Skip to main content

Selecting Privacy Enhancing Technologies for IoT-Based Services

  • Conference paper
  • First Online:
Security and Privacy in Communication Networks (SecureComm 2020)

Abstract

The rising number of IoT devices enables the provisioning of novel services in various domains, such as the automotive domain. This data, however, is often personal or otherwise sensitive. Providers of IoT-based services are confronted with the problem of collecting the necessary amount and quality of data, while at the same time protecting persons’ privacy using privacy enhancing technologies (PETs). Selecting appropriate PETs is neither trivial, nor is it uncritical since applying an unsuitable PET can result in a violation of privacy rights, e.g. according to the GDPR. In this paper, we propose a process to select data-dependent PETs—i.e. technologies which manipulate data, e.g. by distorting values—for IoT-based services. The process takes into account two perspectives on the selection of PETs which both narrow down the number of potentially applicable PETs: First, a data-driven perspective which is based on the data’s properties, e.g. its longevity and sequentiality; and second, a service-driven perspective which takes into account service requirements, e.g. the precision required to provide a particular service. We then show how the process can be applied for automotive services proposing a taxonomy for automotive data and present an exemplary application.

In this way, we aim at providing a reproducible method of selecting PETs that is more specific than existing approaches, and which can be applied both as a standalone process and complementary to existing ones.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://aws.amazon.com/iot/.

  2. 2.

    https://aos.bmwgroup.com/.

  3. 3.

    https://www.genivi.org.

  4. 4.

    https://github.com/GENIVI/vehicle_signal_specification.

References

  1. Birnstill, P., Ren, D., Beyerer, J.: A user study on anonymization techniques for smart video surveillance. In: 12th International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6. IEEE (2015)

    Google Scholar 

  2. Dalenius, T.: Finding a needle in a haystack or identifying anonymous census records. J. Off. Stat. 2(3), 329 (1986)

    Google Scholar 

  3. Dandekar, R.A., Domingo-Ferrer, J., Sebé, F.: LHS-based hybrid microdata vs rank swapping and microaggregation for numeric microdata protection. In: Domingo-Ferrer, J. (ed.) Inference Control in Statistical Databases. LNCS, vol. 2316, pp. 153–162. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47804-3_12

    Chapter  Google Scholar 

  4. De Wolf, P.P., Gouweleeuw, J., Kooiman, P., Willenborg, L., et al.: Reflections on PRAM. In: Statistical Data Protection, pp. 337–349. Citeseer (1998)

    Google Scholar 

  5. Deng, M., Wuyts, K., Scandariato, R., Preneel, B., Joosen, W.: A privacy threat analysis framework: supporting the elicitation and fulfillment of privacy requirements. Requir. Eng. 16(1), 3–32 (2011). https://doi.org/10.1007/s00766-010-0115-7

    Article  Google Scholar 

  6. Domingo-Ferrer, J.: Microaggregation for database and location privacy. In: Etzion, O., Kuflik, T., Motro, A. (eds.) NGITS 2006. LNCS, vol. 4032, pp. 106–116. Springer, Heidelberg (2006). https://doi.org/10.1007/11780991_10

    Chapter  Google Scholar 

  7. Domingo-Ferrer, J., Sebé, F., Castellà-Roca, J.: On the security of noise addition for privacy in statistical databases. In: Domingo-Ferrer, J., Torra, V. (eds.) PSD 2004. LNCS, vol. 3050, pp. 149–161. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-25955-8_12

    Chapter  Google Scholar 

  8. Duckham, M., Kulik, L.: A formal model of obfuscation and negotiation for location privacy. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) Pervasive 2005. LNCS, vol. 3468, pp. 152–170. Springer, Heidelberg (2005). https://doi.org/10.1007/11428572_10

    Chapter  Google Scholar 

  9. Fischetti, M., González, J.J.S.: Models and algorithms for optimizing cell suppression in tabular data with linear constraints. J. Am. Stat. Assoc. 95(451), 916–928 (2000)

