Fog Computing Assisted Efficient Privacy Preserving Data Collection for Big Sensory Data

The property of performing data processing near the source of the data (i.e., at the edge of the network) makes the fog computing more suitable for networking environment of big data. For the sake of achieving efficient big sensory data collection with privacy preservation, this paper proposes a fog computing assisted efficient privacy preserving data collection scheme for big sensory data. In the proposed scheme, the designed layer-aware fog computing architecture provides effective support for exploring the spatio-temporal correlations and avoids long-distance communication with cloud center for utilizing the computation capabilities of local devices. Meanwhile, the proposed sampling perturbation encryption method protects the data privacy against eavesdropper and active attackers without sacrificing the data correlation, and it facilitates the simultaneous executing of decrypting and decompressing operations for encrypted sampling data. Furthermore, the developed data processing at fog node reduces the amount of redundant data transmission significantly, and the formulated optimization model for measurement matrix ensures the high precision of data reconstruction. Finally, the illustrative results reveal that the proposed scheme is an efficient data collection scheme with strong privacy preservation property.