Some implementations try to reduce the lower bits problem by shifting the bits right by a pre-defined amount, however this kind of solution also reduces the range of the output. The lower bits of these generators have much lower statistical randomness than the higher bits and the generated numbers can produce visible lattice and/or planar structures (the best example of that is the famous RANDU PRNG). Rand() is completely implementation defined, but historically it is implemented as a Linear Congruential Generator (LCG), which is usually a fast, but notoriously bad class of PRNGs. Actually, there are very good PRNGs, which are statistically hard or impossible to distinguish from true random numbers. Rand() is a pseudo-random number generator (PRNG), but this doesn't mean it must be bad. None of the answers here explains the real reason of being rand() bad. In modern C++ you should definitely use the C++ library from which comes with multiple random well-defined engines and various distributions for integer and floating point types. There are other answers here detailing this better than I could, so please read them. But the issue with this is that unless RAND_MAX is an exact multiple of 1018 you won't get an uniform distribution.Īnother issue is the Quality of Implementation of rand. A commonly (and naive) used formula is rand() % 1018. But most often you need a random number in a specific interval. It is uniform in this interval, which means that each number in this interval has the same probability to appear. The most visible problem of it is that it lacks a distribution engine: rand gives you a number in interval. It also greatly complicates multithreaded tasks. This makes it impossible to use multiple random engines at the same time. One issue is that it has a global state (set by srand). And here we arrive to the real troubles with the C random library (which includes rand and srand) that are specific to it and make it obsolete (a.k.a.: the reasons you should never use rand and the C random library). So you analyzed your problem and you conclude a pseudo-random generator is the solution. A true random generator has its own issues (efficiency, implementation, entropy) so for problems that are not security related most often a pseudo-random generator is used. And there are most certainly a lot of classes of problems where a pseudo-random generator is acceptable. It's an issue with any pseudo-random generator. This makes it not suitable for certain applications where security is of a great concern. For a given seed it will always give the same sequence (assuming the same implementation). A DRBG is often called a Pseudorandom Number (or Bit) Generator.First, rand is a pseudorandom number generator. The DRBG produces a sequence of bits from a secret initial value called a seed, along with other possible inputs. 1 under Deterministic Random Bit Generator An RBG that includes a DRBG mechanism and (at least initially) has access to a source of entropy input. A DRBG is often called a Pseudorandom Number (or Bit) Generator. 5 under Deterministic random bit generator (DRBG) An RBG that includes a DRBG mechanism and (at least initially) has access to a randomness source. A DRBG is sometimes also called a pseudo-random number generator (PRNG) or a deterministic random number generator. ![]() A cryptographic DRBG has the additional property that the output is unpredictable given that the seed is not known. ![]() The DRBG produces a sequence of bits from a secret initial value called a seed. 1 under Pseudorandom Number Generator A random bit generator that includes a DRBG algorithm and (at least initially) has access to a source of randomness. ![]() 5 under Pseudorandom number generator (PRNG) See Deterministic Random Bit Generator. 1a under Pseudorandom Number Generator (PRNG) See Deterministic random bit generator (DRBG). The input to the generator is called the seed, while the output is called a pseudorandom bit sequence. IETF RFC 4949 Ver 2 - Adapted A deterministic algorithm which, given a truly random binary sequence of length k, outputs a binary sequence of length l > k which appears to be random. A cryptographic PRNG has the additional property that the output is unpredictable, given that the seed is not known. A deterministic computational process that has one or more inputs called "seeds", and it outputs a sequence of values that appears to be random according to specified statistical tests.
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