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Concept
Empreinte numérique
Résumé
In computer science, a fingerprinting algorithm is a procedure that maps an arbitrarily large data item (such as a computer ) to a much shorter bit string, its fingerprint, that uniquely identifies the original data for all practical purposes just as human fingerprints uniquely identify people for practical purposes. This fingerprint may be used for data deduplication purposes. This is also referred to as file fingerprinting, data fingerprinting, or structured data fingerprinting.
Fingerprints are typically used to avoid the comparison and transmission of bulky data. For instance, a web browser or proxy server can efficiently check whether a remote file has been modified, by fetching only its fingerprint and comparing it with that of the previously fetched copy.
Fingerprint functions may be seen as high-performance hash functions used to uniquely identify substantial blocks of data where cryptographic hash functions may be unnecessary.
Special algorithms exist for audio fingerprinting and video fingerprinting.
Collision resistance
To serve its intended purposes, a fingerprinting algorithm must be able to capture the identity of a file with virtual certainty. In other words, the probability of a collision — two files yielding the same fingerprint — must be negligible, compared to the probability of other unavoidable causes of fatal errors (such as the system being destroyed by war or by a meteorite): say, 10−20 or less.
This requirement is somewhat similar to that of a checksum function, but is much more stringent. To detect accidental data corruption or transmission errors, it is sufficient that the checksums of the original file and any corrupted version will differ with near certainty, given some statistical model for the errors. In typical situations, this goal is easily achieved with 16- or 32-bit checksums. In contrast, file fingerprints need to be at least 64-bit long to guarantee virtual uniqueness in large file systems (see birthday attack).
Source officielle
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