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San Francisco, CA
May 23, 2013 to May 24, 2013
ISBN: 978-1-4799-0458-7
pp: 23-27
ABSTRACT
A framework for maintaining security & preserving privacy for analysis of sensor data from smart homes, without compromising on data utility is presented. Storing the personally identifiable data as hashed values withholds identifiable information from any computing nodes. However the very nature of smart home data analytics is establishing preventive care. Data processing results should be identifiable to certain users responsible for direct care. Through a separate encrypted identifier dictionary with hashed and actual values of all unique sets of identifiers, we suggest re-identification of any data processing results. However the level of re-identification needs to be controlled, depending on the type of user accessing the results. Generalization and suppression on identifiers from the identifier dictionary before re-introduction could achieve different levels of privacy preservation. In this paper we propose an approach to achieve data security & privacy through out the complete data lifecycle: data generation/collection, transfer, storage, processing and sharing.
INDEX TERMS
privacy preserving; data security; smart homes; big data
CITATION
Antorweep Chakravorty, Tomasz Wlodarczyk, Chunming Rong, "Privacy Preserving Data Analytics for Smart Homes", SPW, 2013, 2013 IEEE CS Security and Privacy Workshops (SPW2013), 2013 IEEE CS Security and Privacy Workshops (SPW2013) 2013, pp. 23-27, doi:10.1109/SPW.2013.22
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