Privacy Implications of Room Climate Data

  • Philipp Morgner
  • Christian Müller
  • Matthias Ring
  • Björn Eskofier
  • Christian Riess
  • Frederik Armknecht
  • Zinaida Benenson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10493)

Abstract

Smart heating applications promise to increase energy efficiency and comfort by collecting and processing room climate data. While it has been suspected that the sensed data may leak crucial personal information about the occupants, this belief has up until now not been supported by evidence.

In this work, we investigate privacy risks arising from the collection of room climate measurements. We assume that an attacker has access to the most basic measurements only: temperature and relative humidity. We train machine learning classifiers to predict the presence and actions of room occupants. On data that was collected at three different locations, we show that occupancy can be detected with up to 93.5% accuracy. Moreover, the four actions reading, working on a PC, standing, and walking, can be discriminated with up to 56.8% accuracy, which is also far better than guessing (25%). Constraining the set of actions allows to achieve even higher prediction rates. For example, we discriminate standing and walking occupants with 95.1% accuracy. Our results provide evidence that even the leakage of such ‘inconspicuous’ data as temperature and relative humidity can seriously violate privacy.

Notes

Acknowledgement

The work is supported by the German Research Foundation (DFG) under Grant AR 671/3-1: WSNSec – Developing and Applying a Comprehensive Security Framework for Sensor Networks.

