更新。net 6。RNGCryptoServiceProvider被标记为obsolete。相反，调用RandomNumberGenerator.Create()。答案中的代码已相应更新。

根据评论更新。原始实现生成a-h的时间为1.95%，其余字符的时间为1.56%。更新生成所有字符~1.61%的时间。 FRAMEWORK支持- . net Core 3(以及未来支持. net Standard 2.1或以上版本的平台)提供了一个加密的方法RandomNumberGenerator.GetInt32()，在期望的范围内生成一个随机整数。

与目前提出的一些替代方案不同，这个方案在密码学上是合理的。

using System;
using System.Security.Cryptography;
using System.Text;

namespace UniqueKey
{
    public class KeyGenerator
    {
        internal static readonly char[] chars =
            "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890".ToCharArray(); 

        public static string GetUniqueKey(int size)
        {            
            byte[] data = new byte[4*size];
            using (var crypto = RandomNumberGenerator.Create())
            {
                crypto.GetBytes(data);
            }
            StringBuilder result = new StringBuilder(size);
            for (int i = 0; i < size; i++)
            {
                var rnd = BitConverter.ToUInt32(data, i * 4);
                var idx = rnd % chars.Length;

                result.Append(chars[idx]);
            }

            return result.ToString();
        }

        public static string GetUniqueKeyOriginal_BIASED(int size)
        {
            char[] chars =
                "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890".ToCharArray();
            byte[] data = new byte[size];
            using (RNGCryptoServiceProvider crypto = new RNGCryptoServiceProvider())
            {
                crypto.GetBytes(data);
            }
            StringBuilder result = new StringBuilder(size);
            foreach (byte b in data)
            {
                result.Append(chars[b % (chars.Length)]);
            }
            return result.ToString();
        }
    }
}

基于这里对替代方案的讨论，并根据下面的评论进行了更新/修改。

下面是一个小型测试工具，演示了旧输出和更新输出中的字符分布。关于随机性分析的深入讨论，请访问random.org。

using System;
using System.Collections.Generic;
using System.Linq;
using UniqueKey;

namespace CryptoRNGDemo
{
    class Program
    {

        const int REPETITIONS = 1000000;
        const int KEY_SIZE = 32;

        static void Main(string[] args)
        {
            Console.WriteLine("Original BIASED implementation");
            PerformTest(REPETITIONS, KEY_SIZE, KeyGenerator.GetUniqueKeyOriginal_BIASED);

            Console.WriteLine("Updated implementation");
            PerformTest(REPETITIONS, KEY_SIZE, KeyGenerator.GetUniqueKey);
            Console.ReadKey();
        }

        static void PerformTest(int repetitions, int keySize, Func<int, string> generator)
        {
            Dictionary<char, int> counts = new Dictionary<char, int>();
            foreach (var ch in UniqueKey.KeyGenerator.chars) counts.Add(ch, 0);

            for (int i = 0; i < REPETITIONS; i++)
            {
                var key = generator(KEY_SIZE); 
                foreach (var ch in key) counts[ch]++;
            }

            int totalChars = counts.Values.Sum();
            foreach (var ch in UniqueKey.KeyGenerator.chars)
            {
                Console.WriteLine($"{ch}: {(100.0 * counts[ch] / totalChars).ToString("#.000")}%");
            }
        }
    }
}

更新7/25/2022

根据评论中的一个问题，我很好奇这种分布是否真的是随机的。

我不是统计学家，但我可以在电视上扮演一个。如果一位真正的统计学家愿意插话，那将是最受欢迎的。

有62个可能的输出值(A-Za-Z0-9)和int。用于选择数组索引的最大值。int。MaxValue % 62是1，所以一个字符被选中的概率是其他字符的十亿分之一。我们可以通过在索引之前随机旋转输出值数组来进一步减少选择偏差。

t检验或其他统计度量将是确定输出结果中是否存在偏差的最佳方法，但这不是我在午休时间可以完成的工作，因此我留给您对上述代码的修改，以度量与预期的偏差。注意，它趋于零。

using System.Security.Cryptography;
using System.Text;

