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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...066-1395, IEEE Computer Society Washington, DC, USA
[7]. M. Atallah, E. Bertino, A. Elmagarmid, M. Ibrahim, V. Verykios, “Disclosure Limitation of Sensitive Rules”, Page:45-52,Year of Publication: 1999, ISBN:0-7695-0453-1,IEEE Computer Society , Washington, DC, USA
[8]. Gregory F. Cooper and Edward Herskovits. A Bayesian method for the induction of probabilistic networks from data. Mach. Learn., 9(4):309{347, 1992.
[9]. R. Agralwal, T.Imielinski, and A.Swami. Mining associations between sets of items in large databases. In P.Buneman and S. Jajodia, editors, SIGMOD93, pages 207-216, Washington, D.C, USA, May 1993
[10]. O. Doguc, and J.E. Ramirez-Marquez “A generic method for estimating system reliability using Bayesian Networks”, in proc. Reliability Engineering and System Safety, ( 2008)
[11]. http://tunedit.org/repo/UCI/lymph.arff,DatasetAccessDate:31-03-2010
... middle of paper ... ... In Intelligent Data Engineering and Automated Learning–IDEAL 2006 (pp. 1346-1357. Springer Berlin, Heidelberg.
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.
7. Herman T. Tavani, James H. Moor "Privacy Protection, Control of Information, and Privacy-Enhancing Technologies", Computers and Society, March 2001
"Privacy and the Internet: Intrusion, Surveillance and Personal Data." International Review of Law, Computers & Technology Oct. 1996: 219-235.
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)
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).
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...
LeRoux, Yves. "Privacy concerns in the digital world." 03 Oct 2013. Computer Weekly. 24 April 2014 .
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!
Data mining has emerged as an important method to discover useful information, hidden patterns or rules from different types of datasets. Association rule mining is one of the dominating data mining technologies. Association rule mining is a process for finding associations or relations between data items or attributes in large datasets. Association rule is one of the most popular techniques and an important research issue in the area of data mining and knowledge discovery for many different purposes such as data analysis, decision support, patterns or correlations discovery on different types of datasets. Association rule mining has been proven to be a successful technique for extracting useful information from large datasets. Various algorithms or models were developed many of which have been applied in various application domains that include telecommunication networks, market analysis, risk management, inventory control and many others
[5] J.S. Fulda. Data Mining and Privacy. In R. Spinello and H. Trvani, editors, Readings in CyberEthics, pages 413-417. Jones and Barlett, Sudbury MA, 2001.
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.
In recent times we might consider that the right for privacy in one of our major achievements in our society. Unfortunately the same does not apply for the Internet. The Internet once considered a place for free information exchange and sharing, has the potential has becoming the biggest threat to its users. The commercialization of the Internet is one of the major reasons why the chances of a compromise of our privacy have increased. Customer profiling, address trading or simple lack of care with sensitive customer information are just some of the threats that this commercialization has bought to our ‘free’ Internet. Another reason being that with the increase of threats there is a regular change in technology to counter these threats. These changes make it harder for the already overstrained users to cope with the ever changing technology.
Privacy exist wherever personal information or other sensitive information is collected, stored, used, and finally destroyed or deleted – in digital form or otherwise. The challenge of data privacy is to use data while safe-guarding individual's privacy preferences and their personally identifiable information. The fields of computer security, data security, and information security design and utilize software, hardware, and human resources to address this issue.