Exploit the scale of big data for data privacy: An efficient scheme based on distance-preserving artificial noise and secret matrix transform

In this paper we show that the extensive results in blind/non-blind channel identification developed within the community of signal processing in communications can play an important role in guaranteeing big data privacy. It is widely believed that the sheer scale of big data makes most conventional data privacy techniques ineffective for big data. In contrast to this pessimistic common belief, we propose a scheme that exploits the sheer scale to guarantee privacy. This scheme uses jointly artificial noise and secret matrix transform to scramble the source data. Desirable data utility can be supported because the noise and the transform preserve some important geometric properties of the source data. With a comprehensive privacy analysis, we use the blind/non-blind channel identification theories to show that the secret transform matrix and the source data can not be estimated from the scrambled data. The artificial noise and the sheer scale of big data are critical for this purpose. Simulations of collaborative filtering are conducted to demonstrate the proposed scheme.