Analysis of differential distribution of lightweight block cipher based on parallel processing on GPU

As the fast development of IoT technology, various security solutions have to be considered when the corresponding solutions are being deployed. Due to the lightweight nature of the IoT devices such as the RFID tags and so on, traditional encryption schemes such as AES which are relatively heavy in the sense of operations cannot be applied here.

Lightweight block ciphers have since become a default standard when considering security protections on such lightweight IoT devices. Compared with the security analysis approaches by taking advantage of the differential or linear cryptanalysis, the security margin of lightweight block ciphers can be further derived more accurately due to the small internal state.

In this paper, we investigate the security margin of the lightweight block cipher structure especially the SPN design by taking advantage of the parallel computing power of modern GPU architecture. We show how to accelerate the computing of the statistical distinguisher, which is the crucial point for analyzing the security of the cipher design.

Our proposed methods gain notable advantage against traditional CPU architecture in terms of time complexity and possess extensibility for other block ciphers.