Decision making support for privacy data upload in smart home
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Abstract
A smart home equipped with various smart devices allows a service provider to automatically identify daily living activities from sensor/appliance data, but it is risky for dwellers to upload all the data generated in the home. In this paper, we define a threat model in which an attacker(s) can access all or part of the smart home data uploaded to the untrusted cloud server and can physically observe activities. Hence, the attacker can identify the association between the data and the home by matching the uploaded data and the observed data. The proposed method employs k-anonymity for dwellers to make a decision on whether the data should be uploaded or not. We computed values of k from the existing datasets and asked 18 participants to answer upload/no-upload for each pair of activities and time zone. As a result, our k-anonymity based method can reflect the dweller's sensitivity of privacy in uploading the data.
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