Latest 1.2.0
Homepage https://github.com/EntropyString/EntropyString-Swift
License MIT
Platforms ios 9.0, osx 10.11, watchos 2.0, tvos 9.0
Authors ,

EntropyString for Swift

Build Status   Carthage   CocoaPods - EntropyString

Efficiently generate cryptographically strong random strings of specified entropy from various character sets.

TOC

Installation

Carthage

Carthage is a decentralized dependency manager for Objective-C and Swift.

  1. Add the project to your Cartfile.

    github "EntropyString/EntropyString-Swift.git"
  2. Run carthage update and follow the Carthage getting started steps.

  3. Import module EntropyString

    import EntropyString

CocoaPods

CocoaPods is a centralized dependency manager for Objective-C and Swift.

  1. Add the project to your Podfile.

    use_frameworks!
    
    pod 'EntropyString', '~> 1.0.0'
  2. Run pod install and open the .xcworkspace file to launch Xcode.

  3. Import module EntropyString

    import EntropyString

Swift Package Manager

CxNote: Linux not yet supported until random byte generation issue resolved.

The Swift Package Manager is a decentralized dependency manager for Swift.

  1. Add the project to your Package.swift.

    import PackageDescription
    
    let package = Package(
        name: "YourProject",
        dependencies: [
            .Package(url: "https://github.com/EntropyString/EntropyString-Swift.git",
                     majorVersion: 1)
        ]
    )
  2. Import module EntropyString

    import EntropyString

TOC


The remainer of this README is included in the project as a Swift playground for interactive exploration.


TL;DR

  import EntropyString

48-bit string using base32 characters:

  var bits = 48
  var string = RandomString.entropy(of: bits, using: .charSet32)

MRd272t4G3

48-bit string using hex characters:

  string = RandomString.entropy(of: bits, using: .charSet16)

7973b7cf643c

48-bit string using uppercase hex characters:

  let randomString = RandomString()
  try! randomString.use("0123456789ABCDEF", for: .charSet16)
  string = randomString.entropy(of: bits, using: .charSet16)

6D98AA8E6A46

Base 32 character string with a 1 in a million chance of a repeat in 30 such strings:

  bits = Entropy.bits(for: 30, risk: 1000000)
  string = RandomString.entropy(of: bits, using: .charSet32)

BqMhJM

Base 32 character string with a 1 in a trillion chance of a repeat in 10 million such strings:

  bits = Entropy.bits(for: .ten07, risk: .ten12)
  string = RandomString.entropy(of: bits, using: .charSet32)

H9fT8qmMBd9qLfqmpm

OWASP session ID using file system and URL safe characters:

  bits = 128
  string = RandomString.entropy(of: bits, using: .charSet64)

RX3FzLm2YZmeBT2Y5n_79C

TOC

Overview

EntropyString provides easy creation of randomly generated strings of specific entropy using various character sets. Such strings are needed when generating, for example, random IDs and you don’t want the overkill of a GUID, or for ensuring that some number of items have unique names.

A key concern when generating such strings is that they be unique. To truly guarantee uniqueness requires that each newly created string be compared against all existing strings. The overhead of storing and comparing strings in this manner is often too onerous and a different tack is needed.

A common strategy is to replace the guarantee of uniqueness with a weaker but hopefully sufficient probabilistic uniqueness. Specifically, rather than being absolutely sure of uniqueness, we settle for a statement such as "there is less than a 1 in a billion chance that two of my strings are the same". This strategy requires much less overhead, but does require we have some manner of qualifying what we mean by, for example, "there is less than a 1 in a billion chance that 1 million strings of this form will have a repeat".

Understanding probabilistic uniqueness requires some understanding of entropy and of estimating the probability of a collision (i.e., the probability that two strings in a set of randomly generated strings might be the same). Happily, you can use EntropyString without a deep understanding of these topics.

We’ll begin investigating EntropyString by considering our Real Need when generating random strings.

TOC

Real Need

Let’s start by reflecting on a common statement of need for developers, who might say:

I need random strings 16 characters long.

