WiFi fingerprint releasing for indoor localization based on differential privacy

WiFi fingerprint-based localization is regarded as one of the most promising techniques for indoor localization. However, this raises serious privacy concerns. Current approaches to mitigate the privacy concerns rely on the encryption with large calculation consumption. In this paper, we propose a data obfuscation mechanism based on the generalized version of differential privacy. We extend the standard definition to the indoor WiFi fingerprint data for spatial counting where the inputs belong to multiple dimensions of numerical data in a limited range. With a given privacy budget, the proposed method generalizes the original dataset, and then specializes it using differential privacy. As the designed novel scheme expand the range for specialization, the data set released by the proposed algorithm can yield better mining results. Furthermore, experimental results give out comparisons between nonuniform and uniform ε selection scheme, and find uniform ε selection scheme can fully use the privacy budget in our situation.