Abstract
A quasi attribute refers to a distinct subset of unique attributes that can adequately recognize tuples in a table. Hasty distribution of the quasi attributes will prompt privacy leakage. Choosing private data from a list of attributes is decided by the publisher, and it undoubtedly changes from dataset to dataset. The need for dynamically choosing and informing systems about a quasi and a non-quasi attribute remains a challenging task. Presently, there is no particular automation model for the classification of quasi and non-quasi. It could be a burden when a massive dataset has to be classified, or aggregation of datasets has to be performed.
This research paper considers the need to categorize quasi attributes for a non-expert through a direct attack and proposes a solution through the game theory approach and reinforcement machine learning model. For demonstration, a \(2 \times 2\) state matrix is considered. The results include case-wise time consumption and comparison among all necessary steps for accurate navigation, between various attributes. Among all the notable cases, the matrix arrangement with a quasi attribute in \(00^{th}\) and \(11^{th}\)position, non-quasi in \(01^{th}\) and \(10^{th}\) position obtained better performance. This reinforcement-based solution helps the automation of the classification of quasi and non-quasi attributes.
Visvesvaraya Technological University, Belagavi.
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Yaji, S., Bayyapu, N. (2023). Reinforcement Technique for Classifying Quasi and Non-quasi Attributes for Privacy Preservation and Data Protection. In: Prabhu, S., Pokhrel, S.R., Li, G. (eds) Applications and Techniques in Information Security . ATIS 2022. Communications in Computer and Information Science, vol 1804. Springer, Singapore. https://doi.org/10.1007/978-981-99-2264-2_1
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