Efficient data perturbation for privacy preserving and accurate data stream mining
نویسندگان
چکیده
منابع مشابه
Privacy Preserving Data Stream Classification Using Data Perturbation Techniques
Data stream can be conceived as a continuous and changing sequence of data that continuously arrive at a system to store or process. Examples of data streams include computer network traffic, phone conversations, web searches and sensor data etc. These data sets need to be analyzed for identifying trends and patterns, which help us in isolating anomalies and predicting future behavior. However,...
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Data stream can be conceived as a continuous and changing sequence of data that continuously arrive at a system to store or process. Examples of data streams include computer network traffic, phone conversations, web searches and sensor data etc. The data owners or publishers may not be willing to exactly reveal the true values of their data due to various reasons, most notably privacy consider...
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Data mining is the information technology that extracts valuable knowledge from large amounts of data. Due to the emergence of data streams as a new type of data, data stream mining has recently become a very important and popular research issue. Privacy preservation issue of data streams mining is very important issue, in this dissertation work, an approach based on Geometric data perturbation...
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Title of Thesis: On Random Additive Perturbation for Privacy Preserving Data Mining Author: Souptik Datta, Master of Science, 2004 Thesis directed by: Dr. Hillol Kargupta, Associate Professor Department of Computer Science and Electrical Engineering Privacy is becoming an increasingly important issue in many data mining applications. This has triggered the development of many privacy-preserving...
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The major challenge of data perturbation is to achieve the desired balance between the level of privacy guarantee and the level of data utility. Data privacy and data utility are commonly considered as a pair of conflicting requirements in privacy-preserving data mining systems and applications. Multiplicative perturbation algorithms aim at improving data privacy while maintaining the desired l...
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ژورنال
عنوان ژورنال: Pervasive and Mobile Computing
سال: 2018
ISSN: 1574-1192
DOI: 10.1016/j.pmcj.2018.05.003