{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T11:01:13Z","timestamp":1777806073010,"version":"3.51.4"},"reference-count":55,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2018,9,26]],"date-time":"2018-09-26T00:00:00Z","timestamp":1537920000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computer Security"],"published-print":{"date-parts":[[2019,1,11]]},"abstract":"<jats:p>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.<\/jats:p>\n                  <jats:p>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 number of room occupants and to discriminate between different types of activities. On data that was collected at three different locations, we show that occupancy can be detected from data measured by a single sensor with up to [Formula: see text] accuracy. One can even distinguish between the cases that no, one, or two persons are present with up to [Formula: see text] accuracy. Moreover, the four actions reading, working on a PC, standing, and walking, can be discriminated with up to [Formula: see text] accuracy, which is likewise clearly better than guessing ([Formula: see text]). Constraining the set of actions allows to achieve even higher prediction rates. For example, we discriminate standing and walking occupants with [Formula: see text] accuracy. In addition, we show that the accuracy can be increased in most cases if an attacker has access to measurements from two different sensors located in the same room.<\/jats:p>\n                  <jats:p>Our results provide evidence that even the leakage of such \u2018inconspicuous\u2019 data as temperature and relative humidity can seriously violate privacy.<\/jats:p>","DOI":"10.3233\/jcs-181133","type":"journal-article","created":{"date-parts":[[2018,9,29]],"date-time":"2018-09-29T07:24:21Z","timestamp":1538205861000},"page":"113-136","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Privacy implications of room climate data"],"prefix":"10.1177","volume":"27","author":[{"given":"Frederik","family":"Armknecht","sequence":"first","affiliation":[{"name":"University of Mannheim, Germany. E-mails:\u00a0,\u00a0"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zinaida","family":"Benenson","sequence":"additional","affiliation":[{"name":"Friedrich-Alexander-University Erlangen-N\u00fcrnberg (FAU), Germany. E-mails:\u00a0,\u00a0,\u00a0"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philipp","family":"Morgner","sequence":"additional","affiliation":[{"name":"Friedrich-Alexander-University Erlangen-N\u00fcrnberg (FAU), Germany. E-mails:\u00a0,\u00a0,\u00a0"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"University of Mannheim, Germany. E-mails:\u00a0,\u00a0"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Riess","sequence":"additional","affiliation":[{"name":"Friedrich-Alexander-University Erlangen-N\u00fcrnberg (FAU), Germany. E-mails:\u00a0,\u00a0,\u00a0"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2018,9,26]]},"reference":[{"key":"ref001","doi-asserted-by":"crossref","unstructured":"B.\u00a0Ai, Z.\u00a0Fan and R.X.\u00a0Gao, Occupancy estimation for smart buildings by an auto-regressive hidden Markov model, in: American Control Conference, ACC 2014, Portland, OR, USA, June 4\u20136, 2014, IEEE, 2014, pp.\u00a02234\u20132239. ISBN 978-1-4799-3272-6. doi:10.1109\/ACC.2014.6859372.","DOI":"10.1109\/ACC.2014.6859372"},{"key":"ref002","unstructured":"BSI, Protection Profile for the Gateway of a Smart Metering System (Smart Meter Gateway PP), 2014, https:\/\/www.commoncriteriaportal.org\/files\/ppfiles\/pp0073b_pdf.pdf."