A differential privacy protection scheme for sensitive big data in body sensor networks

As a special kind of application of wireless sensor networks, body sensor networks (BSNs) have broad application perspectives in health caring. Big data acquired from BSNs usually contain sensitive information, such as physical condition, location information, and so on, which is compulsory to be appropriately protected. However, previous methods overlooked the privacy protection issue, leading to privacy violation. In this paper, a differential privacy protection scheme for sensitive big data in BSNs is proposed. A tree structure is constructed to reduce errors and provide long range queries. Haar Wavelet transformation method is applied to convert histogram into a complete binary tree. At last, to verify the advantages of our scheme, several experiments are conducted to show the outperformed results. Experimental results demonstrate that the tree structure greatly reduces the calculation overheads which preserves differential privacy for users.

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