Privacy Preserving Through Fireworks Algorithm Based Model for Image Perturbation in Big Data

Introduction

With the emersion of new applications centred on the sharing of image data over large data services such as medical imagery and social nets. Privacy concerns have become a crucial problem in the modern information sciences, particularly with the high growth of data mining and analytical techniques. Data perturbation and de-identification are two main fields in the privacy preserving research area. Unlike the cryptographic systems, which propose a temporary change in the data without holding in consideration the utility of ciphered data, the perturbation techniques consist of minimizing the disruption between the extremities of the major paradox, which hides sensitive data by altering its content while maintaining its utility. At the age of big data, data perturbation, inherited from secure database techniques, becomes an important issue in data protection. It often uses different techniques. We distinguish two different types of data perturbation: differential privacy in which the differential computations are often used such as chaotic mapping (Tong, 2009), and privacy preserving data mining in which a data mining technique is used (Ag???). However, data perturbation is facing growing concerns. The researchers are attempting to prepare and adapt data mining techniques to be useful for privacy matters.

In most engineering disciplines, many problems can be simplified as numerical optimization problems through mathematical modelling. Nowadays, studying such biological phenomenon is no longer concerned only by biologists. All of this gave birth to a new domain of research known by bio-inspired or meta-heuristics methods for optimization. Optimization algorithms and techniques are a set of meta-heuristics known by their efficiency in solving difficult problems. Those algorithms are generally probabilistic and stochastic processes that are inspired from life and nature. One special algorithm which has seen the light recently is known as firework algorithm (FWA). Since its development in 2010 by Tan and Zhu in (Tan, 2010), FWA has been used in many problems and it has proved a high efficiency in finding optimal solutions. This paper presents a suggestion of a new usefulness of FWA algorithm, not on optimization problem but on securing aspect. We work out a model of rotation based perturbation that consists of hiding sensitive information over image data using fireworks explosions.

The remainder of this report is organised as follows: first we introduce some general concepts that are employed in our theoretical account; then we introduce the universal framework of this attack; after that, we describe our experiments and discuss its results in term of efficiency by comparing it with results of some known conventional works; finally, we end with conclusions and perspectives.