},{"key":"ref003","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2015.11.071"},{"key":"ref004","doi-asserted-by":"publisher","DOI":"10.1007\/s12394-010-0046-y"},{"key":"ref005","unstructured":"Chaos Computer Club: Guidelines for Smart Home solutions, 2016, (in German)."},{"key":"ref006","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-016-0549-7"},{"key":"ref007","unstructured":"Moteiv Corporation, Tmote Sky Datasheet, Moteiv Corporation, 2006."},{"key":"ref008","unstructured":"Deloitte, Ready for Takeoff? 2015, Consumer Survey, (in German), https:\/\/www2.deloitte.com\/de\/de\/pages\/technology-media-and-telecommunications\/articles\/smart-home-consumer-survey.html."},{"key":"ref009","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2010.01.016"},{"key":"ref010","doi-asserted-by":"crossref","unstructured":"A.\u00a0Dunkels, B.\u00a0Gr\u00f6nvall and T.\u00a0Voigt, Contiki \u2013 a lightweight and flexible operating system for tiny networked sensors, in: 29th Annual IEEE International Conference on Local Computer Networks, 2004, IEEE, 2004, pp.\u00a0455\u2013462. doi:10.1109\/LCN.2004.38.","DOI":"10.1109\/LCN.2004.38"},{"key":"ref011","doi-asserted-by":"crossref","unstructured":"A.\u00a0Ebadat, G.\u00a0Bottegal, D.\u00a0Varagnolo, B.\u00a0Wahlberg and K.H.\u00a0Johansson, Estimation of building occupancy levels through environmental signals deconvolution, in: BuildSys 2013, Proceedings of the 5th ACM Workshop on Embedded Systems for Energy-Efficient Buildings, Roma, Italy, November 13\u201314, 2013, 2013, pp.\u00a08\u2013188. doi:10.1145\/2528282.2528290.","DOI":"10.1145\/2528282.2528290"},{"key":"ref012","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2015.2471305"},{"key":"ref013","unstructured":"Ecobee, Privacy Policy & Terms of Use, 2015."},{"key":"ref014","doi-asserted-by":"crossref","unstructured":"T.\u00a0Ekwevugbe, N.\u00a0Brown, V.\u00a0Pakka and D.\u00a0Fan, Real-time building occupancy sensing using neural-network based sensor network, in: 7th IEEE International Conference on Digital Ecosystems and Technologies (DEST) 2013, 2013, pp.\u00a0114\u2013119. ISSN 2150-4938. doi:10.1109\/DEST.2013.6611339.","DOI":"10.1109\/DEST.2013.6611339"},{"key":"ref015","unstructured":"European Union Agency For Network And Information Security, Security and Resilience of Smart Home Environments\u00a0\u2013 Good practices and recommendations, 2015."},{"key":"ref016","doi-asserted-by":"crossref","unstructured":"X.\u00a0Fan, Q.\u00a0Xie, X.\u00a0Li, H.\u00a0Huang, J.\u00a0Wang, S.\u00a0Chen, C.\u00a0Xie and J.\u00a0Chen, Activity recognition as a service for smart home: Ambient assisted living application via sensing home, in: 2017 IEEE International Conference on AI Mobile Services (AIMS), 2017, pp.\u00a054\u201361. doi:10.1109\/AIMS.2017.29.","DOI":"10.1109\/AIMS.2017.29"},{"key":"ref017","doi-asserted-by":"crossref","unstructured":"S.\u00a0Ghaffarzadegan, A.\u00a0Reiss, M.\u00a0Ruhs, R.\u00a0Duerichen and Z.\u00a0Feng, Occupancy detection in commercial and residential environments using audio signal, in: Proc. Interspeech 2017, 2017, pp.\u00a03802\u20133806. doi:10.21437\/Interspeech.2017-524.","DOI":"10.21437\/Interspeech.2017-524"},{"key":"ref018","unstructured":"U.\u00a0Greveler, P.\u00a0Gl\u00f6sek\u00f6tterz, B.\u00a0Justusy and D.