![]() ![]() ![]() This allows us to certify randomness in a large number of practical implementations utilizing both quantum and classical entropy sources. ![]() What sets our work apart from other SDI-RNG proposals is that our framework is formulated in a high-level abstract language of trusted randomness sources. In this paper we present an approach to semi-device-independent randomness certification that allows for flexible assumptions about the workings of an RNG. However, to make the RNGs experimentally more feasible, reasonable assumptions about the functioning of some components of the RNG are made, such as a trusted source 27, 28, 29, 30, 31, 32, 33, 34 or measurement device 37, 38, 39. Similar to DI-RNGs, SDI-RNGs include test rounds that are designed to certify the randomness of their output. In an attempt to retain the randomness certification capabilities of DI-RNGs with less stringent experimental requirements, many semi-device-independent random number generators (SDI-RNGs) have been proposed 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39. The disadvantage of DI-RNGs lies in their implementation-loophole-free Bell violations have been achieved only recently and under very strict laboratory conditions 24, 25, 26. Since Bell-type arguments do not assume anything about the devices used apart from space-like separation, this approach can truly be seen as device-independent. The violation of local-realism can be seen as a certificate that the devices use quantum measurements and their outcomes are fundamentally unpredictable. In these test rounds, the ability of the devices to violate Bell-type inequalities is verified 22, 23. ![]() In a small, randomly chosen fraction of the run-time, the device is tested. The security proof for these devices is usually based on Bell-type arguments: the RNG is composed of several non-communicating parts and runs a set of randomness-generation rounds, which involve a predetermined quantum measurement. 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, are called device independent (DI-RNGs), because they assume very little about the hardware they use. Aside from malicious attacks on the RNG, such as inserting back-doors 6 or displaying a simple bias towards certain strings 7, 8, its functioning can be compromised by a simple hardware malfunction, which is often hard to detect 9.Ĭonsiderations such as these have recently resulted in a different approach to RNG designs based on quantum phenomena, where stronger forms of randomness certificates are possible 10. This ignorance of the process used to generate the tested random string opens a window to various security risks. As an example, take the binary expansion of the number e-although the string created in this manner would pass many of the conventionally used statistical tests, it is obviously not suitable for cryptographic purposes. In essence, however, such an approach to analyzing RNGs is problematic, because the statistical tests do not assume anything about the origin of the data they test. The quality of RNGs is traditionally assessed with the help of statistical tests, or, more recently, machine learning 4, 5, which can verify that the produced string is virtually indistinguishable from a truly random string. In this regard, quantum mechanics offers the possibility of truly random events, such as nuclear decay or photons traveling through a semi-transparent mirror (see ref. These range from simple to generate but hard to predict computer data (such as the movement of a mouse cursor on a computer screen or the time between user keystrokes) to seemingly random physical phenomena (such as thermal noise or the breakdown in Zener diodes 1, 2). There are many different sources of entropy that can be utilized for RNG designs. Many of these applications critically depend on the quality of random numbers, and therefore the design of high-quality random number generators (RNGs) is of utmost importance. It has a great number of applications, ranging from randomized sampling, simulations, randomized algorithms, and above all, cryptography. Randomness is an important resource in modern information science. ![]()
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