Is r.uuid() guaranteed to be unique?
Return a UUID (universally unique identifier), a string that can be used as a unique ID.
How universal is r.uuid()? Is it scoped to a table/database/instance of RethinkDB? Or is it simply computing the hash of a random byte sequence (e.g. /dev/rand)? Or does it hash nano-unix time?
You can check the answers to a related question in here.
UUIDs are supposed to be uniques because of the very low probability of colisitions. Although in theory they may not be uniques as it's a random algorithm that generates the UUIDs, you will hardly generate a duplicate.
From the Wikipedia they say that for 68,719,476,736 generated UUIDs (Which it's a very huge number for a common application) you have 0.0000000000000004 for an accidental clash. It's almost impossible..
UUID means universally unique identifier. In this context the word unique should be taken to mean "practically unique" rather than "guaranteed unique". Since the identifiers have a finite size, it is possible for two differing items to share the same identifier. This is a form of hash collision.
Anyone can create a UUID and use it to identify something with reasonable confidence that the same identifier will never be unintentionally created by anyone to identify something else.
A UUID is simply a 128-bit value.
Related
I want to generate 32 characters long unique unguessable alphanumeric secret keys. The secret key will be an identifier for my system and will be used to look up information.
While searching the web I stumbled upon the crypto/rand package of Go. Which is able to generate random alphanumerics with the help of underline system calls. But I am concerned that the value returned by the crypto/rand package might produce a non-unique string down the line.
Can anyone clarify if I can rely on the crypto/rand package for the job?
Of course with randomly generated tokens, there is always the possibility of generating a duplicate token. There are standards such as UUID (excluding v4) that use other methods to try to "guarantee" uniqueness of each identifier. These methods do not truly obviate the possibility of collisions, they just shift the failure modes. For example, UUID1 relies on uniqueness of MAC addresses, which is a whole issue of its own.
If you are not limited by the size of your tokens, you can easily pick a sufficiently large number of bits that the probability of collisions becomes so small that it is completely dwarfed by countless other failure modes (such as programmer error, cosmic rays, a mass global extinction event, etc.).
Very approximately, if you have a true random key length of N bits, you can generate 2^(N/2) keys before having a 50% chance of seeing collisions. See the Wikipedia page for UUID#Collisions for a more general formula.
With crypto/rand there is no guarantee that individual random numbers will occur more than once. The probability of this to happen is very low, however, and it may be good enough for your use case. In many cases UUID will be good enough. If you are curious about the probability of duplicate UUIDs, see Wikipedia for example.
If you really need true uniqueness you may want to combine random numbers with a map to record them, where the number serves as key and the value is a "don't care". While recording the numbers, duplicates can be detected and a new random can be requested in case. However, this approach may introduce a new challenge depending on your setting as the numbers are now kept in memory which is insecure per se. It will also be challenging in terms of complexity if your use case does not determine the quantity of secrets required during the lifetime of the system.
For me, it really boils down to the question whether the identifiers for your system you use for info lookups are really secrets or you just want unique identifiers which are hard to predict before they occur in the system. Maybe you can elaborate on your use case to clarify your requirements.
I think, for this type of thing, you should use UUID
package main
import (
"fmt"
"github.com/google/uuid"
)
func main() {
id := uuid.New()
fmt.Println(id.String())
}
A few months back I was tasked with implementing a unique and random code for our web application. The code would have to be user friendly and as small as possible, but still be essentially random (so users couldn't easily predict the next code in the sequence).
It ended up generating values that looked something like this:
Af3nT5Xf2
Unfortunately, I was never satisfied with the implementation. Guid's were out of the question, they were simply too big and difficult for users to type in. I was hoping for something more along the lines of 4 or 5 characters/digits, but our particular implementation would generate noticeably patterned sequences if we encoded to less than 9 characters.
Here's what we ended up doing:
We pulled a unique sequential 32bit id from the database. We then inserted it into the center bits of a 64bit RANDOM integer. We created a lookup table of easily typed and recognized characters (A-Z, a-z, 2-9 skipping easily confused characters such as L,l,1,O,0, etc.). Finally, we used that lookup table to base-54 encode the 64-bit integer. The high bits were random, the low bits were random, but the center bits were sequential.
The final result was a code that was much smaller than a guid and looked random, even though it absolutely wasn't.
I was never satisfied with this particular implementation. What would you guys have done?
Here's how I would do it.
I'd obtain a list of common English words with usage frequency and some grammatical information (like is it a noun or a verb?). I think you can look around the intertubes for some copy. Firefox is open-source and it has a spellchecker... so it must be obtainable somehow.
Then I'd run a filter on it so obscure words are removed and that words which are too long are excluded.
Then my generation algorithm would pick 2 words from the list and concatenate them and add a random 3 digits number.
I can also randomize word selection pattern between verb/nouns like
eatCake778
pickBasket524
rideFlyer113
etc..
the case needn't be camel casing, you can randomize that as well. You can also randomize the placement of the number and the verb/noun.
