Mohammad Naderi Dehkordi

Bio: Mohammad Naderi Dehkordi is an academic researcher from Islamic Azad University. The author has contributed to research in topics: Association rule learning & Information sensitivity. The author has an hindex of 9, co-authored 26 publications receiving 217 citations. Previous affiliations of Mohammad Naderi Dehkordi include Islamic Azad University, Isfahan & Islamic Azad University of Najafabad.

A Novel Method for Privacy Preserving in Association Rule Mining Based on Genetic Algorithms

Abstract: Extracting of knowledge form large amount of data is an important issue in data mining systems. One of most important activities in data mining is association rule mining and the new head for data mining research area is privacy of mining. Today association rule mining has been a hot research topic in Data Mining and security area. A lot of research has done in this area but most of them focused on perturbation of original database heuristically. Therefore the final accuracy of released database falls down intensely. In addition to accuracy of database the main aspect of security in this area is privacy of database that is not warranted in most heuristic approaches, perfectly. In this paper we introduce new multi-objective method for hiding sensitive association rules based on the concept of genetic algorithms. The main purpose of this method is fully supporting security of database and keeping the utility and certainty of mined rules at highest level.

Association rule hiding using cuckoo optimization algorithm

Abstract: We use Cuckoo Optimization Algorithm for hiding sensitive association rules.A preprocess is defined that causes speedy access to the optimal solution.Introducing three fitness functions with minimum side effects.An immigration algorithm is defined to escape from local optimums.For efficiency assessment, the algorithm is examined on real and synthetic data. Privacy preserving data mining is a new research field that aims to protect the private information and avoid the leakage of this information during the data mining process. One of the techniques in this field is the Privacy Preserving Association Rule Mining which aims to hide sensitive association rules. Many different algorithms with particular approaches have so far been developed to reach this purpose. In this paper, a new and efficient approach has been introduced which benefits from the cuckoo optimization algorithm for the sensitive association rules hiding (COA4ARH). In this method the act of hiding is performed using the distortion technique. Further in this study three fitness functions are defined which makes it possible to achieve a solution with the fewest side effects. Introducing an efficient immigration function in this approach has improved its ability to escape from any local optimum. The efficiency of proposed approach was evaluated by conducting some experiments on different databases. The results of the execution of the proposed algorithm and three of the previous algorithms on different databases indicate that this algorithm has superior performance compared to other algorithms.

Privacy-preserving in association rule mining using an improved discrete binary artificial bee colony

Abstract: Association Rule Hiding (ARH) is the process of protecting sensitive knowledge using data transformation. Although there are some evolutionary-based ARH algorithms, they mostly focus on the itemset hiding instead of the rule hiding. Besides, unstable convergence to the global optimum solution and designing long solutions make them inappropriate in reducing side effects. They use the basic versions of evolutionary approaches, resulting in inappropriate performance in ARH domain where the search space is large and the algorithms easily get trapped in local optima. To deal with these problems, we propose a new rule hiding algorithm based on a binary Artificial Bee Colony (ABC) approach which has good exploration. However, we improve the binary ABC algorithm to enhance its poor exploitation by designing a new neighborhood generation mechanism to balance between exploration and exploitation. We called this algorithm Improved Binary ABC (IBABC). IBABC approach is coupled with our proposed rule hiding algorithm, called ABC4ARH, to select sensitive transactions for modification. To choose victim items, ABC4ARH formulates a heuristic. The performance of ABC4ARH algorithm on the side effects is demonstrated using extensive experiments conducted on five real datasets. Furthermore, the effectiveness of IBABC is verified using the uncapacitated facility location problem and 0–1 knapsack problem.

Sensitive association rules hiding using electromagnetic field optimization algorithm

Abstract: Privacy preserving data mining has been a major research subject in recent years. The most important goal of this area is to protect personal information and prevent disclosure of this information during the data mining process. There are various techniques in the field of privacy preserving data mining. One of these techniques is association rules mining. The main purpose of association rules mining is to hide sensitive association rules. So far, various algorithms have been presented to this field in order to reach the purpose of sensitive association rules hiding. Each algorithm has its own specific functions and methods. To hide sensitive association rules, this paper presents an electromagnetic field optimization algorithm (EFO4ARH). This algorithm utilizes the data distortion technique to hide the sensitive association rules. In this algorithm, two fitness functions are used to reach the solution with the least side effects. Also, in this algorithm, the runtime has been reduced. This algorithm consists of a technique for exiting from local optima point and moving toward global optimal points. The performance of the proposed algorithm is evaluated by doing experiments on both real-world and synthetic datasets. Compared to four reference algorithms, the proposed algorithm shows a reduction in the side effects and better preservation of data quality. The performance of EFO4ARH is tested by standard deviation and mean Friedman ranks of error for standard functions (CEC benchmarks). In addition, hiding experiments show that our proposed algorithm outperforms existing hiding algorithms.

A survey on privacy preserving association rule mining

Abstract: By developing information technology and production methods and collecting data, a great amount of data is daily being collected in commercial, medical databases. Some of this information is important with respect to competition concept in organizations and individual misuses. Nowadays in order to mine knowledge among a great amount of data, data mining tools are used. In order to protect information, fast processing and preventing from revealing private data to keep privacy is presented in data mining. In this article, some techniques in preserving privacy of association rule mining are introduced and some hiding algorithms of association rules are evaluated.

Information Security in Big Data: Privacy and Data Mining

Abstract: 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.

A Gaussian error correction multi‐objective positioning model with NSGA‐II

Abstract: Distance vector‐hop (DVHop), as a range‐independent positioning algorithm, is a significant positioning method in wireless sensor networks (WSNs). It is composed of three parts, including connectivity detection, distance estimation, and position estimation. However, this simple positioning method results in a larger positioning error. Therefore, to enhance the positioning precision, this paper investigates the characteristic of error distribution between the estimated and real distance in the DVHop algorithm and reveals that the error is subjecting to the Gaussian distribution, N∼(0,1/3CR). Furthermore, to improve positioning accuracy, we propose a Gaussian error correction multi‐objective positioning model with non‐dominated sorting (NSGA‐II), which named GGAII‐DVHop. Finally, this model is tested on three complex network topologies, and the results demonstrate that it is significantly superior to other four algorithms in both positioning precision and robustness.

A comprehensive review on privacy preserving data mining

Abstract: Preservation of privacy in data mining has emerged as an absolute prerequisite for exchanging confidential information in terms of data analysis, validation, and publishing. Ever-escalating internet phishing posed severe threat on widespread propagation of sensitive information over the web. Conversely, the dubious feelings and contentions mediated unwillingness of various information providers towards the reliability protection of data from disclosure often results utter rejection in data sharing or incorrect information sharing. This article provides a panoramic overview on new perspective and systematic interpretation of a list published literatures via their meticulous organization in subcategories. The fundamental notions of the existing privacy preserving data mining methods, their merits, and shortcomings are presented. The current privacy preserving data mining techniques are classified based on distortion, association rule, hide association rule, taxonomy, clustering, associative classification, outsourced data mining, distributed, and k-anonymity, where their notable advantages and disadvantages are emphasized. This careful scrutiny reveals the past development, present research challenges, future trends, the gaps and weaknesses. Further significant enhancements for more robust privacy protection and preservation are affirmed to be mandatory.