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A-COMPASS: Formal Foundations for Anonymity Analysis in Microdata

arXiv Security Archived Jun 19, 2026 ✓ Full text saved

arXiv:2606.20492v1 Announce Type: new Abstract: In the information age, one of the leading problems is how to ensure individual's privacy. Depending on the context in which privacy is considered, various data privacy models have emerged. However, the domain of formal verification of these models is still not sufficiently explored even when it comes to the most basic models. An attempt to verify privacy requirements is the Compliance Assertion Language (COMPASS). In COMPASS, one can specify an an

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    Computer Science > Cryptography and Security [Submitted on 18 Jun 2026] A-COMPASS: Formal Foundations for Anonymity Analysis in Microdata Tamara Tagliavia, Silvia Ghilezan In the information age, one of the leading problems is how to ensure individual's privacy. Depending on the context in which privacy is considered, various data privacy models have emerged. However, the domain of formal verification of these models is still not sufficiently explored even when it comes to the most basic models. An attempt to verify privacy requirements is the Compliance Assertion Language (COMPASS). In COMPASS, one can specify an anonymity condition that a table needs to satisfy, and an action that will modify the table if the condition is not satisfied. It is designed to operate on preprocessed tables in a form one record - one group of people. In this paper, we modify the COMPASS language in order to operate on microdata tables in their usual form of one record - one person. The modified language is called A-COMPASS. Along with checking of previously applied anonymity conditions, A-COMPASS enables the execution of anonymization actions as a new feature. We further provide the syntax and the semantics for the A-COMPASS language. We also prove the most important properties of the introduced semantics like determinism and compositionality. Finally, we provide a mechanism to verify anonymity properties, such as k-anonymity and l-diversity. Subjects: Cryptography and Security (cs.CR); Logic in Computer Science (cs.LO) Cite as: arXiv:2606.20492 [cs.CR]   (or arXiv:2606.20492v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.20492 Focus to learn more Submission history From: Tamara Tagliavia [view email] [v1] Thu, 18 Jun 2026 17:08:50 UTC (30 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs cs.LO References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Jun 19, 2026
    Archived
    Jun 19, 2026
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