On the Design of Biorthogonal Halfband Filterbanks With Almost Tight Rational Coefficients

Nagare, Mukund B.; Patil, Bhushan D.; Holambe, Raghunath S.

doi:10.1109/tcsii.2019.2922745

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…Step 23 is the computation of σ 2 sum using equation (26). Steps 24 to 26 denote the computation of σ 2 sumMax using equation (34). Step 29 denotes the output of Algorithm 1.…”

Section: Maximum Variance Of Rangementioning

confidence: 99%

“…Discrete wavelet transform (DWT) is an important technology in signal and image processing [29][30][31]. Lifting scheme, also called second generation wavelet, has many advantages comparing with the first generation wavelet, such as in-place computation, integer-to-integer transforms, and speed [32][33][34]. Wavelet-based privacy preserving is studied in recent years [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

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Differential Privacy via Haar Wavelet Transform and Gaussian Mechanism for Range Query

Chen

et al. 2022

Computational Intelligence and Neuroscience

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Range query is the hot topic of the privacy-preserving data publishing. To preserve privacy, the large range query means more accumulate noise will be injected into the input data. This study presents a research on differential privacy for range query via Haar wavelet transform and Gaussian mechanism. First, the noise injected into the input data via Laplace mechanism is analyzed, and we conclude that it is difficult to judge the level of privacy protection based on the Haar wavelet transform and Laplace mechanism for range query because the sum of independent random Laplace variables is not a variable of a Laplace distribution. Second, the method of injecting noise into Haar wavelet coefficients via Gaussian mechanism is proposed in this study. Finally, the maximum variance for any range query under the framework of Haar wavelet transform and Gaussian mechanism is given. The analysis shows that using Haar wavelet transform and Gaussian mechanism, we can preserve the differential privacy for each input data and any range query, and the variance of noise is far less than that just using the Gaussian mechanism. In an experimental study on the dataset age extracted from IPUM’s census data of the United States, we confirm that the proposed mechanism has much smaller maximum variance of noises than the Gaussian mechanism for range-count queries.

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“…Step 23 is the computation of σ 2 sum using equation (26). Steps 24 to 26 denote the computation of σ 2 sumMax using equation (34). Step 29 denotes the output of Algorithm 1.…”

Section: Maximum Variance Of Rangementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Differential Privacy via Haar Wavelet Transform and Gaussian Mechanism for Range Query

Chen

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…They appear across different time scales. Capturing the full range of these complex patterns using WEC remains a challenge [33][34][35][36][37][38][39][40]. This challenge arises from the crucial role of selecting a pre-defined wavelet function.…”

Section: Introductionmentioning

confidence: 99%

“…Various methods exist for wavelet design. These methods include multi-resolution analysis (MRA) [33,34], lifting schemes [35,36], and filter banks [37,38,39,40,44]. In [45], researchers use a template-based method for the specific wavelet design.…”

Section: Introductionmentioning

confidence: 99%