True random numbers are essential for scientific research and various engineering problems. Their generation, however, depends on a reliable entropy source. Here, we present true random number generation using the conductance noise probed from structurally metastable 1T' MoTe2 prepared via electrochemical exfoliation. The noise, fitting a Poisson process, is a robust entropy source capable of remaining stable even at 15 K. Noise spectral density and statistical time-lag suggest the noise originates from the random polarization of the ferroelectric dipoles in 1T' MoTe2. Using a simple circuit, the noise allows true random number generation, enabling their use as the seed for high-throughput secure random number generation over 1 Mbit/s, appealing for applications such as cryptography where secure data protection has now become severe. Particularly, we demonstrate safeguarding key biometric information in neural networks using the random numbers, proving a critical data privacy measure in big data and artificial intelligence.

翻译：真随机数对于科学研究和各类工程问题至关重要。然而，其生成依赖于可靠的熵源。本文提出利用从电化学剥离制备的结构亚稳态1T' MoTe2中探测到的电导噪声来实现真随机数生成。该噪声符合泊松过程，是一种稳健的熵源，即使在15 K低温下仍能保持稳定。噪声谱密度和统计时滞分析表明，噪声源于1T' MoTe2中铁电偶极子的随机极化。通过简单电路，该噪声可实现真随机数生成，并能作为种子用于超过1 Mbit/s的高通量安全随机数生成，这对于密码学等安全数据保护需求日益严峻的应用具有吸引力。特别地，我们演示了利用这些随机数保护神经网络中的关键生物特征信息，为大数据和人工智能领域提供了一种关键的数据隐私保护措施。