\u00a0Loehr, Multimedia content identification through smart meter power usage profiles, in: Proceedings of the International Conference on Information and Knowledge Engineering (IKE), 2012."},{"key":"ref019","unstructured":"E.\u00a0Hailemariam, R.\u00a0Goldstein, R.\u00a0Attar and A.\u00a0Khan, Real-time occupancy detection using decision trees with multiple sensor types, in: 2011 Spring Simulation Multi-Conference, SpringSim \u201911, Boston, MA, USA, April 03-07, 2011, 2011, pp.\u00a0141\u2013148. http:\/\/dl.acm.org\/citation.cfm?id=2048555."},{"key":"ref020","doi-asserted-by":"publisher","DOI":"10.1145\/1656274.1656278"},{"key":"ref021","doi-asserted-by":"crossref","unstructured":"Z.\u00a0Han, R.X.\u00a0Gao and Z.\u00a0Fan, Occupancy and indoor environment quality sensing for smart buildings, in: 2012 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2012, pp.\u00a0882\u2013887. ISSN 1091-5281. doi:10.1109\/I2MTC.2012.6229557.","DOI":"10.1109\/I2MTC.2012.6229557"},{"key":"ref022","doi-asserted-by":"publisher","DOI":"10.1109\/44.31557"},{"key":"ref023","doi-asserted-by":"crossref","unstructured":"T.\u00a0Hastie, R.\u00a0Tibshirani and J.H.\u00a0Friedman, The Elements of Statistical Learning, 2nd edn, Springer, New York, NY, USA, 2009.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref024","unstructured":"Honeywell, Honeywell connected home privacy statement, 2015."},{"key":"ref025","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2015.2421487"},{"key":"ref026","unstructured":"Intel Security: Intel Security\u2019s International Internet of Things Smart Home Survey Shows Many Respondents Sharing Personal Data for Money, 2016, https:\/\/newsroom.intel.com\/news-releases\/intel-securitys-international-internet-of-things-smart-home-survey."},{"key":"ref027","doi-asserted-by":"crossref","unstructured":"M.\u00a0Jawurek, M.\u00a0Johns and F.\u00a0Kerschbaum, Plug-in privacy for smart metering billing, in: Privacy Enhancing Technologies \u2013 11th International Symposium, PETS 2011, Proceedings, Waterloo, ON, Canada, July 27\u201329, 2011, S.\u00a0Fischer-H\u00fcbner and N.\u00a0Hopper, eds, Lecture Notes in Computer Science, Vol.\u00a06794, Springer, 2011, pp.\u00a0192\u2013210. ISBN 978-3-642-22262-7. doi:10.1007\/978-3-642-22263-4_11.","DOI":"10.1007\/978-3-642-22263-4_11"},{"key":"ref028","unstructured":"M.\u00a0Jawurek, F.\u00a0Kerschbaum and G.\u00a0Danezis, in: SoK: Privacy Technologies for Smart Grids \u2013 a Survey of Options., Microsoft Res, Cambridge, UK, 2012."},{"key":"ref029","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2016.2530019"},{"key":"ref030","doi-asserted-by":"crossref","unstructured":"K.\u00a0Kursawe, G.\u00a0Danezis and M.\u00a0Kohlweiss, Privacy-friendly aggregation for the smart-grid, in: Privacy Enhancing Technologies \u2013 11th International Symposium, PETS 2011, Proceedings, Waterloo, ON, Canada, July 27\u201329, 2011, S.\u00a0Fischer-H\u00fcbner and N.\u00a0Hopper, eds, Lecture Notes in Computer Science, Vol.\u00a06794, Springer, 2011, pp.\u00a0175\u2013191. ISBN 978-3-642-22262-7. doi:10.1007\/978-3-642-22263-4_10.","DOI":"10.1007\/978-3-642-22263-4_10"},{"key":"ref031","unstructured":"K.P.\u00a0Lam, M.\u00a0H\u00f6ynck, B.\u00a0Dong, B.\u00a0Andrews, Y.S.\u00a0Chiou, D.\u00a0Benitez and J.\u00a0Choi, Occupancy detection through an extensive environmental sensor network in an open-plan office building, in: Proc. of Building Simulation 09, an IBPSA Conference, 2009."},{"key":"ref032","doi-asserted-by":"crossref","unstructured":"J.\u00a0Lu, T.\u00a0Sookoor, V.\u00a0Srinivasan, G.