Data Anonymization: Techniques and Models
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-0333-7_6
Reference13 articles.
1. Bayardo, R., & Agrawal, R. (2005). Data privacy through optimal k-anonymization. In 21st International Conference on Data Engineering (ICDE’05) (pp. 217–228)
2. Bingchun, L., & Guohua, L. (2011). The classification of k-anonymity data. In 2011 Seventh International Conference on Computational Intelligence and Security (pp. 1374–1378)
3. Carvalho, G., Mykolyshyn, S., Cabral, B., Bernardino, J., & Pereira, V. (2022). Comparative analysis of data modeling design tools. IEEE Access, 10, 3351–3365.
4. Enam, M. A., Sakib, S., & Rahman, M. S. (2019). An algorithm for l-diversity clustering of a point-set. In 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) (pp. 1–6)
5. Karle, T., & Vora, D. (2017). Privacy preservation in big data using anonymization techniques. In 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI) (pp. 340–343)
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