MATE: target-bit-aware and compression-robust dual-view min-entropy estimation for random number generator evaluation
Year: 2026
Type: article
Abstract: Prediction-based min-entropy estimators often assume that one target-bit setting is suitable for sources with different marginal and temporal structures. We present MATE, a target-bit-aware dual-view framework that combines temporal features with synchronized Gramian Angular Field, Markov Transition Field, and Recurrence Plot representations. MATE profiles target-bit settings from 1 to 16 and evaluates whether the resulting operating points remain stable after compression. Experiments on 18 sources spanning distribution-based, generalized binary autoregressive (gbAR), and deterministic families show a clear source-dependent pattern: all four gbAR sources and all ten $$M^*$$ M ∗ -sequence sources achieve their lowest profiled errors at 1 bit, whereas the distribution-based sources prefer settings between 9 and 12 bits. The mean best-profiled error is 0.074395, compared with 0.647652 at the fixed 8-bit setting. Pruning, 2-bit quantization, and their combination preserve the empirically preferred target-bit setting for all 18 sources. In the shared-stream evaluation under the controlled fixed-8-bit protocol, Full MATE attains a macro mean absolute error of 1.904 bits per symbol, numerically close to 1.910 for NIST SP 800-90B and lower than 3.197 for TPA-LSTM, although none of the prespecified source-level comparisons is significant after Holm correction. Under strict budget matching, HP-UJ has a slightly lower numerical macro error than MA-Joint, but the paired difference is not significant after Holm adjustment. Deployment results show substantial storage reduction and lower incremental CPU memory, but no consistent CPU/GPU acceleration or GPU-memory reduction. Overall, the empirically preferred target-bit granularity varies across the evaluated sources, and the profiled operating points remain stable under compression, while budget-matched accuracy and runtime efficiency require separate evaluation.
Source: Cybersecurity
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7225230737