Big data is a term used for a very large data sets that have many difficulties in storing and pro... more Big data is a term used for a very large data sets that have many difficulties in storing and processing the data. Analysis this much amount of data will lead to information loss. The main goal of this paper is to share data in a way that privacy is preserved while information loss is kept at least. Data that include Government agencies, University details and Medical history etc., are very necessary for an organization to do analysis and predict trends and patterns, but it may prevent the data owner from sharing the data because of privacy regulations [1]. By doing an analysis of several algorithms of Anonymization such as k-anonymity, l-diversity and t-closeness, one can achieve privacy at minimum loss. Admitting these techniques has some limitations. We need to maintain trade-off between privacy and information loss. We introduce a novel approach called Differential Privacy.
The mission of connecting everything on the earth together via internet seems to be impossible.. ... more The mission of connecting everything on the earth together via internet seems to be impossible.. There will be the great effect on human life by Internet of Things (IOT), because with the help of IOT, many impossible things will become possible. IOT devices generates big data having useful, valuable and highly accurate data. It is difficult to extract the required information or data from the set of big data discovered by any device. For this purpose, data mining is used. Data mining will plays important role in constructing smart system that provides convenient services. It is required to extract data and knowledge from the connected things. For this purpose, various data mining techniques are used. Various algorithms such as classification, clustering, association rule mining etc helps to mine data. This paper represents the different Data mining techniques, challenges, and Data mining issues with IOT.
International Journal Of Data Mining And Emerging Technologies, 2013
ABSTRACT Feature selection is the important step in overall data mining process. There exist seve... more ABSTRACT Feature selection is the important step in overall data mining process. There exist several methods for feature selection. This paper discusses numerous methods of feature selection and their impact on the credit approval dataset. Cost-sensitive feature selection using a decision-tree-based algorithm is proposed (CSattribSelectorC4. 5). Sequential minimal optimisation (SMO) is used for classification. The classification results show that the CSattribSelectorC4. 5 helps SMO to reduce the overall misclassification cost on the ..
Advances in Intelligent Systems and Computing, 2015
Huge volume of detailed personal data is regularly collected and sharing of these data is proved ... more Huge volume of detailed personal data is regularly collected and sharing of these data is proved to be beneficial for data mining application. Data that include shopping habits, criminal records, medical history, credit records etc. are very necessary to an organization. To make analysis and predict the trends and patterns, such data is very important but it may prevent the data owners from sharing the data because of many privacy regulations. In order to share data while preserving privacy data owner must come up with a solution which achieves the dual goal of privacy preservation as well as accurate classification result. In Anonymization based Privacy preserving data mining, k-Anonymity is a model which provides privacy to individuals? data by masking the attribute values using generalization and suppression operation. But k-anonymity model is not safe from homogeneity attack and background knowledge attack. So, other techniques have been proposed which try to solve this attack. Proposed model solves these attacks and prevent data disclosure problem from intruders. In proposed model, correlation coefficient method is used which estimates amount of correlation between attributes and helps to automate the attribute selection process for generalization and suppression as user need not to select manually attribute for generalization and/or suppression. The main aim of proposed model is to increase the Privacy Gain and to maintain the accuracy of the data after anonymization.
Big data is a term used for a very large data sets that have many difficulties in storing and pro... more Big data is a term used for a very large data sets that have many difficulties in storing and processing the data. Analysis this much amount of data will lead to information loss. The main goal of this paper is to share data in a way that privacy is preserved while information loss is kept at least. Data that include Government agencies, University details and Medical history etc., are very necessary for an organization to do analysis and predict trends and patterns, but it may prevent the data owner from sharing the data because of privacy regulations [1]. By doing an analysis of several algorithms of Anonymization such as k-anonymity, l-diversity and t-closeness, one can achieve privacy at minimum loss. Admitting these techniques has some limitations. We need to maintain trade-off between privacy and information loss. We introduce a novel approach called Differential Privacy.
The mission of connecting everything on the earth together via internet seems to be impossible.. ... more The mission of connecting everything on the earth together via internet seems to be impossible.. There will be the great effect on human life by Internet of Things (IOT), because with the help of IOT, many impossible things will become possible. IOT devices generates big data having useful, valuable and highly accurate data. It is difficult to extract the required information or data from the set of big data discovered by any device. For this purpose, data mining is used. Data mining will plays important role in constructing smart system that provides convenient services. It is required to extract data and knowledge from the connected things. For this purpose, various data mining techniques are used. Various algorithms such as classification, clustering, association rule mining etc helps to mine data. This paper represents the different Data mining techniques, challenges, and Data mining issues with IOT.
International Journal Of Data Mining And Emerging Technologies, 2013
ABSTRACT Feature selection is the important step in overall data mining process. There exist seve... more ABSTRACT Feature selection is the important step in overall data mining process. There exist several methods for feature selection. This paper discusses numerous methods of feature selection and their impact on the credit approval dataset. Cost-sensitive feature selection using a decision-tree-based algorithm is proposed (CSattribSelectorC4. 5). Sequential minimal optimisation (SMO) is used for classification. The classification results show that the CSattribSelectorC4. 5 helps SMO to reduce the overall misclassification cost on the ..
Advances in Intelligent Systems and Computing, 2015
Huge volume of detailed personal data is regularly collected and sharing of these data is proved ... more Huge volume of detailed personal data is regularly collected and sharing of these data is proved to be beneficial for data mining application. Data that include shopping habits, criminal records, medical history, credit records etc. are very necessary to an organization. To make analysis and predict the trends and patterns, such data is very important but it may prevent the data owners from sharing the data because of many privacy regulations. In order to share data while preserving privacy data owner must come up with a solution which achieves the dual goal of privacy preservation as well as accurate classification result. In Anonymization based Privacy preserving data mining, k-Anonymity is a model which provides privacy to individuals? data by masking the attribute values using generalization and suppression operation. But k-anonymity model is not safe from homogeneity attack and background knowledge attack. So, other techniques have been proposed which try to solve this attack. Proposed model solves these attacks and prevent data disclosure problem from intruders. In proposed model, correlation coefficient method is used which estimates amount of correlation between attributes and helps to automate the attribute selection process for generalization and suppression as user need not to select manually attribute for generalization and/or suppression. The main aim of proposed model is to increase the Privacy Gain and to maintain the accuracy of the data after anonymization.
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Papers by Jalpesh Vasa