const int REPETITIONS = 1_000_000;
const int KEY_SIZE = 32;
int TASK_COUNT = Environment.ProcessorCount - 1;

var expectedPercentage = 100.0 / KeyGenerator.chars.Length;

var done = false;
var iterationNr = 1;
var totalRandomSymbols = 0L;

var grandTotalCounts = new Dictionary<char, long>();
foreach (var ch in KeyGenerator.chars) grandTotalCounts.Add(ch, 0);

while (!done)
{
    var experiments = Enumerable.Range(0, TASK_COUNT).Select(i => Task.Run(Experiment)).ToArray();
    Task.WaitAll(experiments);
    var totalCountsThisRun = experiments.SelectMany(e => e.Result)
        .GroupBy(e => e.Key)
        .Select(e => new { e.Key, Count = e.Select(_ => _.Value).Sum() })
        .ToDictionary(e => e.Key, e => e.Count);

    foreach (var ch in KeyGenerator.chars)
        grandTotalCounts[ch] += totalCountsThisRun[ch];

    var totalChars = grandTotalCounts.Values.Sum();
    totalRandomSymbols += totalChars;

    var distributionScores = KeyGenerator.chars.Select(ch =>
    new
    {
        Symbol = ch,
        OverUnder = (100.0 * grandTotalCounts[ch] / totalChars) - expectedPercentage

    });

    Console.WriteLine($"Iteration {iterationNr++}. Total random symbols: {totalRandomSymbols:N0}");
    foreach (var chWithValue in distributionScores.OrderByDescending(c => c.OverUnder))
    {
        Console.WriteLine($"{chWithValue.Symbol}: {chWithValue.OverUnder:#.00000}%");
    }

    done = Console.KeyAvailable;        
}

Dictionary<char, long> Experiment()
{
    var counts = new Dictionary<char, long>();
    foreach (var ch in KeyGenerator.chars) counts.Add(ch, 0);

    for (int i = 0; i < REPETITIONS; i++)
    {
        var key = KeyGenerator.GetUniqueKey(KEY_SIZE);
        foreach (var ch in key) counts[ch]++;
    }

    return counts;
}

public class KeyGenerator
{
    internal static readonly char[] chars =
        "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890".ToCharArray();

    public static string GetUniqueKey(int size)
    {
        byte[] data = new byte[4 * size];
        using (var crypto = RandomNumberGenerator.Create())
        {
            crypto.GetBytes(data);
        }
        StringBuilder result = new StringBuilder(size);
        for (int i = 0; i < size; i++)
        {
            var rnd = BitConverter.ToUInt32(data, i * 4);
            var idx = rnd % chars.Length;

            result.Append(chars[idx]);
        }

        return result.ToString();
    }
}

我听说LINQ是新的黑色，所以下面是我使用LINQ的尝试:

private static Random random = new Random();

public static string RandomString(int length)
{
    const string chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789";
    return new string(Enumerable.Repeat(chars, length)
        .Select(s => s[random.Next(s.Length)]).ToArray());
}

(注意:Random类的使用使得它不适用于任何与安全性相关的事情，比如创建密码或令牌。如果你需要强随机数生成器，请使用RNGCryptoServiceProvider类。)

非常简单的解决方案。它使用ASCII值，只是在它们之间生成“随机”字符。

public static class UsernameTools
{
    public static string GenerateRandomUsername(int length = 10)
    {
        Random random = new Random();
        StringBuilder sbuilder = new StringBuilder();
        for (int x = 0; x < length; ++x)
        {
            sbuilder.Append((char)random.Next(33, 126));
        }
        return sbuilder.ToString();
    }

}

一个包含所有字母字符和数字的解决方案，你可以随心所欲地更改:

public static string RandomString(int length)
{
    Random rand = new Random();
    string charbase = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789";
    return new string(Enumerable.Range(0,length)
           .Select(_ => charbase[rand.Next(charbase.Length)])
           .ToArray());
}

如果你喜欢单行方法;)

public static Random rand = new Random();
public const string charbase = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789";
 

public static string RandomString(int length) =>
        new string(Enumerable.Range(0,length).Select(_ => charbase[rand.Next(charbase.Length)]).ToArray());

