Abstract
Data anonymization is the process of making information contained in a group of data such that it is not possible to identify unique references to single elements in the group after the process. This action, when conducted onto datasets used to make statistical inference is bound to have ananlogous behaviours on certain indices before and after the process itself. In this paper we study the pipeline of anonymization process for datasets, when this pipeline is managed on cloud technology, where cryptography is not applicable at all, for datasets being available in an open setting. We examine the open problems, and devise a method to address these problems in a logical framework.
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Notes
- 1.
We introduce here the notion of analytical properties in terms of statistical measures, the most common properties desired in dataset anonymization.
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Cristani, M., Tomazzoli, C. (2020). Dataset Anonyization on Cloud: Open Problems and Perspectives. In: Brambilla, M., Cappiello, C., Ow, S. (eds) Current Trends in Web Engineering. ICWE 2019. Lecture Notes in Computer Science(), vol 11609. Springer, Cham. https://doi.org/10.1007/978-3-030-51253-8_9
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