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- What are Hash Functions and How to choose a good Hash Function?
- Python hash() function
- How to Use Hashing Algorithms in Python using hashlib
- Generating Different Hash Functions
- Best non-cryptographic hashing function in Python (size and speed)

## What are Hash Functions and How to choose a good Hash Function?

This module implements a common interface to many different secure hash and message digest algorithms. Older algorithms were called message digests. The modern term is secure hash. If you want the adler32 or crc32 hash functions, they are available in the zlib module. There is one constructor method named for each type of hash. All return a hash object with the same simple interface. For example: use sha to create a SHA hash object. You can now feed this object with bytes-like objects normally bytes using the update method. At any point you can ask it for the digest of the concatenation of the data fed to it so far using the digest or hexdigest methods. For better multithreading performance, the Python GIL is released for data larger than bytes at object creation or on update. Feeding string objects into update is not supported, as hashes work on bytes, not on characters. Constructors for hash algorithms that are always present in this module are sha1shashashashablake2band blake2s. Additional algorithms may also be available depending upon the OpenSSL library that Python uses on your platform. New in version 3. For example, to obtain the digest of the byte string b'Nobody inspects the spammish repetition' :. Is a generic constructor that takes the string name of the desired algorithm as its first parameter. It also exists to allow access to the above listed hashes as well as any other algorithms that your OpenSSL library may offer. The named constructors are much faster than new and should be preferred. Using new with an algorithm provided by OpenSSL:. A set containing the names of the hash algorithms guaranteed to be supported by this module on all platforms. A set containing the names of the hash algorithms that are available in the running Python interpreter. These names will be recognized when passed to new. The same algorithm may appear multiple times in this set under different names thanks to OpenSSL.## Python hash() function

By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I need a reversible hash function obviously the input will be much smaller in size than the output that maps the input to the output in a random-looking way. The use case for this is that I need to find a way to transform small numbers into larger, random-looking ones. They don't actually need to be random in fact, they need to be deterministic, so the same input always maps to the same outputbut they do need to look random at least when base64encoded into strings, so shifting by Z bits won't work as similar numbers will have similar MSBs. I don't know if I'm being clear, or if such an algorithm exists, but I'd appreciate any and all help! None of the answers provided seemed particularly useful, given the question. I had the same problem, needing a simple, reversible hash for not-security purposes, and decided to go with bit relocation. It's simple, it's fast, and it doesn't require knowing anything about boolean maths or crypo algorithms or anything else that requires actual thinking. To get a less sequential looking implementation, you might also want to consider an interlace reordering from [msb,z, The above function is safe for numbers that fit in 32 bits, larger numbers are guaranteed to cause collisions and would need some more bit mask coverage to prevent problems. That said, 32 bits is usually enough for any non-security uid. Also have a look at the multiplicative inverse answer given by Andy Hayden, below. What you are asking for is encryption. A block cipher in its basic mode of operation, ECB, reversibly maps a input block onto an output block of the same size. The input and output blocks can be interpreted as numbers. For example, AES is a bit block cipher, so it maps an input bit number onto an output bit number. If bits is good enough for your purposes, then you can simply pad your input number out to bits, transform that single block with AES, then format the output as a bit number. ECB mode is considered weak, but its weakness is the constraint that you have postulated as a requirement namely, that the mapping be "deterministic". This is a weakness, because once an attacker has observed that maps tofrom then on whenever she sees the latter number, she knows the plaintext was Another simple solution is to use multiplicative inverses see Eri Clippert's blog :. We first calculate the multiplicative inverse with modinv which turns out to be Note: This answer is fast to compute and works for numbers up toif you need to handle larger numbers choose a sufficiently large number and another co-prime. If you choose a relatively large co-prime then this will seem random, be non-sequential and also be quick to calculate. Here is an example:" Simple insecure two-way "obfuscation" for C. Or, if this doesn't need to be terribly secure, you could convert from base 10 to some smaller base like base 8 or base 4, depending on how long you want the numbers to be. How are we doing?

## How to Use Hashing Algorithms in Python using hashlib

Representing genetic sequences using k-mers, or the biological equivalent of n-grams, is a great way to numerically summarize a linear sequence. To circumvent this technological constraint, Bloom filters were designed to probabilisticly track the presence not count of items. While coding up a script that approximated the number of unique k-mers in a region using a Bloom filter and an equation by Swamidass and Baldineeded the multiple hash functions that algorithms like. I came across several python modules that contained an array of efficient hash functions for indexing and cryptography. While each module contained several different hashing algorithms, you should not be using an entirely different algorithm just to yield a different value from the same key. Some implementations allow the user to initialize a function with a random seed with a random seed. A hash function should return different values for similar keys while minimizing the chances of value collisions overall. Returned values should also be approximately uniform in distribution. We can generate a histogram of the returned indicies and perform a chi-squared test to test for uniformity. You can see that the chi-squared test results in an insignificant P-value of 0. To be thorough, lets look at the resulting P-values for Normal and Uniform random variables. You can see that the Normal distribution has a significant 0. This test will be used to ensure hash functions are correct and look Uniform throughout this post. We can cycle through each number of fixed-bit width to demonstrate. This property is why applying an XOR operation to a random variable results in another random variable. Some hash functions incoroporate the random seed using XOR. Lets see how well this holds up in the real world. While not perfect, this is fairly good for a hash function. Please note that since this code uses random values, your results may differ. Generating Different Hash Functions Representing genetic sequences using k-mers, or the biological equivalent of n-grams, is a great way to numerically summarize a linear sequence. While coding up a script that approximated the number of unique k-mers in a region using a Bloom filter and an equation by Swamidass and Baldineeded the multiple hash functions that algorithms like Bloom filters MinHash depend on. I came across several python modules that contained an array of efficient hash functions for indexing and cryptography pyfasthash - Python Non-cryptographic Hash Library hashlib - Secure hashes and message digests built-in hash - Non-cryptographic hashing While each module contained several different hashing algorithms, you should not be using an entirely different algorithm just to yield a different value from the same key. Some implementations allow the user to initialize a function with a random seed with a random seed hashA value, seed1! Import necessary packages import matplotlib. XOR a, b 1. Generate histogram and chi-squared test plt.

## Generating Different Hash Functions

GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. It successfully completes the SMHasher test suite which evaluates collision, dispersion and randomness qualities of hash functions. The reference system uses a Core 2 Duo 3GHz. Score is a measure of quality of the hash function. It depends on successfully passing SMHasher test set. A more recent version, XXH64, has been created thanks to Mathias Westerdahlwhich offers superior speed and dispersion for bit systems. Note however that bit applications will still run faster using the bit version. The reference system uses a Core iM 2. This project also includes a command line utility, named xxhsumoffering similar features to md5sumthanks to Takayuki Matsuoka 's contributions. The library files xxhash. The utility xxhsum is GPL licensed. Starting with v0. The new algorithm is much faster than its predecessors for both long and small inputs, which can be observed in the following graphs:. The algorithm is currently in development, meaning its return values might still change in future versions. However, the API is stable, and can be used in production, typically for ephemeral data produced and consumed in same session. Since v0. XXH3 's return values will be officially finalized upon reaching v0. The following macros can be set at compilation time to modify libxxhash's behavior.

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