Anonymization on refining partition: Same privacy, more utility

In privacy preserving data publishing, to reduce the correlation loss between sensitive attribute (SA) and non-sensitive attributes(NSAs) caused by anonymization methods (such as generalization, anatomy, slicing and randomization, etc.), the records with same NSAs values should be divided into same blocks to meet the anonymizing demands of ?-diversity. However, there are often many blocks (of the initial partition), in which there are more than ? records with different SA values, and the frequencies of different SA values are uneven. Therefore, anonymization on the initial partition causes more correlation loss. To reduce the correlation loss as far as possible, in this paper, an optimizing model is first proposed. Then according to the optimizing model, the refining partition of the initial partition is generated, and anonymization is applied on the refining partition. Although anonymization on refining partition can be used on top of any existing partitioning method to reduce the correlation loss, we demonstrate that a new partitioning method tailored for refining partition could further improve data utility. An experimental evaluation shows that our approach could efficiently reduce correlation loss.

A Novel Non-Uniform BTC and its Application

In this paper, a novel image representation coding based on non-uniform Block Truncation Coding (BTC) is proposed. A given image can automatically be partitioned into different regions with different sizes and the bivariate piecewise polynomials are used to do the Least Square Approximation for the pixel values in each sub-region based on BTC bitmap. When the approximation error and initial partition are specified, a specific image partition result is obtained. The image re-construction quality of the proposed algorithm is better than that of the traditional BTC. Based on this algorithm, an effective denoising scheme of image is implemented and some of the experimental examples are illustrated to prove that the quality of the re-constructed image, the denoising effect are all satisfactory and can be referenced by other researchers.

A Non-Uniform Regional Partition Coding of One Dimension Signal and its Application

In this paper, a novel method for one-dimension-signal coding based on non-uniform regional partition is proposed. A given 1D signal can automatically be partitioned into different regions with different lengths and the one-variable polynomial is used to do the Least Square Approximation for signal values in each sub-region. When the approximation error and initial partition are specified, a specific signal partition result is obtained. Based on this algorithm, an effective denoising scheme of 1D signal is implemented and some of the experimental examples are illustrated to prove that the quality of the re-constructed signal, the denoising effect are all satisfactory and can be referenced by other researchers.

Min-Max Partition Method of Product Modularization Based on Fuzzy Clustering

This paper presents a new method of product module partition based on the fuzzy clustering analysis. This method demonstrates the relevant definitions and calculation methods of the initial partition, min-max partition, submodule relevancy and module aggregation etc., and establishes the incidence matrix to respectively carry out the initial partition for the products and calculation of min-max partition according to various incidence relations between parts and components. Taking the submodule as computing unit in each module set, this paper carries out the fuzzy cluster analysis to obtain the module partition results of the products, and finally demonstrates the rationality and effectiveness of this method by taking the example of the working units of the wheel loaders.