Okay. There are libraries available that address that exact need. But first, there are some questions that arise from the need as stated, such as:

  1. What characters do you want to use?
  2. How many of these strings do you need?
  3. Why do you need these strings?

The available libraries often let you specify the characters to use. So we can assume for now that question 1 is answered with:

Hexadecimal will do fine.

As for question 2, the developer might respond:

I need 10,000 of these things.

Ah, now we’re getting somewhere. The answer to question 3 might lead to the further qualification:

I need to generate 10,000 random, unique IDs.

And the cat’s out of the bag. We’re getting at the real need, and it’s not the same as the original statement. The developer needs uniqueness across a total of some number of strings. The length of the string is a by-product of the uniqueness, not the goal.

As noted in the Overview, guaranteeing uniqueness is difficult, so we’ll replace that declaration with one of probabilistic uniqueness by asking:

  • What risk of a repeat are you willing to accept?

Probabilistic uniqueness contains risk. That’s the price we pay for giving up on the stronger declaration of strict uniqueness. But the developer can quantify an appropriate risk for a particular scenario with a statement like:

I guess I can live with a 1 in a million chance of a repeat.

So now we’ve gotten to the developer’s real need:

I need 10,000 random hexadecimal IDs with less than 1 in a million chance of any repeats.

Not only is this statement more specific, there is no mention of string length. The developer needs probabilistic uniqueness, and strings are to be used to capture randomness for this purpose. As such, the length of the string is simply a by-product of the encoding used to represent the required uniqueness as a string.

How do you address this need using a library designed to generate strings of specified length? Well, you don’t directly, because that library was designed to answer the originally stated need, not the real need we’ve uncovered. We need a library that deals with probabilistic uniqueness of a total number of some strings. And that’s exactly what EntropyString does.

Let’s use EntropyString to help this developer by generating 5 IDs:

  import EntropyString

  let bits = Entropy.bits(for: 10000, risk: .ten06)
  var strings = [String]()
  for i in 0 ..< 5 {
    let string = RandomString.entropy(of: bits, using: .charSet16)
    strings.append(string)
  }
  print("Strings: (strings)")

Strings: ["85e442fa0e83", "a74dc126af1e", "368cd13b1f6e", "81bf94e1278d", "fe7dec099ac9"]

To generate the IDs, we first use

    let bits = Entropy.bits(for: 10000, risk: .ten06)

to determine how much entropy is needed to satisfy the probabilistic uniqueness of a 1 in a million (ten to the sixth power) risk of repeat in a total of 10,000 strings. We didn’t print the result, but if you did you’d see it’s about 45.51 bits. Then inside a loop we used

    let string = RandomString.entropy(of: bits, using: .charSet16)

to actually generate a random string of the specified entropy using hexadecimal (charSet16) characters. Looking at the IDs, we can see each is 12 characters long. Again, the string length is a by-product of the characters used to represent the entropy we needed. And it seems the developer didn’t really need 16 characters after all.

Finally, given that the strings are 12 hexadecimals long, each string actually has an information carrying capacity of 12 4 = 48 bits of entropy (a hexadecimal character carries 4 bits). That’s fine. Assuming all characters are equally probable, a string can only carry entropy equal to a multiple of the amount of entropy represented per character. EntropyString produces the smallest strings that exceed* the specified entropy.

TOC

More Examples

In Real Need our developer used hexadecimal characters for the strings. Let’s look at using other characters instead.

We’ll start with using 32 characters. What 32 characters, you ask? Well, the Character Sets section discusses the default characters available in EntropyString and the Custom Characters section describes how you can use whatever characters you want. For now we’ll stick to the provided defaults.

  import EntropyString

  var bits = Entropy.bits(for: 10000, risk: .ten06)
  var string = RandomString.entropy(of: bits, using: .charSet32)
  print("String: (string)n")

String: PmgMJrdp9h

We’re using the same bits calculation since we haven’t changed the number of IDs or the accepted risk of probabilistic uniqueness. But this time we use 32 characters and our resulting ID only requires 10 characters (and can carry 50 bits of entropy).

