On statistical disclosure control technologies for protecting personal data in tabular data sets
- Personal data disclosure in tabular sets
- Quantifying the risks and utility of tabular data sets
- Protecting tabular data sets
- Bargh, M.S., Latenko, A., Braak, S. van den, Vink, M., Meijer, R.
- Place of publication:
- The Hague
- Year of publication:
- Cahiers 2020-17
The objective of this study is to investigate statistical disclosures and the SDC technologies for protecting personal data in tabular data sets, especially in the context of opening privacy sensitive data sets (as in the case of, e.g., justice domain data sets).
The main research questions that will be addressed in this deliverable are:
Q1: What are the ways for disclosing personal data when tabular data sets are published?
Q2: What are the methods for protecting tabular data sets?
Q3: What are the main functionalities of available SDC tools for protecting personal data in tabular data sets and preserving data utility therein?
The intention is also to explore those state-of-the-art SDC mechanisms or functionalities that are not yet (widely) integrated in the studied SDC tools.
Summary in Dutch
Glossary of terms
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