A privacy preserving association rule mining algorithm was introducedThis algorithm preserved privacy of individual values by computing scalar productMeanwhile the algorithm of computing scalar product was given and the security was analyzed

Privacy preserving association rule mining in vertically partitioned data

The growing popularity and development of data mining technologies bring serious threat to the security of individual,'s sensitive information. An emerging research topic in data mining, known as privacy-preserving data mining (PPDM), has been extensively studied in recent years. The basic idea of PPDM is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. Current studies of PPDM mainly focus on how to reduce the privacy risk brought by data mining operations, while in fact, unwanted disclosure of sensitive information may also happen in the process of data collecting, data publishing, and information (i.e., the data mining results) delivering. In this paper, we view the privacy issues related to data mining from a wider perspective and investigate various approaches that can help to protect sensitive information. In particular, we identify four different types of users involved in data mining applications, namely, data provider, data collector, data miner, and decision maker. For each type of user, we discuss his privacy concerns and the methods that can be adopted to protect sensitive information. We briefly introduce the basics of related research topics, review state-of-the-art approaches, and present some preliminary thoughts on future research directions. Besides exploring the privacy-preserving approaches for each type of user, we also review the game theoretical approaches, which are proposed for analyzing the interactions among different users in a data mining scenario, each of whom has his own valuation on the sensitive information. By differentiating the responsibilities of different users with respect to security of sensitive information, we would like to provide some useful insights into the study of PPDM.

Information Security in Big Data: Privacy and Data Mining

http://www.dxsx.net/admin/editor/upload/201412/2014122915411949.pdf

Digital image forgery detection using passive techniques: A survey

An immunity enhanced particle swarm optimization (IEPSO) algorithm, which combines particle swarm optimization (PSO) with the artificial immune system, is proposed for damage detection of structures. Some immune mechanisms, selection, receptor editing and vaccination are introduced into the basic PSO to improve its performance. The objective function for damage detection is based on vibration data, such as natural frequencies and mode shapes. The feasibility and efficiency of IEPSO are compared with the basic PSO, a differential evolution algorithm and a real-coded genetic algorithm on two examples. Results show that the proposed strategy is efficient on determining the sites and the extents of structure damages.

Damage detection based on improved particle swarm optimization using vibration data

Clustering Objectives in Wireless Sensor Networks: A Survey and Research Direction Analysis

The advent of sensors that are light in weight, small-sized, low power and are enabled by wireless network has led to growth of wireless sensor networks (WSNs) in multiple areas of applications. The key problems faced in WSNs are decreased network lifetime and time delay in transmission of data. In many critical applications such as military and monitoring the eco system, disaster management, etc., data routing is very crucial. Multi hop low-energy adaptive clustering hierarchy protocol has been proposed in literature but is proved to be inefficient. Cluster head optimization is a NP hard. This paper deals with selection of optimal path in routing which improves network lifespan, as well as network’s energy efficiency. Various meta-heuristic techniques particularly particle swarm optimization (PSO) has been effectively used but with poor local optima problem. The proposed method is on the basis of PSO as well as Tabu search algorithms. Results show the efficiency of the proposed Tabu PSO by enhancing the number of clusters formed, percentage of nodes alive and shows the reduction of average packet loss rate and average end to end delay.

A multi objective Tabu particle swarm optimization for effective cluster head selection in WSN

Department of Computer Science and Engineering, Ramco Institute of Technology, Rajapalayam, India Department of Electronics and Communication Engineering, Muthayammal Engineering College, Rasipuram, India School of Computer Science and Engineering, SCE, Taylor's University, Subang Jaya, 47500, Malaysia School of Computer Science and Engineering, Lovely Professional University, Phagwara, India Department of IT, National Engineering College, Kovilpatti, India Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA

An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city

Digital image forgery detection and estimation by exploring basic image manipulations

In this paper we have proposed a novel dynamic least cost multicast routing protocol using hybrid genetic algorithm for IP networks. Our protocol finds the multicast tree with minimum cost subject to delay, degree, and bandwidth constraints. The proposed protocol has the following features: i. Heuristic local search function has been devised and embedded with normal genetic operation to increase the speed and to get the optimized tree, ii. It is efficient to handle the dynamic situation arises due to either change in the multicast group membership or node / link failure, iii. Two different crossover and mutation probabilities have been used for maintaining the diversity of solution and quick convergence. The simulation results have shown that our proposed protocol generates dynamic multicast tree with lower cost. Results have also shown that the proposed algorithm has better convergence rate, better dynamic request success rate and less execution time than other existing algorithms. Effects of degree and delay constraints have also been analyzed for the multicast tree interns of search success rate. Keywords—Dynamic Group membership change, Hybrid Genetic Algorithm, Link / node failure, QoS Parameters.

K. Vijayalakshmi

Papers

A multi objective Tabu particle swarm optimization for effective cluster head selection in WSN

An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city

Digital image forgery detection and estimation by exploring basic image manipulations

Dynamic Routing to Multiple Destinations in IP Networks using Hybrid Genetic Algorithm (DRHGA)

Improving the security of medical image through neuro-fuzzy based ROI selection for reliable transmission