Now let’s suppose we need to ensure the names of a handful of items are unique. Let’s say 30 items. And let’s decide we can live with a 1 in 100,000 probability of collision (we’re just futzing with some code ideas). Using hex characters:

  bits = Entropy.bits(for: 30, risk: .ten05)
  string = RandomString.entropy(of: bits, using: .charSet16)
  print("String: (string)n")

String: 766923a

Using the CharSet 4 characters:

  string = RandomString.entropy(of: bits, using: .charSet4)
  print("String: (string)n")

String: GCGTCGGGTTTTA

Okay, we probably wouldn’t use 4 characters (and what’s up with those characters?), but you get the idea.

Suppose we have a more extreme need. We want less than a 1 in a trillion chance that 10 billion strings of 32 characters repeat. Let’s see, our risk (trillion) is 10 to the 12th and our total (10 billion) is 10 to the 10th, so:

  bits = Entropy.bits(for: .ten10, risk: .ten12)
  string = RandomString.entropy(of: bits, using: .charSet32)
  print("String: (string)n")

String: F78PmfGRNfJrhHGTqpt6Hn

Finally, let say we’re generating session IDs. We’re not interested in uniqueness per se, but in ensuring our IDs aren’t predicatable since we can’t have the bad guys guessing a valid ID. In this case, we’re using entropy as a measure of unpredictability of the IDs. Rather than calculate our entropy, we declare it needs to be 128 bits (since we read on some web site that session IDs should be 128 bits).

  string = RandomString.entropy(of: 128, using: .charSet64)
  print("String: (string)n")

String: b0Gnh6H5cKCjWrCLwKoeuN

Using 64 characters, our string length is 22 characters. That’s actually 132 bits, so we’ve got our OWASP requirement covered! 😌

Also note that we covered our need using strings that are only 22 characters in length. So long to using GUID strings which only carry 122 bits of entropy (commonly used version 4) and use string representations that are 36 characters long (hex with dashes).

TOC

Character Sets

As we’ve seen in the previous sections, EntropyString provides default characters for each of the supported character sets. Let’s see what’s under the hood.

  import EntropyString

  print("CharSet 64: (RandomString.characters(for: .charSet64))n")

The call to RandomString.characters(for:) returns the characters used for any of the sets defined by the CharSet enum. The following code reveals all the character sets.

  print("CharSet 32: (RandomString.characters(for: .charSet32))n")
  print("CharSet 16: (RandomString.characters(for: .charSet16))n")
  print("CharSet  8: (RandomString.characters(for: .charSet8))n")
  print("CharSet  4: (RandomString.characters(for: .charSet4))n")
  print("CharSet  2: (RandomString.characters(for: .charSet2))n")

The default character sets were chosen as follows:

  • CharSet 64: ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-_
    • The file system and URL safe char set from RFC 4648.
  • CharSet 32: 2346789bdfghjmnpqrtBDFGHJLMNPQRT

    • Remove all upper and lower case vowels (including y)
    • Remove all numbers that look like letters
    • Remove all letters that look like numbers
    • Remove all letters that have poor distinction between upper and lower case values.
      The resulting strings don’t look like English words and are easy to parse visually.
  • CharSet 16: 0123456789abcdef
    • Hexadecimal
  • CharSet 8: 01234567
    • Octal
  • CharSet 4: ATCG
    • DNA alphabet. No good reason; just wanted to get away from the obvious.
  • CharSet 2: 01
    • Binary

You may, of course, want to choose the characters used, which is covered next in Custom Characters.

TOC

Custom Characters

Being able to easily generate random strings is great, but what if you want to specify your own characters? For example, suppose you want to visualize flipping a coin to produce 10 bits of entropy.

  import EntropyString

  let randomString = RandomString()
  var flips = randomString.entropy(of: 10, using: .charSet2)
  print("flips: (flips)n")

flips: 0101001110

The resulting string of 0‘s and 1‘s doesn’t look quite right. You want to use the characters H and T instead.

  try! randomString.use("HT", for: .charSet2)
  flips = randomString.entropy(of: 10, using: .charSet2)
  print("flips: (flips)n")

flips: HTTTHHTTHH

Note that setting custom characters in the above code requires using an instance of RandomString, wheras in the previous sections we used class functions for all calls. The function signatures are the same in each case, but you can’t change the static character sets used in the class RandomString (i.e., there is no RandomString.use(_,for:) function).

