DocumentCode :
3425508
Title :
Efficient Data Tagging for Managing Privacy in the Internet of Things
Author :
Evans, D. ; Eyers, D.M.
Author_Institution :
Sch. of Comput. & Math., Univ. of Derby Derby, Derby, UK
fYear :
2012
fDate :
20-23 Nov. 2012
Firstpage :
244
Lastpage :
248
Abstract :
The Internet of Things creates an environment where software systems are influenced and controlled by phenomena in the physical world. The goal is invisible and natural interactions with technology. However, if such systems are to provide a high-quality personalised service to individuals, they must by necessity gather information about those individuals. This leads to potential privacy invasion. Using techniques from Information Flow Control, data representing phenomena can be tagged with their privacy properties, allowing a trusted computing base to control access based on sensitivity and the system to reason about the flows of private data. For this to work well, tags must be assigned as soon as possible after phenomena are detected. Tagging within resource-constrained sensors raises worries that computing the tags may be too expensive and that useful tags are too large in relation to the data´s size and the data´s sensitivity. This paper assuages these worries, giving code templates for two small micro controllers (PIC and AVR) that effect meaningful tagging.
Keywords :
Internet of Things; authorisation; data handling; data privacy; trusted computing; AVR; Internet of Things; PIC; access control; data tagging; information flow control; microcontroller; privacy invasion; resource-constrained sensor; software system; trusted computing; Access control; Buildings; Data privacy; Privacy; Registers; Sensors; Tagging; embedded systems; information flow control; privacy; security; sensors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Green Computing and Communications (GreenCom), 2012 IEEE International Conference on
Conference_Location :
Besancon
Print_ISBN :
978-1-4673-5146-1
Type :
conf
DOI :
10.1109/GreenCom.2012.45
Filename :
6468320
Link To Document :
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