Optimized Pattern Mining Of Sample Data

Journal of Computer Science, 2009

Problem statement: Driven by mutual benefits, or by regulations that require certain data to be published, there has been a demand for the exchange and publication of data among various parties. Data publishing has been ubiquitous in many domains such as medical, business and education. Detailed person-specific data, present in the centralized server or in the distributed environment, in its original form often contains sensitive information about individuals, and publishing such data immediately violates individual privacy. The main problem in this regard is to develop method for publishing data in a more hostile environment so that the published data remains practically useful while individual privacy is preserved. There are n parties, each having a private database, want to jointly conduct a data mining operation on the union of their databases. How could these parties accomplish this without disclosing their database to the other parties or any third party? Approach: To address this issue, we developed a simple technique of transforming the categorical and numeric sensitive data using a mapping table and graded grouping technique, respectively. The typical data mining tasks such as classification, clustering and association rule mining were performed on both the original and transformed tables. The rules/results/patterns of both the tables were compared and the utility of the transformed data was evaluated. Results: The evaluation results demonstrated that the proposed approach was able to achieve cent percent utility for any type of mining task as compared to the original table. The classification accuracy of Adult data set obtained, with education as class variable was 40.08% and the same accuracy was obtained even after transformation. Similarly the number of rules generated for the given confidence 0.9, was the same for both the original and transformed table and equal to 10. Conclusion: The association rules involving categorical sensitive attributes were checked manually for privacy breach. We found that it is not possible to guess the actual sensitive values from the rules, even though there was no information loss. The results can be interpreted only with the concern of data owner or data publisher.

Data Science Journal, 2010

Problem statement: Driven by mutual benefits, or by regulations that require certain data to be published, there has been a demand for the exchange and publication of data among various parties. Data publishing has been ubiquitous in many domains such as medical, business and education. Detailed person-specific data, present in the centralized server or in the distributed environment, in its original form often contains sensitive information about individuals, and publishing such data immediately violates individual privacy. The main problem in this regard is to develop method for publishing data in a more hostile environment so that the published data remains practically useful while individual privacy is preserved. There are n parties, each having a private database, want to jointly conduct a data mining operation on the union of their databases. How could these parties accomplish this without disclosing their database to the other parties or any third party? Approach: To address this issue, we developed a simple technique of transforming the categorical and numeric sensitive data using a mapping table and graded grouping technique, respectively. The typical data mining tasks such as classification, clustering and association rule mining were performed on both the original and transformed tables. The rules/results/patterns of both the tables were compared and the utility of the transformed data was evaluated. Results: The evaluation results demonstrated that the proposed approach was able to achieve cent percent utility for any type of mining task as compared to the original table. The classification accuracy of Adult data set obtained, with education as class variable was 40.08% and the same accuracy was obtained even after transformation. Similarly the number of rules generated for the given confidence 0.9, was the same for both the original and transformed table and equal to 10. Conclusion: The association rules involving categorical sensitive attributes were checked manually for privacy breach. We found that it is not possible to guess the actual sensitive values from the rules, even though there was no information loss. The results can be interpreted only with the concern of data owner or data publisher.

The development in data mining technology brings serious threat to the individual information. The objective of privacy preserving data mining (PPDM) is to safeguard the sensitive information contained in the data. The unwanted disclosure of the sensitive information may happen during the process of data mining results. In this study we identify four different types of users involved in mining application i.e. data source provider, data receiver, data explorer and determiner decision maker. We would like to provide useful insights into the study of privacy preserving data mining. This paper presents a comprehensive noise addition technique for protecting individual privacy in a data set used for classification, while maintaining the data quality. We add noise to all attributes, both numerical and categorical, and both to class and non-class, in such a way so that the original patterns are preserved in a perturbed data set. Our technique is also capable of incorporating previously proposed noise addition techniques that maintain the statistical parameters of the data set, including correlations among attributes. Thus the perturbed data set may be used not only for classification but also for statistical analysis.

