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Privacy: An Axiomatic Approach.

Alexander Ziller1,2, Tamara T Mueller1,2, Rickmer Braren2

  • 1Institute of Artificial Intelligence in Medicine, Technical University of Munich, 81675 Munich, Germany.

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|May 28, 2022
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Summary
This summary is machine-generated.

This study defines privacy using quantifiable information flows to address data collection threats. It enables objective policy discussions on privacy-enhancing technologies (PETs) by clarifying what they protect.

Keywords:
confidentialitydifferential privacyinformation flowprivacyprivacy-enhancing technologiessecrecy

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Area of Science:

  • Computer Science
  • Information Science
  • Social Science

Background:

  • Modern society faces privacy threats due to large-scale data collection.
  • Privacy-enhancing technologies (PETs) like differential privacy (DP) aim to mitigate these threats.
  • A rigorous definition of privacy is needed to effectively develop and implement PETs.

Purpose of the Study:

  • To formulate an axiomatic definition of privacy.
  • To base this definition on quantifiable and irreducible information flows.
  • To bridge social science perspectives with contemporary PETs.

Main Methods:

  • Developed an axiomatic definition of privacy.
  • Integrated concepts from social science and differential privacy (DP).
  • Focused on information-theoretic principles of privacy protection.

Main Results:

  • Proposed a novel, axiomatic definition of privacy.
  • Demonstrated that privacy protection challenges are fundamentally information-theoretic.
  • Established a framework for quantitative reasoning about PETs.

Conclusions:

  • The proposed definition offers a rigorous foundation for understanding and protecting privacy.
  • This work facilitates objective discourse on the societal implementation of PETs.
  • Understanding privacy as information flow is crucial for developing effective data protection strategies.