And since that's a lot of randomizing, Jeff's The Danger of Naïveté is a must-read. Also make sure to study dictionary attacks well in advance.
And after I'd implemented it, I'd run a test to make sure that my algorithms should never collide. If the collision rate was high, then I'd play with the parameters (amount of nouns used, amount of verbs used, length of random number, total number of words, different kinds of casings etc.)
In .NET you can use the RNGCryptoServiceProvider method GetBytes() which will "fill an array of bytes with a cryptographically strong sequence of random values" (from ms documentation).
byte[] randomBytes = new byte[4];
RNGCryptoServiceProvider rng = new RNGCryptoServiceProvider();
rng.GetBytes(randomBytes);
You can increase the lengh of the byte array and pluck out the character values you want to allow.
In C#, I have used the 'System.IO.Path.GetRandomFileName() : String' method... but I was generating salt for debug file names. This method returns stuff that looks like your first example, except with a random '.xyz' file extension too.
If you're in .NET and just want a simpler (but not 'nicer' looking) solution, I would say this is it... you could remove the random file extension if you like.
At the time of this writing, this question's title is:
How can I generate a unique, small, random, and user-friendly key?
To that, I should note that it's not possible in general to create a random value that's also unique, at least if each random value is generated independently of any other. In addition, there are many things you should ask yourself if you want to generate unique identifiers (which come from my section on unique random identifiers):
Can the application easily check identifiers for uniqueness within the desired scope and range (e.g., check whether a file or database record with that identifier already exists)?
Can the application tolerate the risk of generating the same identifier for different resources?
Do identifiers have to be hard to guess, be simply "random-looking", or be neither?
Do identifiers have to be typed in or otherwise relayed by end users?
Is the resource an identifier identifies available to anyone who knows that identifier (even without being logged in or authorized in some way)?
Do identifiers have to be memorable?
In your case, you have several conflicting goals: You want identifiers that are—
unique,
easy to type by end users (including small), and
hard to guess (including random).
Important points you don't mention in the question include:
How will the key be used?
Are other users allowed to access the resource identified by the key, whenever they know the key? If not, then additional access control or a longer key length will be necessary.
Can your application tolerate the risk of duplicate keys? If so, then the keys can be completely randomly generated (such as by a cryptographic RNG). If not, then your goal will be harder to achieve, especially for keys intended for security purposes.
Note that I don't go into the issue of formatting a unique value into a "user-friendly key". There are many ways to do so, and they all come down to mapping unique values one-to-one with "user-friendly keys" — if the input value was unique, the "user-friendly key" will likewise be unique.
If by user friendly, you mean that a user could type the answer in then I think you would want to look in a different direction. I've seen and done implementations for initial random passwords that pick random words and numbers as an easier and less error prone string.
If though you're looking for a way to encode a random code in the URL string which is an issue I've dealt with for awhile then I what I have done is use 64-bit encoded GUIDs.
You could load your list of words as chakrit suggested into a data table or xml file with a unique sequential key. When getting your random word, use a random number generator to determine what words to fetch by their key. If you concatenate 2 of them, I don't think you need to include the numbers in the string unless "true randomness" is part of the goal.
I would like to log some simple usage statistics for a mobile application. For the privacy of my users, I would prefer not to store their actual device serial number in the database if possible. Is there a simple algorithm that can hash or transform a serial number into an anonymous identifier and cannot be reversed, while still guaranteeing uniqueness?
Yes, you want to use UUID. Pretty much all languages have this.
As far as I know, they cannot be reversed to get back the original "seed".
Create a HASH of the devices serial number. A hash is by definition a one way encryption. Use the SHA-256 algorithm.
Now, depending on the size of the input, hash functions may create some collisions, this would cause your unique identifier to be not-unique in some (very rare) cases. To avoid this you could append some other piece of arbitrary information to it like IP address or a time stamp.
[SHA-256 of serial number][Time Stamp][Data to store]
I was wondering whether md5, sha1 and anothers return unique values.
For example, sha1() for test returns a94a8fe5ccb19ba61c4c0873d391e987982fbbd3, which is 40 characters long. So, sha1 for strings larger than 40 chars must be the same (of course it's scrambled, because the given input may contain whitespaces and special chars etc.).
Due to this, when we are storing users' passwords, they can enter either their original password or some super-long one, which nobody knows.
Is this right, or do these hash algorithms provide really unique results - I'm quite sure it's hardly possible.
(Note: You're asking about hashing functions, not encryption).
It's impossible for them to be unique, by definition. They take a large input and reduce its size. It obviously follows, then, that they can't represent all the information they have compressed. So no, they don't provide "truly unique" results.
What they do provide, however, is "collision resistant" results. I.e. they try and show that two slightly different datas produce a significantly different hash.