References

  1. 1.
    Ai, B., Fan, Z., Gao, R.X.: Occupancy estimation for smart buildings by an auto-regressive hidden Markov model. In: American Control Conference, ACC 2014, Portland, OR, USA, 4–6 June 2014, pp. 2234–2239. IEEE (2014)Google Scholar
  2. 2.
    BSI: Protection Profile for the Gateway of a Smart Metering System (Smart Meter Gateway PP). https://www.commoncriteriaportal.org/files/ppfiles/pp0073b_pdf.pdf. Accessed Mar 2014
  3. 3.
    Cavoukian, A., Polonetsky, J., Wolf, C.: SmartPrivacy for the smart grid: embedding privacy into the design of electricity conservation. Identity Inf. Soc. 3(2), 275–294 (2010)CrossRefGoogle Scholar
  4. 4.
    Chaos Computer Club: Guidelines for Smart Home Solutions, February 2016. (in German) https://www.ccc.de/en/updates/2016/smarthome
  5. 5.
    Deloitte: Ready for Takeoff? Consumer Survey, July 2015Google Scholar
  6. 6.
    Dong, B., Andrews, B., Lam, K.P., Höynck, M., Zhang, R., Chiou, Y.-S., Benitez, D.: An information technology enabled sustainability test-bed (ITEST) for occupancy detection through an environmental sensing network. Energy Build. 42(7), 1038–1046 (2010)CrossRefGoogle Scholar
  7. 7.
    Dunkels, A., B., Grönvall, B., Voigt, T.: Contiki - a lightweight and flexible operating system for tiny networked sensors. In: 29th Annual IEEE International Conference on Local Computer Networks, pp. 455–462. IEEE (2004)Google Scholar
  8. 8.
    Ebadat, A., Bottegal, G., Varagnolo, D., Wahlberg, B., Johansson, K.H.: Estimation of building occupancy levels through environmental signals deconvolution. In: BuildSys 2013, Proceedings of 5th ACM Workshop On Embedded Systems For Energy-Efficient Buildings, Roma, Italy, 13–14 November 2013, pp. 8:1–8:8 (2013)Google Scholar
  9. 9.
    Ebadat, A., Bottegal, G., Varagnolo, D., Wahlberg, B., Johansson, K.H.: Regularized deconvolution-based approaches for estimating room occupancies. IEEE Trans. Autom. Sci. Eng. 12(4), 1157–1168 (2015)CrossRefGoogle Scholar
  10. 10.
    Ecobee: Privacy policy & terms of use, April 2015Google Scholar
  11. 11.
    Ekwevugbe, T., Brown, N., Pakka, V., Fan, D.: Real-time building occupancy sensing using neural-network based sensor network. In: 7th IEEE International Conference on Digital Ecosystems and Technologies (DEST), pp. 114–119, July 2013Google Scholar
  12. 12.
    European Union Agency For Network And Information Security: Security and Resilience of Smart Home Environments - Good Practices and Recommendations. https://www.enisa.europa.eu. Accessed December 2015
  13. 13.
    Fischer-Hübner, S., Hopper, N. (eds.): Privacy Enhancing Technologies - PETS 2011. LNCS, vol. 6794. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-22263-4Google Scholar
  14. 14.
    Greveler, U., Glösekötterz, P., Justusy, B., Loehr, D.: Multimedia content identification through smart meter power usage profiles. In: Proceedings of International Conference on Information and Knowledge Engineering (IKE) (2012)Google Scholar
  15. 15.
    Hailemariam, E., Goldstein, R., Attar, R., Khan, A.: Real-time occupancy detection using decision trees with multiple sensor types. In: 2011 Spring Simulation Multi-conference, SpringSim 2011, Boston, MA, USA, 03–07 April 2011, pp. 141–148 (2011)Google Scholar
  16. 16.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  17. 17.
    Han, Z., Gao, R.X., Fan, Z.: Occupancy and indoor environment quality sensing for smart buildings. In: 2012 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 882–887, May 2012Google Scholar
  18. 18.
    Hart, G.W.: Residential energy monitoring and computerized surveillance via utility power flows. IEEE Technol. Soc. Mag. 8(2), 12–16 (1989)CrossRefGoogle Scholar
  19. 19.
    Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning, 2nd edn. Springer, New York (2009)CrossRefMATHGoogle Scholar
  20. 20.
    Honeywell: Honeywell connected home privacy statement, December 2015Google Scholar
  21. 21.
    Huppert, V., Paulus, J., Paulsen, U., Burkart, M., Wullich, B., Eskofier, B.: Quantification of nighttime micturition with an ambulatory sensor-based system. IEEE J. Biomed. Health Inform. 20(3), 865–872 (2016)CrossRefGoogle Scholar
  22. 22.
    icontrol Networks: 2015 State of the Smart Home Report. https://www.icontrol.com/blog/2015-state-of-the-smart-home-report
  23. 23.
    Intel Security: Intel Security’s International Internet of Things Smart Home Survey Shows Many Respondents Sharing Personal Data for Money. https://newsroom.intel.com/news-releases/intel-securitys-international-internet-of-things-smart-home-survey
  24. 24.
    Jawurek, M., Johns, M., Kerschbaum, F.: Plug-in privacy for smart metering billing. In: Fischer-Hübner and Hopper [13], pp. 192–210 (2011)Google Scholar
  25. 25.