    Article  Google Scholar 

  10. Fleming, W.J.: Overview of automotive sensors. Sens. J. 1(4), 296–308 (2001)

    Article  Google Scholar 

  11. Fleming, W.J.: New automotive sensors—A review. Sens. J. 8(11), 1900–1921 (2008)

    Article  Google Scholar 

  12. Hoh, B., Gruteser, M., Xiong, H., Alrabady, A.: Enhancing security and privacy in traffic-monitoring systems. Pervasive Comput. 5(4), 38–46 (2006)

    Article  Google Scholar 

  13. Hoh, B., Gruteser, M., Xiong, H., Alrabady, A.: Preserving privacy in GPS traces via uncertainty-aware path cloaking. In: Proceedings of the 14th ACM Conference on Computer and Communications Security, pp. 161–171. ACM (2007)

    Google Scholar 

  14. Hornung, G.: Verfügungsrechte an fahrzeugbezogenen Daten [Rights of disposition for vehicle-related data]. Datenschutz und Datensicherheit-DuD 39(6), 359–366 (2015)

    Article  Google Scholar 

  15. Hundepool, A., et al.: Statistical Disclosure Control. Wiley, Hoboken (2012)

    Book  Google Scholar 

  16. Kido, H., Yanagisawa, Y., Satoh, T.: An anonymous communication technique using dummies for location-based services. In: Proceedings of the 2005 International Conference on Pervasive Services, ICPS 2005, pp. 88–97. IEEE (2005)

    Google Scholar 

  17. Little, R.J.: Statistical analysis of masked data. J. Off. Stat. 9(2), 407 (1993)

    Google Scholar 

  18. Luna, J., Suri, N., Krontiris, I.: Privacy-by-design based on quantitative threat modeling. In: 7th International Conference on Risks and Security of Internet and Systems (CRiSIS), pp. 1–8. IEEE (2012)

    Google Scholar 

  19. Moore, R.: Controlled data-swapping techniques for masking public use microdata sets. US Census Bureau [Custodian] (1996)

    Google Scholar 

  20. Notario, N., et al.: PRIPARE: integrating privacy best practices into a privacy engineering methodology. In: Security and Privacy Workshops, pp. 151–158. IEEE (2015)

    Google Scholar 

  21. Oliver, I.: Experiences in the development and usage of a privacy requirements framework. In: 24th International Requirements Engineering Conference (RE), pp. 293–302. IEEE (2016)

    Google Scholar 

  22. Potter, B.: Microsoft SDL threat modelling tool. Netw. Secur. 1, 15–18 (2009)

    Article  Google Scholar 

  23. Qian, J., et al.: VoiceMask: anonymize and sanitize voice input on mobile devices. arXiv preprint arXiv:1711.11460 (2017)

  24. Rubin, D.B.: Statistical disclosure limitation. J. Off. Stat. 9(2), 461–468 (1993)

    Google Scholar 

  25. Samarati, P.: Protecting respondents identities in microdata release. Trans. Knowl. Data Eng. 13(6), 1010–1027 (2001)

    Article  Google Scholar 

  26. Spiekermann, S., Cranor, L.F.: Engineering privacy. Trans. Softw. Eng. 35(1), 67–82 (2009)

    Article  Google Scholar 

  27. Torra, V.: Microaggregation for categorical variables: a median based approach. In: Domingo-Ferrer, J., Torra, V. (eds.) PSD 2004. LNCS, vol. 3050, pp. 162–174. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-25955-8_13

    Chapter  Google Scholar 

Download references

Acknowledgment

This work was partly funded by the Bavarian Ministry of Economic Affairs and Media, Energy and Technology, within the project Bayern-Cloud.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Immanuel Kunz .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kunz, I., Banse, C., Stephanow, P. (2020). Selecting Privacy Enhancing Technologies for IoT-Based Services. In: Park, N., Sun, K., Foresti, S., Butler, K., Saxena, N. (eds) Security and Privacy in Communication Networks. SecureComm 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 336. Springer, Cham. https://doi.org/10.1007/978-3-030-63095-9_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-63095-9_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63094-2

  • Online ISBN: 978-3-030-63095-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics