Abstract—Privacy Preserving Data Mining (PPDM) is getting attention of the researchers in different domain especially in Association Rule Mining. The purpose of the preserving association rules is to minimize the disclosing risk on shared information to the external parties. In this paper, we proposed a PPDM model for XML Association Rules (XARs). The proposed model identifies the most probable item called as sensitive to modify the original data source with more accuracy and reliability. Such reliability is not addressed before in the literature in any kind of methodology used in PPDM domain and especially in XML association rules mining. Thus, the significance of the suggested model sets and open new dimension to the academia in order to control the sensitive information in a more unyielding line of attack.
Keywords: XARs, PPDM, K2 algorithm,Bayesian Network, Association Rules
I. INTRODUCTION
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n data mining, trends and patterns are identified on a huge set of data to discover knowledge. In such analysis, varieties of algorithms exist for extracting knowledge such as clustering, classification and association rule mining. Thus, association rules mining one domain for delivering knowledge on complex data. Moreover, the basis of the discovered association rules is usually determined by the minimum support s % and minimum confidence c% to represent the transactional items in database D. Thus, it has the implication of the form AB, where A is the antecedent and B is the consequent. The problem with such display of rules is the disclosure of sensitive information to the external part when data is shared. Hence Privacy Preserving in Data Mining (PPDM) related to Association Rules emerges.
In PPDM, Sensitive information is con...
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Created by Philip Zimmermann in 1991, this program has been widely used throughout the global computer community to protect the confidentiality and integrity of the users’ data, giving them the privacy of delivering messages and files only to their intended individual or authorized person (Singh, 2012). Not only being useful for individuals as a privacy-ensuring program, it has also been used in many corporations to protect their company’s data from falling into the wrong hands (Rouse, 2005).
Over the last twenty years the progress in the technologies which handle information have appeared very dramatic and has therefore posed a threat to information privacy. Analysis of this progress reveals that this progress is not in terms of new technologies being invented but those technologies already known have increased dramatically in their power while also falling dramatically in their prices. This has happened to such a degree that the market penetration could only have been dreamed about by the most optimistic of market analysts a decade ago. The countries that are predominately concerned are those of the developed first world but as a result of the tremendous market penetration of these products their prices have now fallen to such a level that it cannot be long before the technology spreads progressively through the developed world also.
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Privacy challenges. Privacy is a circumstance of restricted right of entry to an information regarding an individual (Knoppers, 2015). Brothers and Rothstein (2015) noted numerous other kinds of privacy, comprising physical, decisional, proprietary and relational or associational privacy. This study emphasizes on informational health privacy. When it comes to privacy issues the crucial question to explore is; how can leadership balance the right of privacy with the advantageous requirement for clinical data-access in EHR? The Privacy Act of 1974 is the US law that represents national standards to protect the private health information of individuals by mandating appropriate safeguards and limitations on the right to use and release of (PHI)
In all, there is an increasing use of big data by a number of different organizations, industries, and people. Many governments, researchers, scientists, and businesses are using big data to obtain information to further their goals. This obtainment of information by these parties is deemed by many as controversial and detrimental to the privacy of individuals. The debate about the use of big data will become more important as more and more information is gathered from a variety of sources. As the more data is collected the less private individual information will be. The topic of big data will grow in popularity as technology advances and more and more data becomes available to a number of different parties.
Data mining is the technique to interpret the data from other perspective and summarize the data so that the data can be useful information. Technically, data mining is a process to identify relations or patterns in the databases to predict the likelihood of future events. According to Eliason et al, there are three systems for healthcare organization to implement the mining data systems. The three systems are the analytics system, the content system and the deployment system. The analytics system is a system that used to collect all data such as patients clinical data, patients financial data, patients satisfactory data and other data. The content system is used to store all medical evidenced data. The deployment system is used to make new organization structure. There are several elements that consist in data mining which are first extract, transform and load transaction data onto the data warehouse system, second, store and manage the data in a multidimensional system, third, provide data access to information technology professionals, forth, analyze the data by application software and lastly, present the data in graph or table format.
In our modern society almost every online act is a freedom of a citizen or a professional expression. Some content is stored and kept safe amongst small groups and some is made publicly available. Yet all acts can generate transactional information which can be viewed by many other parties over the web. This m...
At this point, is important to note that Big data itself does not represent more large data set of structured and unstructured data; nowadays bigger than ever and in continuous expansion that can be defined as the "problem of big data" (Cox M. & Ellsworth D., 1997). The ability to organize this "problem" given certain parameters and to be able to build a model or representation of a reality taking care of the existing patterns and relationships to find the true value that lies hidden in data is what can be defined as Data mining (DM) (Kadiyala, S. S., & Srivastava, A., 2011).
The computer is considered one of the most important technological advances of the twentieth century. Security and privacy issues have been in existence long before the computer became a vital component of organizations' operations. Nevertheless, the operating features of a computer make it a double-edged sword. Computer technologies with reliable error detection and recording capabilities, permit the invasion of a supposedly secure environment to occur on a grand scale and go undetected. Furthermore, computer and communications technology permit the invasion of a persons' privacy and likewise go undetected. Two forces threaten privacy: one, the growth of information technology with its enhanced capacity for surveillance, communication, computation, storage and retrieval and two, the more insidious threat, the increased value of information in decision making. Information has become more vital in the competitive environment, thus, decision makers covet it even if it viol!
LeRoux, Yves. "Privacy concerns in the digital world." 03 Oct 2013. Computer Weekly. 24 April 2014 .
Big data is a hot topic in the Information Technology industry as it is a collection of data that describes the growth of the company, present in both structured and unstructured types. As the industry is dealing with large data, they are also concerned about the security of the data which is provided by big data security tools analytics.
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