    Jawurek, M., Kerschbaum, F., Danezis, G.: SoK: privacy technologies for smart grids - a survey of options. Microsoft Research, Cambridge, UK (2012)Google Scholar
  26. 26.
    Jensen, U., Blank, P., Kugler, P., Eskofier, B.: Unobtrusive and energy-efficient swimming exercise tracking using on-node processing. IEEE Sens. J. 16(10), 3972–3980 (2016)CrossRefGoogle Scholar
  27. 27.
    Kursawe, K., Danezis, G., Kohlweiss, M.: Privacy-friendly aggregation for the smart-grid. In: Fischer-Hübner and Hopper [13], pp. 175–191 (2011)Google Scholar
  28. 28.
    Lam, K.P., Höynck, M., Dong, B., Andrews, B., Chiou, Y.S., Benitez, D., Choi, J.: Occupancy detection through an extensive environmental sensor network in an open-plan office building. In: Proceedings of Building Simulation 2009, an IBPSA Conference (2009)Google Scholar
  29. 29.
    Lu, J., Sookoor, T., Srinivasan, V., Gao, G., Holben, B., Stankovic, J., Field, E., Whitehouse, K.: The smart thermostat: using occupancy sensors to save energy in homes. In: Proceedings of 8th ACM Conference on Embedded Networked Sensor Systems, pp. 211–224. ACM (2010)Google Scholar
  30. 30.
    Masood, M.K., Soh, Y.C., Chang, V.W., Real-time occupancy estimation using environmental parameters. In: 2015 International Joint Conference on Neural Networks, IJCNN 2015, Killarney, Ireland, 12–17 July 2015, pp. 1–8. IEEE (2015)Google Scholar
  31. 31.
    Molina-Markham, A., Shenoy, P., Fu, K., Cecchet, E., Irwin, D.: Private memoirs of a smart meter. In: Proceedings of 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, BuildSys 2010, New York, NY, USA, pp. 61–66. ACM (2010)Google Scholar
  32. 32.
    Moteiv Corporation: Tmote Sky Datasheet (2006)Google Scholar
  33. 33.
    Nest: Privacy statement for nest products and services, March 2016Google Scholar
  34. 34.
    R Core Team: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2014)Google Scholar
  35. 35.
    Rainie, L., Duggan, M.: Pew Research: Privacy and Information Sharing. http://www.pewinternet.org/2016/01/14/privacy-and-information-sharing. Accessed Jan 2016
  36. 36.
    Reinhardt, A., Englert, F., Christin, D.: Averting the privacy risks of smart metering by local data preprocessing. Pervas. Mob. Comput. 16, 171–183 (2015)CrossRefGoogle Scholar
  37. 37.
    Rial, A., Danezis, G.: Privacy-preserving smart metering. In: Proceedings of 10th Annual ACM Workshop on Privacy in the Electronic Society, WPES 2011, pp. 49–60. ACM, New York (2011)Google Scholar
  38. 38.
    Ring, M., Jensen, U., Kugler, P., Eskofier, B.: Software-based performance and complexity analysis for the design of embedded classification systems. In: Proceedings of 21st International Conference on Pattern Recognition, ICPR 2012, Tsukuba, Japan, 11–15 November 2012, pp. 2266–2269. IEEE Computer Society (2012)Google Scholar
  39. 39.
    Selinger, M.: Test: Smart Home Kits Leave the Door Wide Open - for Everyone. https://www.av-test.org/en/news/news-single-view/test-smart-home-kits-leave-the-door-wide-open-for-everyone/. Accessed Apr 2014
  40. 40.
    Spiekermann, S., Acquisti, A., Böhme, R., Hui, K.-L.: The challenges of personal data markets and privacy. Electron. Mark. 25(2), 161–167 (2015)CrossRefGoogle Scholar
  41. 41.
    van Kasteren, T., Noulas, A., Englebienne, G., Kröse, B.: Accurate activity recognition in a home setting. In: Proceedings of 10th International Conference on Ubiquitous Computing. ACM (2008)Google Scholar
  42. 42.
    Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, Burlington (2011)Google Scholar
  43. 43.
    Wörner, D., von Bomhard, T., Roeschlin, M., Wortmann, F.: Look twice: uncover hidden information in room climate sensor data. In: 4th International Conference on the Internet of Things, IoT 2014, Cambridge, MA, USA, 6–8 October 2014, pp. 25–30. IEEE (2014)Google Scholar
  44. 44.
    Yang, W., Li, N., Qi, Y., Qardaji, W., McLaughlin, S., McDaniel, P.: Minimizing private data disclosures in the smart grid. In: Proceedings of 2012 ACM Conference on Computer and Communications Security, pp. 415–427. ACM (2012)Google Scholar
  45. 45.
    Yang, Z., Li, N., Becerik-Gerber, B., Orosz, M.D.: A systematic approach to occupancy modeling in ambient sensor-rich buildings. Simulation 90(8), 960–977 (2014)CrossRefGoogle Scholar
  46. 46.
    Zhang, R., Lam, K.P., Chiou, Y.-S., Dong, B.: Information-theoretic environment features selection for occupancy detection in open office spaces. Build. Simul. 5(2), 179–188 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Philipp Morgner
    • 1
  • Christian Müller
    • 2
  • Matthias Ring
    • 1
  • Björn Eskofier
    • 1
  • Christian Riess
    • 1
  • Frederik Armknecht
    • 2
  • Zinaida Benenson
    • 1
  1. 1.Friedrich-Alexander-Universität Erlangen-NürnbergErlangenGermany
  2. 2.University of MannheimMannheimGermany

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