\u00a0Gao, B.\u00a0Holben, J.\u00a0Stankovic, E.\u00a0Field and K.\u00a0Whitehouse, The smart thermostat: Using occupancy sensors to save energy in homes, in: Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, ACM, 2010, pp.\u00a0211\u2013224.","DOI":"10.1145\/1869983.1870005"},{"key":"ref033","doi-asserted-by":"crossref","unstructured":"M.K.\u00a0Masood, Y.C.\u00a0Soh and V.W.\u00a0Chang, Real-time occupancy estimation using environmental parameters, in: 2015 International Joint Conference on Neural Networks, IJCNN 2015, Killarney, Ireland, July 12\u201317, 2015, IEEE, 2015, pp.\u00a01\u20138. ISBN 978-1-4799-1960-4. doi:10.1109\/IJCNN.2015.7280781.","DOI":"10.1109\/IJCNN.2015.7280781"},{"key":"ref034","doi-asserted-by":"crossref","unstructured":"H.D.\u00a0Mehr, H.\u00a0Polat and A.\u00a0Cetin, Resident activity recognition in smart homes by using artificial neural networks, in: 2016 4th International Istanbul Smart Grid Congress and Fair (ICSG), 2016, pp.\u00a01\u20135. doi:10.1109\/SGCF.2016.7492428.","DOI":"10.1109\/SGCF.2016.7492428"},{"key":"ref035","doi-asserted-by":"crossref","unstructured":"A.\u00a0Molina-Markham, P.\u00a0Shenoy, K.\u00a0Fu, E.\u00a0Cecchet and D.\u00a0Irwin, Private memoirs of a smart meter, in: Proceedings of the 2Nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, BuildSys \u201910, ACM, New York, NY, USA, 2010, pp.\u00a061\u201366. ISBN 978-1-4503-0458-0. doi:10.1145\/1878431.1878446.","DOI":"10.1145\/1878431.1878446"},{"key":"ref036","doi-asserted-by":"crossref","unstructured":"P.\u00a0Morgner, C.\u00a0M\u00fcller, M.\u00a0Ring, B.\u00a0Eskofier, C.\u00a0Riess, F.\u00a0Armknecht and Z.\u00a0Benenson, Privacy implications of room climate data, in: Computer Security \u2013 ESORICS 2017 \u2013 22nd European Symposium on Research in Computer Security, Proceedings, Part II, Oslo, Norway, September 11\u201315, 2017, 2017, pp.\u00a0324\u2013343.","DOI":"10.1007\/978-3-319-66399-9_18"},{"key":"ref037","unstructured":"Nest, Privacy Statement for Nest Products and Services, 2016."},{"key":"ref038","unstructured":"icontrol Networks: 2015 State of the Smart Home Report."},{"key":"ref039","doi-asserted-by":"publisher","DOI":"10.1016\/j.buildenv.2017.01.023"},{"key":"ref040","unstructured":"R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2014. http:\/\/www.R-project.org."},{"key":"ref041","unstructured":"L.\u00a0Rainie and M.\u00a0Duggan, Pew Research: Privacy and Information Sharing, 2016, http:\/\/www.pewinternet.org\/2016\/01\/14\/privacy-and-information-sharing."},{"key":"ref042","unstructured":"Regulation (EU) 2016\/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95\/46\/EC (General Data Protection Regulation),\n                      Official Journal of the European Union L\n                      119\n                      (2016), 1\u201388, http:\/\/eur-lex.europa.eu\/legal-content\/EN\/TXT\/?uri=OJ:L:2016:119:TOC."},{"key":"ref043","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2014.10.002"},{"key":"ref044","doi-asserted-by":"crossref","unstructured":"A.\u00a0Rial and G.\u00a0Danezis, Privacy-preserving smart metering, in: Proceedings of the 10th Annual ACM Workshop on Privacy in the Electronic Society, WPES \u201911, ACM, New York, NY, USA, 2011, pp.\u00a049\u201360. ISBN 978-1-4503-1002-4. doi:10.1145\/2046556.2046564.","DOI":"10.1145\/2046556.2046564"},{"key":"ref045","unstructured":"M.\u00a0Ring, U.\u00a0Jensen, P.\u00a0Kugler and B.