Crossroads, 2009

As it becomes evident, there exists an extended set of application scenarios in which information or knowledge derived from the data must be shared with other (possibly untrusted) entities. The sharing of data and/or knowledge may come at a cost to privacy, primarily due to two reasons:

Data mining is the process of finding correlations or patterns among the dozens of fields in large database. A fruitful direction for data mining research will be the development of techniques that incorporate privacy concerns. Since primary task in our paper is that accurate data which we retrieve should be somewhat changed while providing to users. For this reason, recently much research effort has been devoted for addressing the problem of providing security in data mining. We consider the concrete case of building a decision tree classifier from data in which the values of individual records have been reconstructed. The resulting data records look very different from the original records and the distribution of data values is also very different from the original distribution. By using these reconstructed distribution we are able to build classifiers whose accuracy is comparable to the accuracy of classifiers built with the original data.

Jurnal Elektronika dan Telekomunikasi

Nowadays, data from various sources are gathered and stored in databases. The collection of the data does not give a significant impact unless the database owner conducts certain data analysis such as using data mining techniques to the databases. Presently, the development of data mining techniques and algorithms provides significant benefits for the information extraction process in terms of the quality, accuracy, and precision results. Realizing the fact that performing data mining tasks using some available data mining algorithms may disclose sensitive information of data subject in the databases, an action to protect privacy should be taken into account by the data owner. Therefore, privacy preserving data mining (PPDM) is becoming an emerging field of study in the data mining research group. The main purpose of PPDM is to investigate the side effects of data mining methods that originate from the penetration into the privacy of individuals and organizations. In addition, it gu...

Advances in Database Technology - EDBT 2004, 2004

In recent years, privacy preserving data mining has become an important problem because of the large amount of personal data which is tracked by many business applications. In many cases, users are unwilling to provide personal information unless the privacy of sensitive information is guaranteed. In this paper, we propose a new framework for privacy preserving data mining of multi-dimensional data. Previous work for privacy preserving data mining uses a perturbation approach which reconstructs data distributions in order to perform the mining. Such an approach treats each dimension independently and therefore ignores the correlations between the different dimensions. In addition, it requires the development of a new distribution based algorithm for each data mining problem, since it does not use the multi-dimensional records, but uses aggregate distributions of the data as input. This leads to a fundamental re-design of data mining algorithms. In this paper, we will develop a new and flexible approach for privacy preserving data mining which does not require new problem-specific algorithms, since it maps the original data set into a new anonymized data set. This anonymized data closely matches the characteristics of the original data including the correlations among the different dimensions. We present empirical results illustrating the effectiveness of the method.

uotechnology.edu.iq

The results of data Mining (DM) such as association rules, classes, clusters, etc, will be readily available for working team. So the mining will penetrate the privacy of sensitive data and makes the stolen of the knowledge resulted much more easily. The main objective of ...

arXiv (Cornell University), 2023

Data mining is the way toward mining fascinating patterns or information from an enormous level of the database. Data mining additionally opens another risk to privacy and data security.One of the maximum significant themes in the research fieldis privacy-preserving DM (PPDM). Along these lines, the investigation of ensuring delicate information and securing sensitive mined snippets of data without yielding the utility of the information in a dispersed domain.Extracted information from the analysis can be rules, clusters, meaningful patterns, trends or classification models. Privacy breach occur at some stage in the communication of data and aggregation of data. So far, many effective methods and techniques have been developed for privacy-preserving data mining, but yields into information loss and side effects on data utility and data mining effectiveness downgraded. In the focal point of consideration on the viability of Data Mining, Privacy and rightness should be improved and to lessen the expense.

Privacy preserving has become crucial in knowledge-based applications. And proper integration of individual privacy is essential for data mining operations. This privacy-based data mining is important for sectors such as healthcare, pharmaceuticals, investigation and security service providers, where the data mining is transformed into cooperative task among individuals. Data mining is successful in many applications, data mining refers special concerns for private data. In data mining, clustering algorithms are most of skilled and frequently used frameworks. The integrated architecture takes a systemic view of the problem of implementing established protocols for data collection, inference control, information sharing and keeping information safety. The goal is to investigating privacy preservation issues was to take a systemic view of the architectural requirements and also design principles and explore possible solutions that would lead to the guidelines for buildup practical privacy-preserving data mining systems. In this paper, we propose the methods which uses formula-based technique for sharing of secret data in privacy-preserving mechanism. The process includes formula-based methodology which enables the information to be partitioned into numerous shares and handled independently at various servers. This paper surveys the most relevant Privacy preserving data mining 'PPDM' techniques from the literature are used to evaluate such techniques and presents the typical applications of PPDM methods in relevant fields. The ongoing current challenges and open issues in PPDM are discussed in the paper.