Abstract
Various privacy security standards are used in practical applications to decide research issues. The general strategies of privacy preservation are used to process the data security pledge at a lower privacy level. Furthermore, probability and mathematical models are introduced in order to maintain privacy. Privacy-preserving data mining models are used to preserve the privacy of different high-level data characteristics. The main objective of protecting privacy that is based on data release is to obtain a common approach of protecting privacy in different kinds of applications so that the designed algorithm could be implemented in a versatile way. This paper presents protected data extraction and presentation algorithm (PDEPA) that is used for data extraction and presentation with minimized error in classification and computation time, as well as increased privacy. The framework's development is aimed at ensuring that the running environment and applications are privacy compatible. The web-based distributed application's data extraction time and classification error are reduced.
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Sharma, V., Bhushan, S., Singh, A.K., Kumar, P. (2022). Privacy-Preserving Data Mining in Web Domain Using Protected Data Extraction and Presentation Algorithm. In: Pundir, A.K.S., Yadav, N., Sharma, H., Das, S. (eds) Recent Trends in Communication and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-1324-2_32
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DOI: https://doi.org/10.1007/978-981-19-1324-2_32
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