An efficient reversible privacy-preserving data mining technology over data streams.
With the popularity of smart handheld devices and the emergence of cloud computing, users and companies can save various data, which may contain private data, to the cloud. Topics relating to data security have therefore received much attention. This study focuses on data stream environments and uses the concept of a sliding window to design a reversible privacy-preserving technology to process continuous data in real time, known as a continuous reversible privacy-preserving (CRP) algorithm. Data with CRP algorithm protection can be accurately recovered through a data recovery process. In addition, by using an embedded watermark, the integrity of the data can be verified. The results from the experiments show that, compared to existing algorithms, CRP is better at preserving knowledge and is more effective in terms of reducing information loss and privacy disclosure risk. In addition, it takes far less time for CRP to process continuous data than existing algorithms. As a result, CRP is confirmed as suitable for data stream environments and fulfills the requirements of being lightweight and energy-efficient for smart handheld devices.
Department of Information Management, National Taichung University of Science and Technology, Taichung, Taiwan.
Chen-Yi
CY
Lin
Department of Information Engineering and Computer Science, Feng Chia University, Taichung, Taiwan.
Yuan-Hung
YH
Kao
Department of Information Engineering and Computer Science, Feng Chia University, Taichung, Taiwan.
Wei-Bin
WB
Lee
Department of Distribution Management, National Taichung University of Science and Technology, Taichung, Taiwan.
Rong-Chang
RC
Chen
Cloud computing; Data protection; Data streams; Sliding window