如果你的值不是完全随机的，但实际上可能依赖于某些东西——你可以计算出“某个东西”的md5或sha1哈希，然后将其截断为你想要的任何长度。

你也可以生成和截断一个guid。

在查看了其他答案并考虑了CodeInChaos的评论，以及CodeInChaos仍然有偏见(尽管较少)的答案之后，我认为需要一个最终的终极剪切和粘贴解决方案。所以在更新我的答案时，我决定全力以赴。

For an up to date version of this code, please visit the new Hg repository on Bitbucket: https://bitbucket.org/merarischroeder/secureswiftrandom. I recommend you copy and paste the code from: https://bitbucket.org/merarischroeder/secureswiftrandom/src/6c14b874f34a3f6576b0213379ecdf0ffc7496ea/Code/Alivate.SolidSwiftRandom/SolidSwiftRandom.cs?at=default&fileviewer=file-view-default (make sure you click the Raw button to make it easier to copy and make sure you have the latest version, I think this link goes to a specific version of the code, not the latest).

更新说明:

Relating to some other answers - If you know the length of the output, you don't need a StringBuilder, and when using ToCharArray, this creates and fills the array (you don't need to create an empty array first) Relating to some other answers - You should use NextBytes, rather than getting one at a time for performance Technically you could pin the byte array for faster access.. it's usually worth it when your iterating more than 6-8 times over a byte array. (Not done here) Use of RNGCryptoServiceProvider for best randomness Use of caching of a 1MB buffer of random data - benchmarking shows cached single bytes access speed is ~1000x faster - taking 9ms over 1MB vs 989ms for uncached. Optimised rejection of bias zone within my new class.

问题的最终解决方案:

static char[] charSet =  "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789".ToCharArray();
static int byteSize = 256; //Labelling convenience
static int biasZone = byteSize - (byteSize % charSet.Length);
public string GenerateRandomString(int Length) //Configurable output string length
{
    byte[] rBytes = new byte[Length]; //Do as much before and after lock as possible
    char[] rName = new char[Length];
    SecureFastRandom.GetNextBytesMax(rBytes, biasZone);
    for (var i = 0; i < Length; i++)
    {
        rName[i] = charSet[rBytes[i] % charSet.Length];
    }
    return new string(rName);
}

但是你需要我的新(未经测试的)类:

/// <summary>
/// My benchmarking showed that for RNGCryptoServiceProvider:
/// 1. There is negligable benefit of sharing RNGCryptoServiceProvider object reference 
/// 2. Initial GetBytes takes 2ms, and an initial read of 1MB takes 3ms (starting to rise, but still negligable)
/// 2. Cached is ~1000x faster for single byte at a time - taking 9ms over 1MB vs 989ms for uncached
/// </summary>
class SecureFastRandom
{
    static byte[] byteCache = new byte[1000000]; //My benchmark showed that an initial read takes 2ms, and an initial read of this size takes 3ms (starting to raise)
    static int lastPosition = 0;
    static int remaining = 0;

    /// <summary>
    /// Static direct uncached access to the RNGCryptoServiceProvider GetBytes function
    /// </summary>
    /// <param name="buffer"></param>
    public static void DirectGetBytes(byte[] buffer)
    {
        using (var r = new RNGCryptoServiceProvider())
        {
            r.GetBytes(buffer);
        }
    }

    /// <summary>
    /// Main expected method to be called by user. Underlying random data is cached from RNGCryptoServiceProvider for best performance
    /// </summary>
    /// <param name="buffer"></param>
    public static void GetBytes(byte[] buffer)
    {
        if (buffer.Length > byteCache.Length)
        {
            DirectGetBytes(buffer);
            return;
        }

        lock (byteCache)
        {
            if (buffer.Length > remaining)
            {
                DirectGetBytes(byteCache);
                lastPosition = 0;
                remaining = byteCache.Length;
            }

            Buffer.BlockCopy(byteCache, lastPosition, buffer, 0, buffer.Length);
            lastPosition += buffer.Length;
            remaining -= buffer.Length;
        }
    }

