When creating a password sometimes I need the password to contain uppercase, lowercase, number or a symbol. If I create the password like this it's not guaranteed that each is represented:
$input = 'a..zA..Z0..9$!_'; // generally the whole allowed alphabet
while (strlen($password) < 10) {
$password .= securelyPickRandomCharacterFromString($input);
}
In this case there is a chance that the random process (even though it's safe and completely random) will only select lowercase letters.
I can think of naive approaches like first selecting 1 from each group and then filling the rest randomly. But that would not be cryptographically safe as say random_bytes() is. Maybe shuffle after?
What would be a good and safe algorithm to generate the password containing each "group" of characters.
Note: The password requirements are usually for external system and are out of my control.
Here's a really naive way of doing it (pseudo code):
// Create your lists/arrays of groups
char listLowerCase[] = {'a','b',....};
char listUpperCase[] = {'A','B',....};
char listNums[] = {'1','2',....};
char listSpecialChars[] = {'#','#',...};
// Form a password from those groups (I'm just gonna use length 4 as example)
String pwd = listLowerCase[randomNumber] + listUpperCase[randomNumber]
+ listNums[randomNumber] + listSpecialChars[randomNumber];
// Shuffle the password
String shuffledPwd = shuffle(pwd);
In case you don't want to have the same number of characters from each group, you could try also setting random numbers that determine how many characters you will use in each group. Hope that helped you.
The simplest approach by far is to generate a random password using the chosen alphabet. Then check for constraints such as “must contain at least one of each character class”. If the password doesn't match the constraints, try again in a loop.
do {
$password = '';
while (strlen($password) < 10) {
$password .= securelyPickRandomCharacterFromString($input);
}
} while (!matches_constraints($password));
Within the given constraints on acceptable passwords, this has optimal security, since every acceptable password of the chosen length is equally likely.
In addition to simplicity and security, another benefit of this approach is that it accommodates arbitrary constraints, such as the common “must contain at least 3 of 4 character classes”, without having to write different, complex code for each different constraint.
With any sensible parameters, the number of redraws will be small. For example, a 10-character password generated from non-space printable ASCII characters (94-character alphabet) has a ((94-10)/94)^10 ≈ 0.32 chance of not containing a digit, the smallest of the four character classes. The chance of missing one of the four classes is a little under 0.42. So on average you'll need a little less than 2 iterations to get a suitable password with these particular parameters. Generating random letters is cheap, so performance is not a problem.
You should include protection against nonsensical parameters, for example attempting to generate a 3-character password that contains characters from all 4 classes (which would loop forever), or a 4-character password that contains characters from all 4 classes (which could take a very long time). So add a maximum iteration count.
$iterations = 0;
do {
if ($iterations >= 10) throw new NonsensicalPasswordConstraintException;
$password = '';
while (strlen($password) < 10) {
$password .= securelyPickRandomCharacterFromString($input);
}
++$iterations;
} while (!matches_constraints($password));
Related
I want to create a random string of a fixed length (8 chars in my use case) and the generated string has to be case sensitive and unique against a blacklist. I know this sounds like a UUID but I have a specific requirement that prevents me from utilizing them
some characters are disallowed, i.e. I, l and 1 are lookalikes, and O and 0 as well
My initial implementation is solid and solves the task but performs poorly. And by poorly I mean it is doomed to be slower and slower every day.
This is my current implementation I want to optimize:
private function uuid()
{
$chars = 'ABCDEFGHJKLMNPQRSTVUWXYZabcdefghijkmnopqrstvuwxyz23456789';
$uuid = null;
while (true) {
$uuid = substr(str_shuffle($chars), 0, 8);
if (null === DB::table('codes')->select('id')->whereRaw('BINARY uuid = ?', [$uuid])->first())) {
break;
}
}
return $uuid;
}
Please spare me the critique, we live in an agile world and this implementation is functional and is quick to code.
With a small set of data it works beautifully. However if I have 10 million entries in the blacklist and try to create 1000 more it fails flat as it takes 30+ minutes.
A real use case would be to have 10+ million entries in the DB and to attempt to create 20 thousand new unique codes.
I was thinking of pre-seeding all allowed values but this would be insane:
(24+24+8)^8 = 9.6717312e+13
It would be great if the community can point me in the right direction.
