chownIoT: Enhancing IoT Privacy by Automated Handling of Ownership Change

  • Md Sakib Nizam KhanEmail author
  • Samuel Marchal
  • Sonja Buchegger
  • N. Asokan
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 547)


Considering the increasing deployment of smart home IoT devices, their ownership is likely to change during their life-cycle. IoT devices, especially those used in smart home environments, contain privacy-sensitive user data, and any ownership change of such devices can result in privacy leaks. The problem arises when users are either not aware of the need to reset/reformat the device to remove any personal data, or not trained in doing it correctly as it can be unclear what data is kept where. In addition, if the ownership change is due to theft or loss, then there is no opportunity to reset. Although there has been a lot of research on security and privacy of IoT and smart home devices, to the best of our knowledge, there is no prior work specifically on automatically securing ownership changes. We present a system called Open image in new window for securely handling ownership change of IoT devices. Open image in new window combines authentication (of both users and their smartphone), profile management, data protection by encryption, and automatic inference of ownership change. For the latter, we use a simple technique that leverages the context of a device. Finally, as a proof of concept, we develop a prototype that implements Open image in new window inferring ownership change from changes in the WiFi SSID. The performance evaluation of the prototype shows that Open image in new window has minimal overhead and is compatible with the dominant IoT boards on the market.


Ownership Privacy Smart home IoT 



This work is supported by the Academy of Finland under the WiFiUS program (grant 309994), the Wallenberg AI, Autonomous Systems and Software Program (WASP), and the Swedish Foundation for Strategic Research (grant SSF FFL09-0086).


  1. 1.
    Apthorpe, N., Reisman, D., Feamster, N.: Closing the blinds: four strategies for protecting smart home privacy from network observers. arXiv preprint arXiv:1705.06809 (2017)
  2. 2.
    Apthorpe, N., Reisman, D., Feamster, N.: A smart home is no castle: Privacy vulnerabilities of encrypted IoT traffic. arXiv preprint arXiv:1705.06805 (2017)
  3. 3.
    Bohn, J.: Instant personalization and temporary ownership of handheld devices. In: 2004 Sixth IEEE Workshop on Mobile Computing Systems and Applications, WMCSA 2004, pp. 134–143. IEEE (2004)Google Scholar
  4. 4.
    Ertaul, L., Mudan, A., Sarfaraz, N.: Performance comparison of AES-CCM and AES-GCM authenticated encryption modes. In: Proceedings of the International Conference on Security and Management (SAM), p. 331. The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp) (2016)Google Scholar
  5. 5.
    Francis, L., Hancke, G., Mayes, K., Markantonakis, K.: Practical NFC peer-to-peer relay attack using mobile phones. In: Ors Yalcin, S.B. (ed.) RFIDSec 2010. LNCS, vol. 6370, pp. 35–49. Springer, Heidelberg (2010). Scholar
  6. 6.
    Giry, D.: Keylength - NIST report on cryptographic key length and cryptoperiod (2016). (2017). Accessed 26 May 2017
  7. 7.
    Hu, J., Weaver, A.C.: A dynamic, context-aware security infrastructure for distributed healthcare applications. In: Proceedings of the First Workshop on Pervasive Privacy Security, Privacy, and Trust, pp. 1–8. Citeseer (2004)Google Scholar
  8. 8.
    Jih, W.R., Cheng, S.y., Hsu, J.Y., Tsai, T.M., et al.: Context-aware access control in pervasive healthcare. In: Computer Science and Information Engineering, National Taiwan University, Taiwan (2005)Google Scholar
  9. 9.
    Kapsalis, V., Hadellis, L., Karelis, D., Koubias, S.: A dynamic context-aware access control architecture for e-services. Comput. Secur. 25(7), 507–521 (2006)CrossRefGoogle Scholar
  10. 10.
    Khan, M.: Enhancing privacy in IoT devices through automated handling of ownership change. Master’s thesis, School of Science, Aalto University, Finland 28 August 2017.
  11. 11.
    Kumar, Y., Munjal, R., Sharma, H.: Comparison of symmetric and asymmetric cryptography with existing vulnerabilities and countermeasures. Int. J. Comput. Sci. Manag. Stud. 11(03), 60–63 (2011)Google Scholar
  12. 12.
    Lenstra, A.K., Verheul, E.R.: Selecting cryptographic key sizes. J. Cryptol. 14(4), 255–293 (2001)MathSciNetCrossRefGoogle Scholar
  13. 13.
    McGrew, D., Bailey, D.: AES-CCM cipher suites for Transport Layer Security (TLS). Technical report (2012)Google Scholar
  14. 14.
    Miettinen, M., Asokan, N., Nguyen, T.D., Sadeghi, A.R., Sobhani, M.: Context-based zero-interaction pairing and key evolution for advanced personal devices. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pp. 880–891. ACM (2014)Google Scholar
  15. 15.
    Miettinen, M., Heuser, S., Kronz, W., Sadeghi, A.R., Asokan, N.: ConXsense: automated context classification for context-aware access control. In: Proceedings of the 9th ACM Symposium on Information, Computer and Communications Security, pp. 293–304. ACM (2014)Google Scholar
  16. 16.
    Miettinen, M., Marchal, S., Hafeez, I., Asokan, N., Sadeghi, A.R., Tarkoma, S.: IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT. arXiv preprint arXiv:1611.04880 (2016)
  17. 17.
    Pradeep, B., Singh, S.: Ownership authentication transfer protocol for ubiquitous computing devices. arXiv preprint arXiv:1208.1712 (2012)
  18. 18.
    Ren, B., Liu, C., Cheng, B., Hong, S., Zhao, S., Chen, J.: EasyGuard: enhanced context-aware adaptive access control system for android platform: poster. In: Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking, pp. 458–459. ACM (2016)Google Scholar
  19. 19.
    Rescorla, E.: RFC 2631 - Diffie-Hellman key agreement method. (1999). Accessed 04 May 2017
  20. 20.
    Shrestha, B., Saxena, N., Truong, H.T.T., Asokan, N.: Contextual proximity detection in the face of context-manipulating adversaries. arXiv preprint arXiv:1511.00905 (2015)
  21. 21.
    Tam, P., Newmarch, J.: Protocol for ownership of physical objects in ubiquitous computing environments. In: IADIS International Conference E-Society 2004, pp. 614–621 (2004)Google Scholar
  22. 22.
    Valiev, A.: Automatic ownership change detection for IoT devices. G2 pro gradu, diplomityö 20 August 2018.
  23. 23.
    Whiting, D., Housley, R., Ferguson, N.: Counter with CBC-MAC (CCM). Technical report (2003)Google Scholar
  24. 24.
    Wullems, C., Looi, M., Clark, A.: Towards context-aware security: an authorization architecture for intranet environments. In: Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops 2004, pp. 132–137. IEEE (2004)Google Scholar
  25. 25.
    Zhang, G., Parashar, M.: Context-aware dynamic access control for pervasive applications. In: Proceedings of the Communication Networks and Distributed Systems Modeling and Simulation Conference, pp. 21–30 (2004)Google Scholar
  26. 26.
    Zhang, L., McDowell, W.C.: Am i really at risk? Determinants of online users’ intentions to use strong passwords. J. Internet Commer. 8(3–4), 180–197 (2009)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Md Sakib Nizam Khan
    • 1
    Email author
  • Samuel Marchal
    • 2
  • Sonja Buchegger
    • 1
  • N. Asokan
    • 2
  1. 1.KTH Royal Institute of TechnologyStockholmSweden
  2. 2.Aalto UniversityEspooFinland

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