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

Abstract

Nowadays, Social networks and cloud services contain billions of users over the planet. Instagram, Facebook and other networks give the opportunity to share images. Users upload millions of pictures each day, including personal images. Another domain, which concerns medical studies, requires a highly sensitive medical images that retain personal details close to patients. Image perturbation have attracted a great deal of attention in the last few years. Many works concerning image ciphering and perturbing have been published. This paper deals with the problem of image perturbation for privacy preserving. The authors build three new systems that consist of hiding small details in pictures by rotating some pixels. Their models use two algorithms: the first one involves a simulation of the firework algorithm in which they place fireworks on selected pixels then represents sparks as rotation processes. The second system consists of a model of rotation based perturbation using iterated local search algorithm (ILS) with 2 optimization stages. Meanwhile, the third one consists of using the same principle of the previous system except by using the ILS algorithm with 3 optimization stages.

Assessment of college students’ mental health status based on temporal perception and hybrid clustering algorithm under the impact of public health events

“…This nature-inspired metaheuristic approach simulates the explosion of fireworks to enhance the search process within complex solution spaces. Through its utilization, the algorithm has exhibited remarkable efficacy in solving a variety of optimization problems, ranging from engineering design and parameter tuning to financial portfolio optimization ( Karimov & Ozbayoglu, 2015 ; Rahmani et al, 2015 ).…”

A Fireworks Algorithm Based on Transfer Spark for Evolutionary Multitasking

“…Afterward, the superior solutions from the whole fireworks and sparks are selected as the fireworks for the next generation to continually improve the quality of the solution iteratively. Benefiting from the powerful global search and information utilization capabilities of FWA, it has attracted much research interest (Zheng et al, 2013;Liu et al, 2015;Li et al, 2017;Li and Tan, 2018) and has demonstrated excellent performance in many real-world problems (Yang and Tan, 2014;Bacanin and Tuba, 2015;Bouarara et al, 2015;Ding et al, 2015;Rahmani et al, 2015). In this paper, an innovative transfer vector (TV) is introduced to represent the bias of knowledge transfer between tasks.…”

Accelerating Fireworks Algorithm with Weight-Based Guiding Sparks

“…Many powerful variants of FWA have been sprung up like mushrooms by incorporating various effective search mechanisms, such as enhanced FWA (EFWA) [2], dynamic FWA (dynFWA) [3], adaptive FWA (AFWA) [4], guided FWA (GFWA) [5] and others [6][7][8][9]. They have also solved many complex real-world applications successfully, including multilevel image thresholding [10], RFID network planning [11] and privacy preserving [12], etc., thanks to their excellent characteristics. Although they have achieved gratifying results, there is still plenty of room to further improve FWA performance.…”