An efficient model for secure data publishing

Data Mining is the field of extracting and analyzing the data from large datasets. Exchange of databases is very important to get financial benefits now a day. To review business strategies and to get maximum benefit data analytics is needed. Data stored at distributed sites are integrated and published by data publisher. Data Publishing is the technique in which the data is released to others for the use. In addition to privacy preserving data size and security is also a challenge while publishing and transmitting the database. So there is a requirement of a technique which can reduce the size of database efficiently and transfer it in a secure manner. This paper proposes an efficient model for secure data publishing by using both compression and color encryption thus introducing a new approach. A new algorithm is designed and a tool is developed for implementing the proposed work

Mandelbrot and Julia Sets via Jungck-CR Iteration with s-convexity

In today’s world, fractals play an important role in many fields, e.g., image compression or encryption, biology, physics, and so on. One of the earliest studied fractal types was the Mandelbrot and Julia sets. These fractals have been generalized in many different ways. One of such generalizations is the use of various iteration processes from the fixed point theory. In this paper, we study the use of Jungck-CR iteration process, extended further by the use of s-convex combination. The Jungck-CR iteration process with s-convexity is an implicit three-step feedback iteration process. We prove new escape criteria for the generation of Mandelbrot and Julia sets through the proposed iteration process. Moreover, we present some graphical examples obtained by the use of escape time algorithm and the derived criteria

An efficient model for secure data publishing

Data Mining is the field of extracting and analyzing the data from large datasets. Exchange of databases is very important to get financial benefits now a day. To review business strategies and to get maximum benefit data analytics is needed. Data stored at distributed sites are integrated and published by data publisher. Data Publishing is the technique in which the data is released to others for the use. In addition to privacy preserving data size and security is also a challenge while publishing and transmitting the database. So there is a requirement of a technique which can reduce the size of database efficiently and transfer it in a secure manner. This paper proposes an efficient model for secure data publishing by using both compression and color encryption thus introducing a new approach. A new algorithm is designed and a tool is developed for implementing the proposed work

A Novel Hybrid Secure Image Encryption Based on Julia Set of Fractals and 3D Lorenz Chaotic Map

Chaos-based encryption schemes have attracted many researchers around the world in the digital image security domain. Digital images can be secured using existing chaotic maps, multiple chaotic maps, and several other hybrid dynamic systems that enhance the non-linearity of digital images. The combined property of confusion and diffusion was introduced by Claude Shannon which can be employed for digital image security. In this paper, we proposed a novel system that is computationally less expensive and provided a higher level of security. The system is based on a shuffling process with fractals key along with three-dimensional Lorenz chaotic map. The shuffling process added the confusion property and the pixels of the standard image is shuffled. Three-dimensional Lorenz chaotic map is used for a diffusion process which distorted all pixels of the image. In the statistical security test, means square error (MSE) evaluated error value was greater than the average value of 10000 for all standard images. The value of peak signal to noise (PSNR) was 7.69(dB) for the test image. Moreover, the calculated correlation coefficient values for each direction of the encrypted images was less than zero with a number of pixel change rate (NPCR) higher than 99%. During the security test, the entropy values were more than 7.9 for each grey channel which is almost equal to the ideal value of 8 for an 8-bit system. Numerous security tests and low computational complexity tests validate the security, robustness, and real-time implementation of the presented scheme

Image Retrieval Based on Fractal Dictionary Parameters

Content-based image retrieval is a branch of computer vision. It is important for efficient management of a visual database. In most cases, image retrieval is based on image compression. In this paper, we use a fractal dictionary to encode images. Based on this technique, we propose a set of statistical indices for efficient image retrieval. Experimental results on a database of 416 texture images indicate that the proposed method provides a competitive retrieval rate, compared to the existing methods

Proceedings of the Scientific Data Compression Workshop

Continuing advances in space and Earth science requires increasing amounts of data to be gathered from spaceborne sensors. NASA expects to launch sensors during the next two decades which will be capable of producing an aggregate of 1500 Megabits per second if operated simultaneously. Such high data rates cause stresses in all aspects of end-to-end data systems. Technologies and techniques are needed to relieve such stresses. Potential solutions to the massive data rate problems are: data editing, greater transmission bandwidths, higher density and faster media, and data compression. Through four subpanels on Science Payload Operations, Multispectral Imaging, Microwave Remote Sensing and Science Data Management, recommendations were made for research in data compression and scientific data applications to space platforms