![]() The second pass produces the final HMAC code derived from the inner hash result and the outer key. Next, the first pass of the hash algorithm produces an internal hash derived from the message and the inner key. Before either pass, the secret key is used to derive two keys – inner and outer. HMAC uses two passes of hash computation. The cryptographic strength of the HMAC depends upon the cryptographic strength of the underlying hash function, the size of its hash output, and the size and quality of the key. It trades off the need for a complex public key infrastructure by delegating the key exchange to the communicating parties, who are responsible for establishing and using a trusted channel to agree on the key prior to communication.Īny cryptographic hash function, such as SHA-2 or SHA-3, may be used in the calculation of an HMAC the resulting MAC algorithm is termed HMAC-X, where X is the hash function used (e.g. HMAC can provide authentication using a shared secret instead of using digital signatures with asymmetric cryptography. As with any MAC, it may be used to simultaneously verify both the data integrity and authenticity of a message. Compared with the traditional Rabin hashing algorithm, it achieves a much faster speed.In cryptography, an HMAC (sometimes expanded as either keyed-hash message authentication code or hash-based message authentication code) is a specific type of message authentication code (MAC) involving a cryptographic hash function and a secret cryptographic key. Here FastCDC uses Gear hashing algorithm which can calculate the rolling hashing results quickly and keep the uniform distribution of the hashing results as Rabin. Where Gear array is a pre-calculated hashing array. While i < MinSize do fp ← ( fp << 1 ) + Gear] Skip the first MinSize bytes, and kickstart the hash MaxSize ← 64KB // split maximum chunk size is 64 KB If n ≤ MinSize then return n if n ≥ MaxSize then n ← MaxSize MinSize ← 2KB // split minimum chunk size is 2 KB ![]() ![]() buffer size is less than minimum chunk size The basic version pseudocode is provided as follows: It uses a fast rolling Gear hash algorithm, skipping the minimum length, normalizing the chunk-size distribution, and last but not the least, rolling two bytes each time to speed up the CDC algorithm, which can achieve about 10X higher throughput than Rabin-based CDC approach. FastCDC proposes a new and efficient Content-Defined Chunking approach. However, comparing a string byte-by-byte will introduce the heavy computation overhead. The Content-Defined Chunking algorithm needs to compute the hash value of a data stream byte by byte and split the data stream into chunks when the hash value meets a predefined value. Content-defined chunking (CDC) is a chunking technique in which the division of the data stream is not based on fixed chunk size, as in fixed-size chunking, but on its content. ![]() Gear fingerprint and content-based chunking algorithm FastCDC Ĭhunking is a technique to divide a data stream into a set of blocks, also called chunks. This approach will ensure that any change in the file will only affect its current and possibly the next chunk, but no other chunk. H = c 1 a k − 1 + c 2 a k − 2 + c 3 a k − 3 +. The Rabin–Karp string search algorithm is often explained using a rolling hash function that only uses multiplications and additions: They cannot be 3-wise independent, for example. FastCDC (Fast Content-Defined Chunking) uses a compute-efficient Gear fingerprint as its rolling hash.Īt best, rolling hash values are pairwise independent or strongly universal. Low Bandwidth Network Filesystem (LBFS) uses a Rabin fingerprint as its rolling hash. Another popular application is the rsync program, which uses a checksum based on Mark Adler's adler-32 as its rolling hash. One of the main applications is the Rabin–Karp string search algorithm, which uses the rolling hash described below. ( discuss) ( August 2020)Ī rolling hash (also known as recursive hashing or rolling checksum) is a hash function where the input is hashed in a window that moves through the input.Ī few hash functions allow a rolling hash to be computed very quickly-the new hash value is rapidly calculated given only the old hash value, the old value removed from the window, and the new value added to the window-similar to the way a moving average function can be computed much more quickly than other low-pass filters. It has been suggested that this article should be split into a new article titled Content-Defined Chunking.
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