Entropy extractor based high-throughput post-processings for True Random Number Generators

Abstract

In cryptographic systems, true random number generation is essential, as a compromised TRNG could lead to a security catastrophe. The raw random numbers are discrete values that are derived at discrete points in time from a noise source of a TRNG. These values often exhibit statistical defects that require post-processing, also called conditioner, to improve uniformity. The two main types of post-processing are algorithmic post-processing and cryptographic post-processing, both of which have pros and cons in theories and applications. However, another type of post-processing existing between these two types, named entropy extractor, has often been overlooked by the applied cryptographic community. Therefore, we implement two information-theoretically provable entropy extractors: Toeplitz extractor and Trevisan extractor catering to various performance requirements and applications of high-throughput TRNG post-processing. This paper proposes a combination of matrix chunking and FFT acceleration to boost the performance of the Toeplitz extractor, along with a modified Toeplitz matrix design to decrease the hardware consumption. In addition, we introduce a lightweight single-bit extractor to implement an efficient Trevisan extractor. Both algorithms are devised and verified through FPGA hardware simulations. The enhanced Toeplitz extractor achieves a throughput of 42 Gbps, while the Trevisan extractor attains 1.82 Gbps, representing an 84% and 73% improvement in throughput-to-area ratio over the previous best-performing design for each extractor. The standard statistical test suites, such as NIST SP800-22, NIST SP800-90B, and AIS-31, are adopted to evaluate the effectiveness of the proposed post-processing techniques. Naturally, this approach can only serve as a supplementary measure, as modern standards, such as AIS-31, necessitate formal analysis and stochastic models to account for randomness.