Defining Anonymity Properties of Datasets with the Compliance Assertion Language (COMPASS)

Author:

Göbel Richard1ORCID,Kitzing Stephanie1ORCID

Affiliation:

1. Institute for Information Systems at Hof University, Germany

Abstract

Organizations that manage Personally Identifiable Information cannot share this information directly due to legal restrictions. Meanwhile, there are several solutions that support the anonymization of these types of data to make these available to a wider audience. For these solutions, it is important that the corresponding anonymization modules guarantee legal properties. In general, due to the scale and complexity of the software, it is difficult to prove that it does not violate these properties in some cases. This article proposes a new approach that addresses this challenge. The approach provides a software component that checks the output of an anonymization module against editable legal constraints—the Privacy-Enhancing Verification Component (PE-VC). An organization can formulate these constraints separately from the software using the new Compliance Assertion Language. Because the PE-VC is a carefully developed and verified module that can be used without modification for different anonymization modules, an auditor only needs to check the specified assertions and not the software itself. This approach ensures a much higher level of confidence in the correctness of the output of an anonymization software.

Funder

Federal Ministry for Digital and Transport of Germany

Publisher

Association for Computing Machinery (ACM)

Subject

Public Administration,Software,Information Systems,Computer Science Applications,Computer Networks and Communications

Reference28 articles.

1. Privacy by design: the definitive workshop. A foreword by Ann Cavoukian, Ph.D

2. Centre for Data Ethics and Innovation. 2021. Privacy Enhancing Technologies Adoption Guide—What Are PETs? Retrieved July 22 2022 from https://cdeiuk.github.io/pets-adoption-guide/what-are-pets.

3. Coordination Office for IT Standards (KoSIT) Bremen Germany. 2020. DSMeld. Retrieved August 25 2020 from https://www1.osci.de/meldewesen/dsmeld/dsmeld-9-aenderung-22310.

4. Finding a needle in a haystack or identifying anonymous census records;Dalenius Tore;J. Offic. Stat.,1986

5. European Parliament Council of the European Union. 2016. Article 25 of the Regulation

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