The Research of Data Mining Technology of Privacy Preserving in Sharing Platform of Internet of Things

2012 ◽  
pp. 481-485 ◽  
Author(s):  
Luyu Chen ◽  
Guangwei Ren
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Privacy Preserving ◽  
Mining Technology

Privacy Preserving Data Mining, Concepts, Techniques, and Evaluation Methodologies

2008 ◽  
pp. 2379-2401 ◽  
Author(s):  
Igor Nai Fovino
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Privacy Preserving ◽  
Research Field ◽  
Sensitive Information ◽  
Discovery Process ◽  
Privacy Preserving Data Mining ◽  
Mining Technology ◽  
Evaluation Methodologies ◽  
New Research

Intense work in the area of data mining technology and in its applications to several domains has resulted in the development of a large variety of techniques and tools able to automatically and intelligently transform large amounts of data in knowledge relevant to users. However, as with other kinds of useful technologies, the knowledge discovery process can be misused. It can be used, for example, by malicious subjects in order to reconstruct sensitive information for which they do not have an explicit access authorization. This type of “attack” cannot easily be detected, because, usually, the data used to guess the protected information, is freely accessible. For this reason, many research efforts have been recently devoted to addressing the problem of privacy preserving in data mining. The mission of this chapter is therefore to introduce the reader in this new research field and to provide the proper instruments (in term of concepts, techniques and example) in order to allow a critical comprehension of the advantages, the limitations and the open issues of the Privacy Preserving Data Mining Techniques.

Privacy Preserving Data Mining, Concepts, Techniques, and Evaluation Methodologies

2008 ◽  
pp. 277-301
Author(s):  
Igor Nai Fovino
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Privacy Preserving ◽  
Research Field ◽  
Sensitive Information ◽  
Discovery Process ◽  
Privacy Preserving Data Mining ◽  
Mining Technology ◽  
Evaluation Methodologies ◽  
New Research

Intense work in the area of data mining technology and in its applications to several domains has resulted in the development of a large variety of techniques and tools able to automatically and intelligently transform large amounts of data in knowledge relevant to users. However, as with other kinds of useful technologies, the knowledge discovery process can be misused. It can be used, for example, by malicious subjects in order to reconstruct sensitive information for which they do not have an explicit access authorization. This type of “attack” cannot easily be detected, because, usually, the data used to guess the protected information, is freely accessible. For this reason, many research efforts have been recently devoted to addressing the problem of privacy preserving in data mining. The mission of this chapter is therefore to introduce the reader in this new research field and to provide the proper instruments (in term of concepts, techniques and example) in order to allow a critical comprehension of the advantages, the limitations and the open issues of the Privacy Preserving Data Mining Techniques.

Design of College English Process Evaluation System Based on Data Mining Technology and Internet of Things

2020 ◽  
Vol 16 (2) ◽  
pp. 18-33 ◽  
Author(s):  
Hongli Lou
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Process Evaluation ◽  
Evaluation System ◽  
Current Situation ◽  
The Internet ◽  
Mining Technology ◽  
College English ◽  
Procedural Evaluation ◽  
The Internet Of Things

This article proposes a new idea for the current situation of procedural evaluation of college English based on Internet of Things. The Internet of Things is used to obtain the intelligent data to enhance the teaching flexibility. The data generated during the process of procedural evaluation is carefully analyzed through data mining to infer whether the teacher's procedural evaluation in English teaching can be satisfied.

The Study of Privacy Preserving Data Mining Technology for Information Security

2014 ◽  
Vol 556-562 ◽  
pp. 3532-3535
Author(s):  
Heng Li ◽  
Xue Fang Wu
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Rapid Development ◽  
Privacy Preserving ◽  
Future Research ◽  
Privacy Preserving Data Mining ◽  
Mining Technology ◽  
Network Database ◽  
Use Of Data

With the rapid development of computer technology and the popularity of the network, database scale, scope and depth of the constantly expanding, which has accumulated vast amounts of different forms of stored data. The use of data mining technology can access valuable information from a lot of data. Privacy preserving has been one of the greater concerns in data mining. Privacy preserving data mining has a rapid development in a short year. But it still faces many challenges in the future. A number of methods and techniques have been developed for privacy preserving data mining. This paper analyzed the representative techniques for privacy preservation. Finally the present problems and directions for future research are discussed.

scholarly journals Towards an Efficient Privacy-Preserving Decision Tree Evaluation Service in the Internet of Things

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 103 ◽  
Author(s):  
Lin Liu ◽  
Jinshu Su ◽  
Baokang Zhao ◽  
Qiong Wang ◽  
Jinrong Chen ◽  
...  
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Decision Tree ◽  
Secret Sharing ◽  
Cloud Model ◽  
Privacy Preserving ◽  
The Internet ◽  
Fast Development ◽  
The Internet Of Things ◽  
Tree Evaluation

With the fast development of the Internet of Things (IoT) technology, normal people and organizations can produce massive data every day. Due to a lack of data mining expertise and computation resources, most of them choose to use data mining services. Unfortunately, directly sending query data to the cloud may violate their privacy. In this work, we mainly consider designing a scheme that enables the cloud to provide an efficient privacy-preserving decision tree evaluation service for resource-constrained clients in the IoT. To design such a scheme, a new secure comparison protocol based on additive secret sharing technology is proposed in a two-cloud model. Then we introduce our privacy-preserving decision tree evaluation scheme which is designed by the secret sharing technology and additively homomorphic cryptosystem. In this scheme, the cloud learns nothing of the query data and classification results, and the client has no idea of the tree. Moreover, this scheme also supports offline users. Theoretical analyses and experimental results show that our scheme is very efficient. Compared with the state-of-art work, both the communication and computational overheads of the newly designed scheme are smaller when dealing with deep but sparse trees.

WIT120 data mining technology based on internet of things

2019 ◽  
Vol 23 (4) ◽  
pp. 680-688
Author(s):  
Qingyuan Zhou ◽  
Zongming Zhang ◽  
Yuancong Wang
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Mining Technology
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