As another example, we saw in Character Sets the default characters for CharSet 16 are 0123456789abcdef. Suppose you like uppercase hexadecimal letters instead.

  try! randomString.use("0123456789ABCDEF", for: .charSet16)
  let hex = randomString.entropy(of: 48, using: .charSet16)
  print("hex: (hex)n")

hex: 4D20D9AA862C

Or suppose you want a random password with numbers, lowercase letters and special characters.

  try! randomString.use("1234567890abcdefghijklmnopqrstuvwxyz-=[];,./~!@#$%^&*()_+{}|:<>?", for: .charSet64)
  let password = randomString.entropy(of: 64, using: .charSet64)
  print("password: (password)")

password: }4?0x*$o_=w

Note that randomString.use(_,for:) throws a RandomStringError if the number of characters doesn’t match the number required for the character set or if the characters are not unique.

  do {
    try randomString.use("abcdefg", for: .charSet8)
  }
  catch {
    print(error)
  }

invalidCharCount

  do {
    try randomString.use("01233210", for: .charSet8)
  }
  catch {
    print(error)
  }

charsNotUnique

TOC

Efficiency

To efficiently create random strings, EntropyString generates the necessary number of bytes needed
for each string and uses those bytes in a bit shifting scheme to index into a character set. For example, consider generating strings from the .charSet32 character set. There are 32 characters in the set, so an index into an array of those characters would be in the range [0,31]. Generating a random string of .charSet32 characters is thus reduced to generating a random sequence of indices in the range [0,31].

To generate the indices, EntropyString slices just enough bits from the array of bytes to create each index. In the example at hand, 5 bits are needed to create an index in the range [0,31]. EntropyString processes the byte array 5 bits at a time to create the indices. The first index comes from the first 5 bits of the first byte, the second index comes from the last 3 bits of the first byte combined with the first 2 bits of the second byte, and so on as the byte array is systematically sliced to form indices into the character set. And since bit shifting and addition of byte values is really efficient, this scheme is quite fast.

The EntropyString scheme is also efficient with regard to the amount of randomness used. Consider the following common Swift solution to generating random strings. To generated a character, an index into the available characters is create using arc4random_uniform. The code looks something like:

  for _ in 0 ..< len {
    let offset = Int(arc4random_uniform(charCount))
    let index = chars.index(chars.startIndex, offsetBy: offset)
    let char = chars[index]
    string += String(char)
  }

In the code above, arc4random_uniform generates 32 bits of randomness per call, returned as an UInt32. The returned value is used to create an index. Suppose we’re creating strings with len=16 and charCount=32. Each char consumes 32 bits of randomness (UInt32) while only injecting 5 bits (log2(32)) of entropy into string. The resulting string has an information carrying capacity of 80 bits. So creating each string requires a total of 512 bits of randomness while only actually carrying 80 bits of that entropy forward in the string itself. That means 432 bits (84% of the total) of the generated randomness is simply wasted.

Compare that to the EntropyString scheme. For the example above, slicing off 5 bits at a time requires a total of 80 bits (10 bytes). Creating the same strings as above, EntropyString uses 80 bits of randomness per string with no wasted bits. In general, the EntropyString scheme can waste up to 7 bits per string, but that’s the worst case scenario and that’s per string, not per character!

Fortunately you don’t need to really understand how the bytes are efficiently sliced and diced to get the string. But you may want to know that Secure Bytes are used, and that’s the next topic.

TOC

Secure Bytes

As described in Efficiency, EntropyString uses an underlying array of bytes to generate strings. The entropy of the resulting strings is, of course, directly related to the randomness of the bytes used. That’s an important point. Strings are only capable of carrying information (entropy); random bytes actually provide the entropy itself.

