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If the maximum number of leading zeros observed is n, an estimate for the number of distinct elements in the set is 2 n. [1] In the HyperLogLog algorithm, a hash function is applied to each element in the original multiset to obtain a multiset of uniformly distributed random numbers with the same cardinality as the original multiset. The ...
A common solution is to combine both the mean and the median: Create hash functions and split them into distinct groups (each of size ). Within each group use the mean for aggregating together the l {\displaystyle l} results, and finally take the median of the k {\displaystyle k} group estimates as the final estimate.
A universal hashing scheme is a randomized algorithm that selects a hash function h among a family of such functions, in such a way that the probability of a collision of any two distinct keys is 1/m, where m is the number of distinct hash values desired—independently of the two keys. Universal hashing ensures (in a probabilistic sense) that ...
To handle the bounded storage constraint, streaming algorithms use a randomization to produce a non-exact estimation of the distinct number of elements, . State-of-the-art estimators hash every element into a low-dimensional data sketch using a hash function, (). The different techniques can be classified according to the data sketches they store.
In mathematics and computing, universal hashing (in a randomized algorithm or data structure) refers to selecting a hash function at random from a family of hash functions with a certain mathematical property (see definition below). This guarantees a low number of collisions in expectation, even if the data is chosen by an adversary. Many ...
The original technique for constructing k-independent hash functions, given by Carter and Wegman, was to select a large prime number p, choose k random numbers modulo p, and use these numbers as the coefficients of a polynomial of degree k − 1 whose values modulo p are used as the value of the hash function.
An associative array stores a set of (key, value) pairs and allows insertion, deletion, and lookup (search), with the constraint of unique keys. In the hash table implementation of associative arrays, an array A {\displaystyle A} of length m {\displaystyle m} is partially filled with n {\displaystyle n} elements, where m ≥ n {\displaystyle m ...
Example of a Key Derivation Function chain as used in the Signal Protocol.The output of one KDF function is the input to the next KDF function in the chain. In cryptography, a key derivation function (KDF) is a cryptographic algorithm that derives one or more secret keys from a secret value such as a master key, a password, or a passphrase using a pseudorandom function (which typically uses a ...