| Title | Revisiting Compressive Sensing based Encryption Schemes for IoT |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Kuldeep, G., Zhang, Q. |
| Conference Name | 2020 IEEE Wireless Communications and Networking Conference (WCNC) |
| Date Published | may |
| Keywords | composability, compressed sensing, compressive sampling, computational secrecy, Cyber-physical systems, Encryption, IoT, privacy, pubcrawl, Resiliency, Time Series Data |
| Abstract | Compressive sensing (CS) is regarded as one of the promising solutions for IoT data encryption as it achieves simultaneous sampling, compression, and encryption. Theoretical work in the literature has proved that CS provides computational secrecy. It also provides asymptotic perfect secrecy for Gaussian sensing matrix with constraints on input signal. In this paper, we design an attack decoding algorithm based on block compressed sensing decoding algorithm to perform ciphertext-only attack on real-life time series IoT data. It shows that it is possible to retrieve vital information in the plaintext under some conditions. Furthermore, it is also applied to a State-of-the Art CS-based encryption scheme for smart grid, and the power profile is reconstructed using ciphertext-only attack. Additionally, the statistical analysis of Gaussian and Binomial measurements is conducted to investigate the randomness provided by them. |
| DOI | 10.1109/WCNC45663.2020.9120785 |
| Citation Key | kuldeep_revisiting_2020 |
Revisiting Compressive Sensing based Encryption Schemes for IoT