dataset. Furthermore, we indicate the remaining challenges of privacy preserving clustering and discuss the future significant research in this area.
- Subjects :
- Information Systems and Management
Computer science
business.industry
05 social sciences
Big data
Cyber-physical system
050301 education
Cloud computing
02 engineering and technology
Data science
Computer Science Applications
Theoretical Computer Science
Artificial Intelligence
Control and Systems Engineering
Social system
Tensor (intrinsic definition)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
business
Cluster analysis
0503 education
Image retrieval
Software
Subjects
Details
- ISSN :
- 00200255
- Volume :
- 515
- Database :
- OpenAIRE
- Journal :
- Information Sciences
- Accession number :
- edsair.doi...........895e00cfbeb2f48abee5784dcc0ebc72