Best,
Nikola
Two options:
Just use a hash of something unique, and truncate so it fits in the bandwidth of your identifier. Hashes sometimes collide, so you will still need to check the database and retry if a code is already in use.
s = "This is a string that uniquely identifies voucher #1. Blah blah."
h = hash(s)
guid = truncate(hash)
Generate five of the digits from an incrementing counter and three randomly. A thief will have a worse than 1 in 140,000 chance of guessing a code, depending on your character set.
u = Db.GetIncrementingCounter()
p = Random.GetCharacters(3)
guid = u + p
I ended up modifying the approach: instead of checking for uuid existence on every loop, e.g. 50K DB checks, I now split the generated codes into multiple chunks of 1000 codes and issue an INSERT IGNORE batch query within a transaction.
If the affected rows are as many as the items (1000 in this case) I know there wasn't a collision and I can commit the transaction. Otherwise I need to rollback the chunk and generate another 1000 codes.
Here is the problem, where I need to transform an ID (defined as a long integer) to a smaller alfanumeric identifier. The details are the following:
Each individual on the problem as an unique ID, a long integer of size 13 (something like 123123412341234).
I need to generate a smaller representation of this unique ID, a alfanumeric string, something like A1CB3X. The problem is that 5 or 6 character length will not be enough to represent such a large integer.
The new ID (eg A1CB3X) should be valid in a context where we know that only a small number of individuals are present (less than 500). The new ID should be unique within that small set of individuals.
The new ID (eg A1CB3X) should be the result of a calculation made over the original ID. This means that taking the original ID elsewhere and applying the same calculation, we should get the same new ID (eg A1CB3X).
This calculation should occur when the individual is added to the set, meaning that not all individuals belonging to that set will be know at that time.
Any directions on how to solve such a problem?
Assuming that you don't need a formula that goes in both directions (which is impossible if you are reducing a 13-digit number to a 5 or 6-character alphanum string):
If you can have up to 6 alphanumeric characters that gives you 366 = 2,176,782,336 possibilities, assuming only numbers and uppercase letters.
To map your larger 13-digit number onto this space, you can take a modulo of some prime number slightly smaller than that, for example 2,176,782,317, the encode it with base-36 encoding.
alphanum_id = base36encode(longnumber_id % 2176782317)
For a set of 500, this gives you a
2176782317P500 / 2176782317500 chance of a collision
(P is permutation)
Best option is to change the base to 62 using case sensitive characters
If you want it to be shorter, you can add unicode characters. See below.
Here is javascript code for you: https://jsfiddle.net/vewmdt85/1/
function compress(n) {
var symbols = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyzÀÁÂÃÄÅÆÇÈÉÊËÌÍÎÏÐÑÒÓÔÕÖØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïð'.split('');
var d = n;
var compressed = '';
while (d >= 1) {
compressed = symbols[(d - (symbols.length * Math.floor(d / symbols.length)))] + compressed;
d = Math.floor(d / symbols.length);
}
return compressed;
}
$('input').keyup(function() {
$('span').html(compress($(this).val()))
})
$('span').html(compress($('input').val()))
How about using some base-X conversion, for example 123123412341234 becomes 17N644R7CI in base-36 and 9999999999999 becomes 3JLXPT2PR?
If you need a mapping that works both directions, you can simply go for a larger base.
Meaning: using base 16, you can reduce 1 to 16 to a single character.
So, base36 is the "maximum" that allows for shorter strings (when 1-1 mapping is required)!
Trying to generate random digits with SecureRandom class of rails. Can we create a random number with SecureRandom.hex which includes only digits and no alphabets.
For example:
Instead of
SecureRandom.hex(4)
=> "95bf7267"
It should give
SecureRandom.hex(4)
=> "95237267"
Check out the api for SecureRandom: http://rails.rubyonrails.org/classes/ActiveSupport/SecureRandom.html
I believe you're looking for a different method: #random_number.
SecureRandom.random_number(a_big_number)
Since #hex returns a hexadecimal number, it would be unusual to ask for a random result that contained only numerical characters.
For basic use cases, it's simple enough to use #rand.
rand(9999)
Edited:
I'm not aware of a library that generates a random number of specified length, but it seems simple enough to write one. Here's my pass at it:
def rand_by_length(length)
rand((9.to_s * length).to_i).to_s.center(length, rand(9).to_s).to_i
end
The method #rand_by_length takes an integer specifying length as a param and tries to generate a random number of max digits based on the length. String#center is used to pad the missing numbers with random number characters. Worst case calls #rand for each digit of specified length. That may serve your need.
Numeric id's are good because they are easier to read over the phone (no c for charlie).
