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.

/pdf/a-comprehensive-review-on-privacy-preserving-data-mining-25u6js5lq1.pdf

A comprehensive review on privacy preserving data mining

Data mining technology is used to extract useful knowledge from very large datasets, but the process of data collection and data dissemination may result in an inherent threat to privacy. Some sensitive or private information concerning individuals, businesses and organizations has to be suppressed before it is shared or published. Privacy-preserving data mining (PPDM) has become an important issue in recent years. In the past, many heuristic approaches were developed to sanitize databases for the purpose of hiding sensitive information in PPDM, but data sanitization of PPDM is considered to be an NP-hard problem. It is critical to find the balance between privacy protection for hiding sensitive information and maintaining the discovery of knowledge, or even reducing artificial knowledge in the sanitization process. In this paper, a GA-based framework with two optimization algorithms is proposed for data sanitization. A novel evaluation function with three concerned factors is designed to find the appropriate transactions to be deleted in order to hide sensitive itemsets. Experiments are then conducted to evaluate the performance of the proposed GA-based algorithms with regard to different factors such as the execution time, the number of hiding failures, the number of missing itemsets, the number of artificial itemsets, and database dissimilarity.

The GA-based algorithms for optimizing hiding sensitive itemsets through transaction deletion

Data mining is used to mine meaningful and useful information or knowledge from a very large database. Some secure or private information can be discovered by data mining techniques, thus resulting in an inherent risk of threats to privacy. Privacy-preserving data mining (PPDM) has thus arisen in recent years to sanitize the original database for hiding sensitive information, which can be concerned as an NP-hard problem in sanitization process. In this paper, a compact prelarge GA-based (cpGA2DT) algorithm to delete transactions for hiding sensitive itemsets is thus proposed. It solves the limitations of the evolutionary process by adopting both the compact GA-based (cGA) mechanism and the prelarge concept. A flexible fitness function with three adjustable weights is thus designed to find the appropriate transactions to be deleted in order to hide sensitive itemsets with minimal side effects of hiding failure, missing cost, and artificial cost. Experiments are conducted to show the performance of the proposed cpGA2DT algorithm compared to the simple GA-based (sGA2DT) algorithm and the greedy approach in terms of execution time and three side effects.

/pdf/efficiently-hiding-sensitive-itemsets-with-transaction-lf2jhv9sz4.pdf

Efficiently hiding sensitive itemsets with transaction deletion based on genetic algorithms.

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.

Association rule hiding using cuckoo optimization algorithm

Businesses share data, outsourcing for specific business problems. Large companies stake a large part of their business on analysis of private data. Consulting firms often handle sensitive third party data as part of client projects. Organizations face great risks while sharing their data. Most of this sharing takes place with little secrecy. It also increases the legal responsibility of the parties involved in the process. So, it is crucial to reliably protect their data due to legal and customer concerns. In this paper, a review of the state-of-the-art methods for privacy preservation is presented. It also analyzes the techniques for privacy preserving association rule mining and points out their merits and demerits. Finally the challenges and directions for future research are discussed.

/pdf/a-survey-on-privacy-preserving-association-rule-mining-1j5z2o9x2j.pdf

A Survey on Privacy Preserving Association Rule Mining

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.

/pdf/a-novel-method-for-privacy-preserving-in-association-rule-288z88brxt.pdf

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

Privacy preserving association rule mining has been an active research area since recently. So data mining techniques have been developed in many applications. On the other hand, it also causes an intimidation to privacy of sensitive information. We investigate to find an appropriate balance between an urgency of need for privacy and information extraction on association rules. In this paper, we propose a novel technique for concealing sensitive association rules. In our approach, we do some perturbation in original dataset based on genetic algorithms with suitable fitness function to hide sensitive rules and minimum number of modifications. Finally, a set of experiments is performed to show the effectiveness of our approach.

Ahmad Khadem Zadeh

Papers

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

A new approach for sensitive association rules hiding

An Improvement Energy Consumption Policy Using Communication Reduction in Wireless Body Sensor Network