| Title |
Privacy Preserving Data Mining Classifier for Smart City Applications |
| ID_Doc |
36166 |
| Authors |
Amma, NGN; Dhanaseelan, FR |
| Title |
Privacy Preserving Data Mining Classifier for Smart City Applications |
| Year |
2018 |
| Published |
|
| DOI |
|
| Abstract |
The life style of people are changing day by day due to the paradigm shift in technology development so as the living environment is changed as smart cities. As smartness increases, privacy issues also increases. This leads to life threatening problems and there is a need to protect the sensitive data generated from smart environment. The issue of protecting the sensitive data as well as classifying the sensitive data is addressed in this paper. The private data is encrypted using homomorphic encryption and Naive Bayes algorithm is used to classify the data. Experiments are conducted on three datasets, viz., Road Traffic Data, Pollution Data, and Parking Data provided by City Pulse Smart City Dataset. It is seen that the proposed approach is promising compared to existing methods and achieved accuracy of 89.24%, 92.17%, and 86.39% for Road Traffic Data, Pollution Data, and Parking Data respectively. |
| Author Keywords |
data mining; homomorphic encryption; naive bayes; privacy preserving; smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000475971500117 |
| WoS Category |
Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications |
| Research Area |
Computer Science; Engineering; Telecommunications |
| PDF |
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