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Data provenance to audit compliance with privacy policy in the Internet of Things

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Abstract

Managing privacy in the IoT presents a significant challenge. We make the case that information obtained by auditing the flows of data can assist in demonstrating that the systems handling personal data satisfy regulatory and user requirements. Thus, components handling personal data should be audited to demonstrate that their actions comply with all such policies and requirements. A valuable side-effect of this approach is that such an auditing process will highlight areas where technical enforcement has been incompletely or incorrectly specified. There is a clear role for technical assistance in aligning privacy policy enforcement mechanisms with data protection regulations. The first step necessary in producing technology to accomplish this alignment is to gather evidence of data flows. We describe our work producing, representing and querying audit data and discuss outstanding challenges.

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Notes

  1. https://www.ftc.gov/system/files/documents/reports/federal-trade-commission-staff-report-november-2013-workshop-entitled-internet-things-privacy/150127iotrpt.pdf, https://www.ftc.gov/system/files/documents/advocacy_documents/comment-staff-bureau-consumer-protection-office-policy-planning-national-telecommunications/160603ntiacomment.pdf

  2. https://www.w3.org/TR/prov-overview/, as stated in Section 2 building on standards is of fundamental importance for the interoperability of IoT systems.

  3. PBAC uses provenance information to make primary data access decisions, while PAC controls access to the provenance data itself.

  4. Apache Accumulo is a scalable open-source key/value store implementation based on the design of Google’s BigTable.

  5. http://activemq.apache.org/

  6. ISO/IEC 20922:2016 http://www.iso.org/iso/catalogue_detail.htm?csnumber=69466

  7. https://flume.apache.org/

  8. Code Civil Article 1316-1.

  9. https://www.ftc.gov/sites/default/files/documents/reports/federal-trade-commission-report-protecting-consumer-privacy-era-rapid-change-recommendations/120326privacyreport.pdf https://www.ftc.gov/sites/default/files/documents/reports/mobile-privacy-disclosures-building-trust-through-transparency-federal-trade-commission-staff-report/130201mobileprivacyreport.pdf

  10. https://www.ftc.gov/sites/default/files/documents/reports/mobile-privacy-disclosures-building-trust-through-transparency-federal-trade-commission-staff-report/130201mobileprivacyreport.pdf

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Acknowledgements

This work was supported by the US National Science Foundation under grant SSI-1450277 End-to-End Provenance, and the UK Engineering and Physical Sciences Research Council grant EP/K011510 CloudSafetyNet: End-to-end application security in the cloud. Cambridge authors acknowledge the support of Microsoft through the Microsoft Cloud Computing Research Centre.

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Authors and Affiliations

  1. Center for Research on Computation and Society, Harvard University, Cambridge, MA, USA

    Thomas Pasquier & Margo Seltzer

  2. Computer Laboratory, University of Cambridge, Cambridge, UK

    Jatinder Singh & Jean Bacon

  3. Computing and Information Science, Cornell Tech, New York, NY, USA

    Julia Powles

  4. Department of Computer Science, University of Otago, Dunedin, New Zealand

    David Eyers

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  1. Thomas Pasquier
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  2. Jatinder Singh
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  3. Julia Powles
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  4. David Eyers
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  6. Jean Bacon
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Correspondence to Thomas Pasquier.

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The software artefacts corresponding to our work to date are available online under a GNU General Public Licence at http://camflow.org/.

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Pasquier, T., Singh, J., Powles, J. et al. Data provenance to audit compliance with privacy policy in the Internet of Things. Pers Ubiquit Comput 22, 333–344 (2018). https://doi.org/10.1007/s00779-017-1067-4

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