Hashing algorithms (which is what you are referring to) do not provide unique results. What you are referring to is called the Pigeonhole Principle. The number of inputs exceeds the number of outputs, so multiple inputs must be mapped to the same output. This is why the longer the output hash the better, because there are less number of inputs mapped to an output.
Encrypting something must provide a unique results, because you can encrypt a message and decrypt it and get the same message.
SHA1 is not encryption algorithm, but a cryptographic hash function.
You are right - since it maps arbitrary long input to a fixed size hash there can be collisions. But the idea of a cryptographic hash function is to make it impossible to create such collisions "on demand". That's why we call them one-way hash functions, too.
Quote (source):
The ideal cryptographic hash function has four main or significant properties:
* it is easy to compute the hash value for any given message,
* it is infeasible to find a message that has a given hash,
* it is infeasible to modify a message without changing its hash,
* it is infeasible to find two different messages with the same hash.
Hashing algorithms never guarantee a different result for a different input. That's why hashing is always used as a one-way "encryption".
But you have to be realistic, a 160-bit hashing algorithm can have 2^160 possible combinations, which is... a lot! (1 with 48 zeroes)
These are not encryption functions, but hashing ones.
Hashing, by definition, can have two different strings collide (map to the same value) for the very reasons you mention. But that is usually not relevant because:
Cryptographic hashes (such as SHA1) try hard to make the collision probability for similar strings (very, very) low
You cannot deduce the original string from the hash.
These two mean that you cannot take a hash and easily generate one of the strings that map to it.
I understand that according to pigeonhole principle, if number of items is greater than number of containers, then at least one container will have more than one item. Does it matter which container will it be? How does this apply to MD5, SHA1, SHA2 hashes?
No it doesn't matter which container it is, and in fact this is not that important to cryptographic hashes; much more important is the birthday paradox, which says that you only need to hash sqrt(numberNeededByPigeonHolePrincipal) values, on average, before finding a collision.
Thus, the hash needs to be large enough that the square-root of the search space is too large to brute-force. The square-root-of-search-space for SHA1 is 280, and as of March 2012, no two values have ever been found with the same SHA1-hash (though I predict that will happen within the next year or two..); same with SHA2, a family of hashes which all have an even larger search-space. MD5 has been broken for a while though.
If you have more items to hash than you have slots, then you'll have hash collisions. But if you have a poor hashing algorithm, then you'll see collisions even when the items / slots ratio is very small. A good hashing algorithm (including most of the ones you'll see in the wild) will attempt to spread the resulting hashes over the entire output space as evenly as possible, and thus minimize collisions.
Note that a hash collision is not the end of the world. When used in a hash table, for instance, it just means that more than one item is stored in a slot, and the table code will have to traverse a little bit more to find or add the target item, increasing lookup time slightly.
You'll see people refer to MD5 as a "broken" hashing algorithm, when in reality, it's just a poor one to use as a cryptographic hash. It'll be better than one you build yourself.
The point of a hash function is to randomly distribute items into containers. For any good hash function, it doesn't/shouldn't "matter" which container is which as they must be indistinguishable.
This does not apply to "perfect hash" implementations which attempt to do better than random distribution — unlike the algorithms you mentioned.
As Michael mentioned, collisions happen LONG before there are as many items as slots. You must have graceful collision handling (or a perfect hash) if you want to handle the birthday paradox.
I think which application you're using the hash function for is an important distinction. Frequent collision in hashing containers, for example, can degrade performance. Frequent collision in cryptography will have far more devastating consequences (see: cryptographic hash function on Wikipedia).
Collision happens relatively easily even with "decent" hashing algorithm. For example, in Java,
String s = new String(new char[size]);
always hashes to 0. That is, all strings containing only \0 hash to 0 in Java.
As for "does it matter which container will it be?", again it depends on the application. You can design hash functions that would hash "similar" objects to nearby values. This is useful when you want to search for similar objects, for example. Just hash them all and see where they fall. In this case, collisions or near-collisions are desirable, because it groups objects that are similar.
In other applications, you want even the slightest change in the object to result in an entirely different hash value. This is the case in cryptography, for example, where you want to be as certain as possible that something has not been modified. It is far more difficult to find different objects that hash to the same value in this case.
Depending on your application, cryptographic hashes like MDA, SHA1/2 etc. may not be the ideal choice, precisely because they appear as if entirely random, thus giving you collisions as prediced by the birthday paradox. Traditionally, one reason for using simple hashes based on the remainder operation is that keys were expected to be serial numbers or similar, so that a remainder operation would sustain fewer collisions than expected at random. E.g. if the keys are the integers are 1..1000 you might have no collisions at all in a container of size 1009 if your hash function is the key mod 1009. People would sometimes hand-tune systems by carefully picking container size and hash function to achieve an even split.
Of course, if you have to worry about people maliciously choosing keys that will cause you difficulty, or an upstream system sending you very biassed keys (because e.g. it has its own hash table and decides to process all keys that hash to X at once). you may wish to use a hash based on a keyed cryptographic hash function to defend against this.