Harmonic wavelets not only possess the traditional advantages of a wavelet function,they also have other merits such as clear expressions,more flexible time-frequency divisions,a simple transformation algorithm,a fine...Harmonic wavelets not only possess the traditional advantages of a wavelet function,they also have other merits such as clear expressions,more flexible time-frequency divisions,a simple transformation algorithm,a finer box-like frequency spectrum and others.Given the frequency distribution characteristics of the nondestructive testing signals from a rockbolt support system and based on the discrete harmonic wavelet transformation theory,we have effectively abstracted signals from frequency ranges concerned by removing useless high and low frequency signals from the testing signals of the rockbolt support system and obtained filtered signals with a reconstruction algorithm of harmonic wavelets.Finally,we applied the harmonic wavelet transformation in filtering analog signals and measured response signals of rockbolts.The results indicate that harmonic wavelets also have excellent filtering characteristics.展开更多
Multidimensional data provides enormous opportunities in a variety of applications. Recent research has indicated the failure of existing sanitization techniques （e.g., k-anonymity） to provide rigorous privacy guara...Multidimensional data provides enormous opportunities in a variety of applications. Recent research has indicated the failure of existing sanitization techniques （e.g., k-anonymity） to provide rigorous privacy guarantees. Privacy- preserving multidimensional data publishing currently lacks a solid theoretical foundation. It is urgent to develop new techniques with provable privacy guarantees, e-Differential privacy is the only method that can provide such guarantees. In this paper, we propose a multidimensional data publishing scheme that ensures c-differential privacy while providing accurate results for query processing. The proposed solution applies nonstandard wavelet transforms on the raw multidimensional data and adds noise to guarantee c-differential privacy. Then, the scheme processes arbitrarily queries directly in the noisy wavelet- coefficient synopses of relational tables and expands the noisy wavelet coefficients back into noisy relational tuples until the end result of the query. Moreover, experimental results demonstrate the high accuracy and effectiveness of our approach.展开更多
基金Financial support for this work provided by the National Basic Research Program of China (No.2007CB209400)the 111 Project of China (No.B07028)+2 种基金the Key Program of National Natural Science Foundation of China(No.50834004)the National Natural Science Foundation of China (No.50874104)the Natural Science Foundation of Jiangsu Province(No.BK2006040)
文摘Harmonic wavelets not only possess the traditional advantages of a wavelet function,they also have other merits such as clear expressions,more flexible time-frequency divisions,a simple transformation algorithm,a finer box-like frequency spectrum and others.Given the frequency distribution characteristics of the nondestructive testing signals from a rockbolt support system and based on the discrete harmonic wavelet transformation theory,we have effectively abstracted signals from frequency ranges concerned by removing useless high and low frequency signals from the testing signals of the rockbolt support system and obtained filtered signals with a reconstruction algorithm of harmonic wavelets.Finally,we applied the harmonic wavelet transformation in filtering analog signals and measured response signals of rockbolts.The results indicate that harmonic wavelets also have excellent filtering characteristics.
基金the National Basic Research Program of China under Grant 2013CB338004,Doctoral Program of Higher Education of China under Grant No.20120073120034,National Natural Science Foundation of China under Grants No.61070204,61101108,and National S&T Major Program under Grant No.2011ZX03002-005-01
文摘Multidimensional data provides enormous opportunities in a variety of applications. Recent research has indicated the failure of existing sanitization techniques （e.g., k-anonymity） to provide rigorous privacy guarantees. Privacy- preserving multidimensional data publishing currently lacks a solid theoretical foundation. It is urgent to develop new techniques with provable privacy guarantees, e-Differential privacy is the only method that can provide such guarantees. In this paper, we propose a multidimensional data publishing scheme that ensures c-differential privacy while providing accurate results for query processing. The proposed solution applies nonstandard wavelet transforms on the raw multidimensional data and adds noise to guarantee c-differential privacy. Then, the scheme processes arbitrarily queries directly in the noisy wavelet- coefficient synopses of relational tables and expands the noisy wavelet coefficients back into noisy relational tuples until the end result of the query. Moreover, experimental results demonstrate the high accuracy and effectiveness of our approach.
