Huge volume of data from domain specific applications such as medical, financial, telephone, shopping records and individuals are regularly generated. Sharing of these data is proved to be beneficial for data mining application. Since data mining often involves data that contains personally identifiable information and therefore releasing such data may result in privacy breaches. On one hand such data is an important asset to business decision making by analyzing it. On the other hand data privacy concerns may prevent data ...
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Huge volume of data from domain specific applications such as medical, financial, telephone, shopping records and individuals are regularly generated. Sharing of these data is proved to be beneficial for data mining application. Since data mining often involves data that contains personally identifiable information and therefore releasing such data may result in privacy breaches. On one hand such data is an important asset to business decision making by analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information for data analysis. 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 accuracy of data mining task mainly clustering and classification. Existing techniques for privacy preserving data mining is designed for traditional static data sets and are not suitable for data streams. Privacy preserving data stream mining is an emerging research area in the field of privacy aware data mining.
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Add this copy of Privacy Preserving Data Mining-Issues & Techniques: to cart. £82.74, new condition, Sold by Media Smart rated 3.0 out of 5 stars, ships from Hawthorne, CA, UNITED STATES, published 2014 by Scholars' Press.
Add this copy of Privacy Preserving Data Mining-Issues & Techniques: to cart. £106.50, good condition, Sold by Bonita rated 4.0 out of 5 stars, ships from Santa Clarita, CA, UNITED STATES, published 2014 by Scholars' Press.
