|
Title:
|
TIME SERIES DATA PUBLISHING AND MINING SYSTEM |
|
Author(s):
|
Ye Zhu , Yongjian Fu , Huirong Fu |
|
ISBN:
|
978-972-8924-88-1 |
|
Editors:
|
Ajith P. Abraham |
|
Year:
|
2009 |
|
Edition:
|
Single |
|
Keywords:
|
Privacy-preserving data mining, time series data mining. |
|
Type:
|
Full Paper |
|
First Page:
|
95 |
|
Last Page:
|
102 |
|
Language:
|
English |
|
Cover:
|
|
|
Full Contents:
|
click to dowload 
|
|
Paper Abstract:
|
Time series data mining poses new challenges to privacy. Through extensive experiments, we find that existing privacypreserving
techniques such as aggregation and adding random noise are insufficient due to privacy attacks such as data
flow separation attack. We also present a general model for publishing and mining time series data and its privacy issues.
Based on the model, we propose a spectrum of privacy preserving methods. For each method, we study its effects on
classification accuracy, aggregation error, and privacy leak. Experiments are conducted to evaluate the performance of
the methods. Our results show that the methods can effectively preserve privacy without losing much classification
accuracy and within a specified limit of aggregation error. |
|
|
|
|
|
|
