Abstract
In the age of big data, data is increasing in a tremendous way in many fields and the data shared by the users is in a great risk. To preserve privacy of an individual anonymization-based algorithm like k-anonymity-related algorithm and differential privacy is proposed to make sure that the resulting dataset is free from privacy disclosure. However, majority of these anonymization algorithms are applied in isolated environment, without considering the utility in knowledge task making the dataset less informative. Also the presence of redundant data also decreases the performance and reduces accuracy of anonymization. Hence a preprocessing-based anonymization is required to increase the utility and to achieve accuracy in anonymization. This paper aims to apply the feature selection fast correlation-based filter (FCBF) solution to select the relevant features and remove the redundant data. Then k-anonymity is applied to dataset to achieve data anonymization. Comparisons on real-world dataset were made with anonymized dataset with preprocessing and without preprocessing and result was produced.
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All author states that there is no conflict of interest. We used our own data. Humans and animals are not involved in this research work.
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Selvi, U., Pushpa, S. (2020). Big Data Feature Selection to Achieve Anonymization. In: Bindhu, V., Chen, J., Tavares, J. (eds) International Conference on Communication, Computing and Electronics Systems. Lecture Notes in Electrical Engineering, vol 637. Springer, Singapore. https://doi.org/10.1007/978-981-15-2612-1_6
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DOI: https://doi.org/10.1007/978-981-15-2612-1_6
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