Abstract
Analyzing data is a cost-intensive process, particularly for organizations lacking the necessary in-house human and computational capital. Data analytics outsourcing offers a cost-effective solution, but data sensitivity and query response time requirements, make data protection a necessary pre-processing step. For performance and privacy reasons, anonymization is preferred over encryption. Yet, manual anonymization is time-intensive and error-prone. Automated anonymization is a better alternative but requires satisfying the conflicting objectives of utility and privacy. In this paper, we present an automated anonymization scheme that extends the standard k-anonymization and l-diversity algorithms to satisfy the dual objectives of data utility and privacy. We use a multi-objective optimization scheme that employs a weighting mechanism, to minimise information loss and maximize privacy. Our results show that automating l-diversity results in an added average information loss of 7 % over automated k-anonymization, but in a diversity of between 9–14 % in comparison to 10–30 % in k-anonymised datasets. The lesson that emerges is that automated l-diversity offers better privacy than k-anonymization and with negligible information loss.
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Notes
- 1.
Quasi-identifiers: Attributes which independently or combined can be used to uniquely identify an individual.
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Acknowledgements
The authors gratefully acknowledge funding for this research provided by the National Research Foundation (NRF) of South Africa, and the Hasso-Plattner-Institute (HPI). In addition, the authors are grateful for the anonymous reviews.
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Kayem, A.V.D.M., Vester, C.T., Meinel, C. (2016). Automated k-Anonymization and l-Diversity for Shared Data Privacy. In: Hartmann, S., Ma, H. (eds) Database and Expert Systems Applications. DEXA 2016. Lecture Notes in Computer Science(), vol 9827. Springer, Cham. https://doi.org/10.1007/978-3-319-44403-1_7
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