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A Rule-Based Approach to Local Anonymization for Exclusivity Handling in Statistical Databases

  • Statistical databases in general and data warehouses in particular are used to analyze large amounts of business data in pre-defined as well as ad-hoc reports. Operators of statistical databases must ensure that individual sensitive data, e.g., personal data, medical data, or business-critical data, are not revealed to unprivileged users while making use of these data in aggregates. Business rules must be defined and enforced to prevent disclosure. The unsupervised nature of ad-hoc reports, defined by the user and unknown to the database operator upfront, adds to the complexity of keeping data secure. Storing sensitive data in statistical databases demands automated methods to prevent direct or indirect disclosure of such sensitive data. This document describes a rule-based approach to local recoding of sensitive data. It introduces the notion of exclusivity to describe quasiidentifiers with local rules based on the multidimensional data model. It further defines options to treat exclusive entities that may disclose sensitive data.Statistical databases in general and data warehouses in particular are used to analyze large amounts of business data in pre-defined as well as ad-hoc reports. Operators of statistical databases must ensure that individual sensitive data, e.g., personal data, medical data, or business-critical data, are not revealed to unprivileged users while making use of these data in aggregates. Business rules must be defined and enforced to prevent disclosure. The unsupervised nature of ad-hoc reports, defined by the user and unknown to the database operator upfront, adds to the complexity of keeping data secure. Storing sensitive data in statistical databases demands automated methods to prevent direct or indirect disclosure of such sensitive data. This document describes a rule-based approach to local recoding of sensitive data. It introduces the notion of exclusivity to describe quasiidentifiers with local rules based on the multidimensional data model. It further defines options to treat exclusive entities that may disclose sensitive data. The local anonymization minimizes information loss and works well even with very large data sets.show moreshow less
Metadaten
Author:Jens AlbrechtORCiD, Marc Fiedler, Tim Kiefer
DOI:https://doi.org/10.1007/978-3-319-45381-1_7
Parent Title (English):In: Domingo-Ferrer, J., Pejić-Bach, M. (eds) Privacy in Statistical Databases. PSD 2016. Lecture Notes in Computer Science, vol 9867.
Publisher:Springer
Place of publication:Cham
Document Type:conference proceeding (article)
Language:English
Release Date:2023/06/14
Pagenumber:14
Licence (German):Keine Lizenz - Deutsches Urheberrecht gilt
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