EntropyString automatically generates the necessary number of bytes needed to create a random string. On Apple OSes, EntropyString uses either SecRandomCopyBytes or arc4random_buf, both of which are cryptographically secure random number generators. SecRandomCopyBytes is the stronger of the two, but can fail if the system entropy pool lacks sufficient randomness. Rather than propagate that failure, if SecRandomCopyBytes fails EntropyString falls back and uses arc4random_buf to generate the bytes. Though not as strong, arc4random_buf does not fail.

You may, of course, want feedback as to when or if SecRandomCopyBytes fails. RandomString.entropy(of:using:secRand) provides an additional inout parameter that acts as a flag should a SecRandomCopyBytes call fail.

On Linux OSes, EntropyString always uses arc4random_buf. The secRand parameter in the RandomString.entropy(of:using:secRand) is ignored.

  import EntropyString

  var secRand = true
  RandomString.entropy(of: 20, using: .charSet32, secRand: &secRand)
  print("secRand: (secRand)")

secRand: true

If SecRandomCopyBytes is used, the secRand parameter will remain true; otherwise it will be flipped to false.

You can also pass in secRand as false, in which case the entropy call will not attempt to use SecRandomCopyBytes and will use arc4random_buf instead.

  secRand = false
  RandomString.entropy(of: 20, using: .charSet32, secRand: &secRand)

Rather than have EntropyString generate bytes automatically, you can provide your own Custom Bytes to create a string, which is the next topic.

TOC

Custom Bytes

As described in Secure Bytes, EntropyString automatically generates random bytes using either SecRandomCopyBuf or arc4random_buf. These functions are fine, but you may have a need to provide your own bytes, say for deterministic testing or to use a specialized byte genterator. The RandomString.entropy(of:using:bytes) function allows passing in your own bytes to create a string.

Suppose we want a string capable of 30 bits of entropy using 32 characters. We pass in 4 bytes (to cover the 30 bits):

  import EntropyString

  let bytes: RandomString.Bytes = [250, 200, 150, 100]
  let string = try! RandomString.entropy(of: 30, using: .charSet32, bytes: bytes)
  print("String: (string)n")

string: Th7fjL

The bytes provided can come from any source. However, the number of bytes must be sufficient to generate the string as described in the Efficiency section. RandomString.entropy(of:using:bytes) throws RandomString.RandomError.tooFewBytes if the string cannot be formed from the passed bytes.

  do {
    try RandomString.entropy(of: 32, using: .charSet32, bytes: bytes)
  }
  catch {
    print(error)
  }

error: tooFewBytes

Note how the number of bytes needed is dependent on the number of characters in our set. In using a string to represent entropy, we can only have multiples of the bits of entropy per character used. So in the example above, to get at least 32 bits of entropy using a character set of 32 characters (5 bits per char), we’ll need enough bytes to cover 35 bits, not 32, so a tooFewBytes error is thrown.

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Latest podspec

{
    "name": "EntropyString",
    "version": "1.2.0",
    "summary": "Efficiently generate cryptographically strong random strings of specified entropy from various character sets.",
    "description": "Efficiently generate cryptographically strong and secure random strings of specified entropy from various character sets for use when probabilisticly unique string identifiers are needed. Entropy is calculated from a total number of strings and acceptable risk of a repeat.",
    "homepage": "https://github.com/EntropyString/EntropyString-Swift",
    "license": {
        "type": "MIT",
        "file": "LICENSE"
    },
    "authors": {
        "knoxen": "[email protected]",
        "dingo sky": "[email protected]"
    },
    "social_media_url": "http://twitter.com/knoxen",
    "platforms": {
        "ios": "9.0",
        "osx": "10.11",
        "watchos": "2.0",
        "tvos": "9.0"
    },
    "source": {
        "git": "https://github.com/EntropyString/EntropyString-Swift.git",
        "tag": "1.2.0"
    },
    "source_files": "Sources/**/*.swift",
    "pushed_with_swift_version": "3.1"
}

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