Trngforming

Trngforming is a theoretical data processing concept that describes a class of transforms in which a true random number generator (TRNG) is used as an integral input to the transformation process itself. Unlike standard deterministic transforms, trngforming introduces controlled randomness into the transform coefficients or the data domain, with the randomness governed by a specified seed or entropy source. The aim is to produce representations that are robust to certain perturbations, enable privacy-preserving operations, or facilitate testing and benchmarking of signal processing pipelines.

Principles and methods: The approach combines a transform stage, such as a linear or nonlinear basis change, with a stochastic layer that samples randomness from a TRNG to alter coefficients, quantize outcomes, or select transform paths. Variants include randomized linear transforms, probabilistic quantization, and seed-based mutation of transform trees. Repeatability is achieved by fixing seeds; reliability depends on TRNG quality and the statistical properties of the randomness.

Applications and limitations: Potential uses include privacy-preserving data sharing, watermarking, robust encoding, and stress testing of decoders. Limitations include potential loss of fidelity, increased decoding complexity, and dependency on hardware TRNG quality. Because randomness can alter output, precise reversibility is not guaranteed unless the process is carefully designed and the random components are fully accounted for during decoding.

See also: random number generator, transform coding, stochastic process, data obfuscation, cryptography.