A novel method of privacy-preserving based on compressive sensing for big data
Denglong Lyu, Shibing Zhu · 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · 2017
Privacy Aiming at the problems faced by big data privacy preserving, a new big data privacy preserving method based on compressive sensing is proposed. First, making big data anonymous, and compressing anonymous big data into small data with compressive sensing technology and reducing data processing overhead, while achieving two levels of data encryption. Then the data users can apply the key and decryption algorithm from the data owner to accurately reconstruct the original data and use data. The theoretical derivation and simulation experiments show that this method avoids the direct decryption and calculation of big data, reduces the computational overhead, and can reconstruct the original data from small data without affecting the normal use.