Abstract
In recent days, the personal health information is collected in different fields. When the data is shared for different reasons, it poses a major danger to the field of health care. A number of anonymization methods are implemented to maintain person’s privacy. The existing methods of anonymization support only single sensitive and low-dimensional data. In our recent experiment, a method of anonymization is expected in order to anonymize high-dimensional data with multiple sensitive attributes. In line with the concept of k-anonymity and l-diversity, it combines anatomization and improved slicing strategy. The experimental findings show that it is limited to the static discharge of information only. In dynamic situations, the current technique may produce poor quality or high data loss. Hence, in this proposed approach, an anonymization model is designed in such a way to anonymize continuously growing dataset while assuring high utility.
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Shyamala Susan, V. (2021). An Anonymization Approach for Dynamic Dataset with Multiple Sensitive Attributes. In: Dash, S.S., Das, S., Panigrahi, B.K. (eds) Intelligent Computing and Applications. Advances in Intelligent Systems and Computing, vol 1172. Springer, Singapore. https://doi.org/10.1007/978-981-15-5566-4_65
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DOI: https://doi.org/10.1007/978-981-15-5566-4_65
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