\u00a0Eskofier, Software-based performance and complexity analysis for the design of embedded classification systems, in: Proceedings of the 21st International Conference on Pattern Recognition, ICPR, Tsukuba, Japan, November 11\u201315, 2012, IEEE Computer Society, 2012, pp.\u00a02266\u20132269. http:\/\/ieeexplore.ieee.org\/xpl\/freeabs_all.jsp?arnumber=6460616. ISBN 978-1-4673-2216-4."},{"key":"ref046","unstructured":"M.\u00a0Selinger, Test: Smart Home Kits Leave the Door Wide Open \u2013 for Everyone, 2014, https:\/\/www.av-test.org\/en\/news\/news-single-view\/test-smart-home-kits-leave-the-door-wide-open-for-everyone\/."},{"key":"ref047","doi-asserted-by":"publisher","DOI":"10.1007\/s12525-015-0191-0"},{"key":"ref048","doi-asserted-by":"crossref","unstructured":"G.\u00a0Sprint, D.\u00a0Cook, R.\u00a0Fritz and M.\u00a0Schmitter-Edgecombe, Detecting health and behavior change by analyzing smart home sensor data, in: 2016 IEEE International Conference on Smart Computing (SMARTCOMP), 2016, pp.\u00a01\u20133. doi:10.1109\/SMARTCOMP.2016.7501687.","DOI":"10.1109\/SMARTCOMP.2016.7501687"},{"key":"ref049","doi-asserted-by":"crossref","unstructured":"T.\u00a0van Kasteren, A.\u00a0Noulas, G.\u00a0Englebienne and B.\u00a0Kr\u00f6se, Accurate activity recognition in a home setting, in: Proceedings of the 10th International Conference on Ubiquitous Computing, ACM, 2008.","DOI":"10.1145\/1409635.1409637"},{"key":"ref050","doi-asserted-by":"crossref","unstructured":"I.H.\u00a0Witten, E.\u00a0Frank and M.A.\u00a0Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn, Morgan Kaufmann, Burlington, MA, USA, 2011.","DOI":"10.1016\/B978-0-12-374856-0.00001-8"},{"key":"ref051","doi-asserted-by":"crossref","unstructured":"D.\u00a0W\u00f6rner, T.\u00a0von Bomhard, M.\u00a0Roeschlin and F.\u00a0Wortmann, Look twice: Uncover hidden information in room climate sensor data, in: 4th International Conference on the Internet of Things, IoT 2014, Cambridge, MA, USA, October 6\u20138, 2014, IEEE, 2014, pp.\u00a025\u201330. ISBN 978-1-4799-5154-3. doi:10.1109\/IOT.2014.7030110.","DOI":"10.1109\/IOT.2014.7030110"},{"key":"ref052","doi-asserted-by":"crossref","unstructured":"W.\u00a0Yang, N.\u00a0Li, Y.\u00a0Qi, W.\u00a0Qardaji, S.\u00a0McLaughlin and P.\u00a0McDaniel, Minimizing private data disclosures in the smart grid, in: Proceedings of the 2012 ACM Conference on Computer and Communications Security, ACM, 2012, pp.\u00a0415\u2013427.","DOI":"10.1145\/2382196.2382242"},{"key":"ref053","doi-asserted-by":"publisher","DOI":"10.1177\/0037549713489918"},{"key":"ref054","doi-asserted-by":"publisher","DOI":"10.1007\/s12273-012-0075-6"},{"key":"ref055","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2752134"}],"container-title":["Journal of Computer Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JCS-181133","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JCS-181133","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JCS-181133","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T20:45:16Z","timestamp":1777495516000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JCS-181133"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,26]]},"references-count":55,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,1,11]]}},"alternative-id":["10.3233\/JCS-181133"],"URL":"https:\/\/doi.org\/10.3233\/jcs-181133","relation":{},"ISSN":["0926-227X","1875-8924"],"issn-type":[{"value":"0926-227X","type":"print"},{"value":"1875-8924","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9,26]]}}}