    /// <summary>
    /// Return a single byte from the cache of random data.
    /// </summary>
    /// <returns></returns>
    public static byte GetByte()
    {
        lock (byteCache)
        {
            return UnsafeGetByte();
        }
    }

    /// <summary>
    /// Shared with public GetByte and GetBytesWithMax, and not locked to reduce lock/unlocking in loops. Must be called within lock of byteCache.
    /// </summary>
    /// <returns></returns>
    static byte UnsafeGetByte()
    {
        if (1 > remaining)
        {
            DirectGetBytes(byteCache);
            lastPosition = 0;
            remaining = byteCache.Length;
        }

        lastPosition++;
        remaining--;
        return byteCache[lastPosition - 1];
    }

    /// <summary>
    /// Rejects bytes which are equal to or greater than max. This is useful for ensuring there is no bias when you are modulating with a non power of 2 number.
    /// </summary>
    /// <param name="buffer"></param>
    /// <param name="max"></param>
    public static void GetBytesWithMax(byte[] buffer, byte max)
    {
        if (buffer.Length > byteCache.Length / 2) //No point caching for larger sizes
        {
            DirectGetBytes(buffer);

            lock (byteCache)
            {
                UnsafeCheckBytesMax(buffer, max);
            }
        }
        else
        {
            lock (byteCache)
            {
                if (buffer.Length > remaining) //Recache if not enough remaining, discarding remaining - too much work to join two blocks
                    DirectGetBytes(byteCache);

                Buffer.BlockCopy(byteCache, lastPosition, buffer, 0, buffer.Length);
                lastPosition += buffer.Length;
                remaining -= buffer.Length;

                UnsafeCheckBytesMax(buffer, max);
            }
        }
    }

    /// <summary>
    /// Checks buffer for bytes equal and above max. Must be called within lock of byteCache.
    /// </summary>
    /// <param name="buffer"></param>
    /// <param name="max"></param>
    static void UnsafeCheckBytesMax(byte[] buffer, byte max)
    {
        for (int i = 0; i < buffer.Length; i++)
        {
            while (buffer[i] >= max)
                buffer[i] = UnsafeGetByte(); //Replace all bytes which are equal or above max
        }
    }
}

对于历史-我对这个答案的旧解决方案，使用随机对象:

    private static char[] charSet =
      "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789".ToCharArray();

    static rGen = new Random(); //Must share, because the clock seed only has Ticks (~10ms) resolution, yet lock has only 20-50ns delay.
    static int byteSize = 256; //Labelling convenience
    static int biasZone = byteSize - (byteSize % charSet.Length);
    static bool SlightlyMoreSecurityNeeded = true; //Configuration - needs to be true, if more security is desired and if charSet.Length is not divisible by 2^X.
    public string GenerateRandomString(int Length) //Configurable output string length
    {
      byte[] rBytes = new byte[Length]; //Do as much before and after lock as possible
      char[] rName = new char[Length];
      lock (rGen) //~20-50ns
      {
          rGen.NextBytes(rBytes);

          for (int i = 0; i < Length; i++)
          {
              while (SlightlyMoreSecurityNeeded && rBytes[i] >= biasZone) //Secure against 1/5 increased bias of index[0-7] values against others. Note: Must exclude where it == biasZone (that is >=), otherwise there's still a bias on index 0.
                  rBytes[i] = rGen.NextByte();
              rName[i] = charSet[rBytes[i] % charSet.Length];
          }
      }
      return new string(rName);
    }

性能:

securefastrrandom -第一次运行= ~9-33ms。听不清。持续:5毫秒(有时高达13毫秒)超过10,000次迭代，单次平均迭代= 1.5微秒。注意:通常需要2个缓存刷新，但偶尔需要8个缓存刷新——这取决于有多少单个字节超出了偏置区域 随机-第一次运行= ~0-1ms。听不清。正在进行:5毫秒超过10,000次迭代。单次平均迭代= 0.5微秒。速度差不多。

还可以看看:

https://bitbucket.org/merarischroeder/number-range-with-no-bias/src https://stackoverflow.com/a/45118325/887092

这些联系是另一种方法。缓冲可以添加到这个新的代码库中，但最重要的是探索不同的方法来消除偏差，并对速度和利弊进行基准测试。