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Security and Communication Networks
Volume 2017, Article ID 3847092, 12 pages
https://doi.org/10.1155/2017/3847092
Research Article

Parameterization of LSB in Self-Recovery Speech Watermarking Framework in Big Data Mining

1School of Electrical and Information Engineering, Tianjin University, Tianjin, China
2School of Mathematics, Tianjin University, Tianjin, China
3School of Computer Software, Tianjin University, Tianjin, China
4Commonwealth Scientific and Industrial Research Organization, Campbell, ACT, Australia

Correspondence should be addressed to Wenhuan Lu; nc.ude.ujt@nauhnew

Received 18 August 2017; Accepted 9 October 2017; Published 12 November 2017

Academic Editor: Lianyong Qi

Copyright © 2017 Shuo Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The privacy is a major concern in big data mining approach. In this paper, we propose a novel self-recovery speech watermarking framework with consideration of trustable communication in big data mining. In the framework, the watermark is the compressed version of the original speech. The watermark is embedded into the least significant bit (LSB) layers. At the receiver end, the watermark is used to detect the tampered area and recover the tampered speech. To fit the complexity of the scenes in big data infrastructures, the LSB is treated as a parameter. This work discusses the relationship between LSB and other parameters in terms of explicit mathematical formulations. Once the LSB layer has been chosen, the best choices of other parameters are then deduced using the exclusive method. Additionally, we observed that six LSB layers are the limit for watermark embedding when the total bit layers equaled sixteen. Experimental results indicated that when the LSB layers changed from six to three, the imperceptibility of watermark increased, while the quality of the recovered signal decreased accordingly. This result was a trade-off and different LSB layers should be chosen according to different application conditions in big data infrastructures.