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Enhancing Transparency with Distributed Privacy-Preserving Logging

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Abstract

Transparency of data processing is often a requirement for compliance to legislation and/or business requirements. Furthermore, it has recognised as a key privacy principle, for example in the European Data Protection Directive. At the same time, transparency of the data processing should be limited to the users involved in order to minimise the leakage of sensitive business information and privacy of the employees (if any) performing the data processing.

We propose a cryptographic logging solution, making the resulting log data publicly accessible, that can be used by data subjects to gain insight in the data processing that takes place on their personal data, without disclosing any information about data processing on other users’ data. Our proposed solution can handle arbitrary distributed processes, dynamically continuing the logging from one data processor to the next. Committing to the logged data is irrevocable, and will result in log data that can be verified by the data subject, the data processor and a third party with respect to integrity. Moreover, our solution allows data processors to offload storage and interaction with users to dedicated log servers. Finally, we show that our scheme is applicable in practice, providing performance results for a prototype implementation.

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Notes

  1. 1.

    We use the technical terminology of data processor and user, as opposed to the EU Data Protection Directive in which a more formal/legal terminology (data controller, data subject) is used.

  2. 2.

    Schemes that do not support deletion detection are subject to so-called truncation attacks, for which the adversary can delete one or more consecutive entries at the end of a log.

References

  1. Bournez, Carine; and Ardagna, Claudio A.: Policy Requirements and State of the Art. Came- nisch, Fischer-Hubner and Rannenberg: Privacy and Identity Management for Life, ISBN 9783-642-20316-9, Springer, 2011, p. 295-312.

    Google Scholar 

  2. ECRYPT II: Yearly Report on Algorithms and Keysizes (2012). D.SPA.20 Rev. 1.0, ICT-2007- 216676 ECRYPT II, 2012.

    Google Scholar 

  3. Hedbom, Hans; Pulls, Tobias; Hjartquist, Peter; and Laven, Andreas: Adding Secure Transparency Logging to the PRIME Core. Bezzi, Duquenoy, Fischer-Hubner, Hansen and Zhang: Privacy and Identity Management for Life, ISBN 978-3-642-14281-9, Springer, 2010, p. 299-314

    Google Scholar 

  4. Ko, Ryan K.L.; Jagadpramana, Peter; Mowbray, Miranda; Pearson, Siani; Kirchberg, Markus; Liang, Qianhui; and Leek, Bu-Sung: TrustCloud: A Framework for Accountability and Trust in Cloud Computing. In: Proceedings of EuroPKI 2011. Camenisch and Costas: LNCS 6711, Springer, 2011, p. 584-588.

    Google Scholar 

  5. Pulls, Tobias; Wouters, Karel; Vliegen, Jo; and Grahn, Christian: Distributed Privacy-Preserving Log Trails. Karlstad University Studies 2012:24, 2012.

    Google Scholar 

  6. Roberts, John: No one is perfect: The limits of transparency and an ethic for ’intelligent’ accountability. Accounting, Organizations and Society 34(8), 2009.

    Google Scholar 

  7. Sackmann, Stefan; Struker, Jens; and Accorsi, Rafael: Personalization in Privacy-Aware Highly Dynamic Systems. Communications of the ACM 49(9), ACM, 2006, p. 32-38.

    Google Scholar 

  8. Schneier, Bruce; and Kelsey, John: Personalization Cryptographic Support for Secure Logs on Untrusted Machines. In: USENIX Security Symposium. USENIX, 1998, p. 53-62.

    Google Scholar 

  9. United Nations Department of Economic and Social Affairs: UN e-Government Survey 2012. E-Government for the People. ISBN 978-92-1-055353-7, 2012.

    Google Scholar 

  10. Wouters, Karel; Simoens, Koen; Lathouwers, Danny; Preneel, Bart: Secure and Privacy-Friendly Logging for eGovernment Services. In: ARES. IEEE Computer Society, 2008, p. 1091-1096.

    Google Scholar 

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Peeters, R., Pulls, T., Wouters, K. (2013). Enhancing Transparency with Distributed Privacy-Preserving Logging. In: Reimer, H., Pohlmann, N., Schneider, W. (eds) ISSE 2013 Securing Electronic Business Processes. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-03371-2_6

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  • DOI: https://doi.org/10.1007/978-3-658-03371-2_6

  • Publisher Name: Springer Vieweg, Wiesbaden

  • Print ISBN: 978-3-658-03370-5

  • Online ISBN: 978-3-658-03371-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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