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Automated Analysis of Equivalence Properties for Security Protocols Using Else Branches Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Privacy Implications of Room Climate Data

Authors : Philipp Morgner, Christian Müller, Matthias Ring, Björn Eskofier, Christian Riess, Frederik Armknecht, Zinaida Benenson

Published in: Computer Security – ESORICS 2017

Publisher: Springer International Publishing

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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.

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Footnotes
2
Ethical review boards at both locations only consider medical experiments.
 
3
Note that some features are based on both, temperature and relative humidity, which is why the sum of both numbers exceeds 100%.
 
4
Methodological details, such as representativeness, breakdown by country and the exact formulation of the questions, are not known about this survey.
 
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Metadata
Title
Privacy Implications of Room Climate Data
Authors
Philipp Morgner
Christian Müller
Matthias Ring
Björn Eskofier
Christian Riess
Frederik Armknecht
Zinaida Benenson
Copyright Year
2017
Book
Computer Security – ESORICS 2017

Print ISBN: 978-3-319-66398-2

Electronic ISBN: 978-3-319-66399-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-66399-9_18

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