Try this
length = 20
id = (SecureRandom.random_number * (10**length)).round.to_s # => "98075825200269950976"
and for bonus points break it up for easier reading
id.split(//).each_slice(4).to_a.map(&:join).join('-') # => "9807-5825-2002-6995-0976"
This will create a number of the desired length.
length = 11
rand(10**length..(10**length+1)-1).to_s
length = 4
[*'0'..'9'].sample(length).join
as simple as that :)
What is the right way to split a string into words ?
(string doesn't contain any spaces or punctuation marks)
For example: "stringintowords" -> "String Into Words"
Could you please advise what algorithm should be used here ?
! Update: For those who think this question is just for curiosity. This algorithm could be used to camеlcase domain names ("sportandfishing .com" -> "SportAndFishing .com") and this algo is currently used by aboutus dot org to do this conversion dynamically.
Let's assume that you have a function isWord(w), which checks if w is a word using a dictionary. Let's for simplicity also assume for now that you only want to know whether for some word w such a splitting is possible. This can be easily done with dynamic programming.
Let S[1..length(w)] be a table with Boolean entries. S[i] is true if the word w[1..i] can be split. Then set S[1] = isWord(w[1]) and for i=2 to length(w) calculate
S[i] = (isWord[w[1..i] or for any j in {2..i}: S[j-1] and isWord[j..i]).
This takes O(length(w)^2) time, if dictionary queries are constant time. To actually find the splitting, just store the winning split in each S[i] that is set to true. This can also be adapted to enumerate all solution by storing all such splits.
As mentioned by many people here, this is a standard, easy dynamic programming problem: the best solution is given by Falk Hüffner. Additional info though:
(a) you should consider implementing isWord with a trie, which will save you a lot of time if you use properly (that is by incrementally testing for words).
(b) typing "segmentation dynamic programming" yields a score of more detail answers, from university level lectures with pseudo-code algorithm, such as this lecture at Duke's (which even goes so far as to provide a simple probabilistic approach to deal with what to do when you have words that won't be contained in any dictionary).
There should be a fair bit in the academic literature on this. The key words you want to search for are word segmentation. This paper looks promising, for example.
In general, you'll probably want to learn about markov models and the viterbi algorithm. The latter is a dynamic programming algorithm that may allow you to find plausible segmentations for a string without exhaustively testing every possible segmentation. The essential insight here is that if you have n possible segmentations for the first m characters, and you only want to find the most likely segmentation, you don't need to evaluate every one of these against subsequent characters - you only need to continue evaluating the most likely one.
If you want to ensure that you get this right, you'll have to use a dictionary based approach and it'll be horrendously inefficient. You'll also have to expect to receive multiple results from your algorithm.
For example: windowsteamblog (of http://windowsteamblog.com/ fame)
windows team blog
window steam blog
Consider the sheer number of possible splittings for a given string. If you have n characters in the string, there are n-1 possible places to split. For example, for the string cat, you can split before the a and you can split before the t. This results in 4 possible splittings.
You could look at this problem as choosing where you need to split the string. You also need to choose how many splits there will be. So there are Sum(i = 0 to n - 1, n - 1 choose i) possible splittings. By the Binomial Coefficient Theorem, with x and y both being 1, this is equal to pow(2, n-1).
Granted, a lot of this computation rests on common subproblems, so Dynamic Programming might speed up your algorithm. Off the top of my head, computing a boolean matrix M such M[i,j] is true if and only if the substring of your given string from i to j is a word would help out quite a bit. You still have an exponential number of possible segmentations, but you would quickly be able to eliminate a segmentation if an early split did not form a word. A solution would then be a sequence of integers (i0, j0, i1, j1, ...) with the condition that j sub k = i sub (k + 1).
If your goal is correctly camel case URL's, I would sidestep the problem and go for something a little more direct: Get the homepage for the URL, remove any spaces and capitalization from the source HTML, and search for your string. If there is a match, find that section in the original HTML and return it. You'd need an array of NumSpaces that declares how much whitespace occurs in the original string like so:
Needle: isashort
Haystack: This is a short phrase
Preprocessed: thisisashortphrase
NumSpaces : 000011233333444444
And your answer would come from:
location = prepocessed.Search(Needle)
locationInOriginal = location + NumSpaces[location]
originalLength = Needle.length() + NumSpaces[location + needle.length()] - NumSpaces[location]
Haystack.substring(locationInOriginal, originalLength)
Of course, this would break if madduckets.com did not have "Mad Duckets" somewhere on the home page. Alas, that is the price you pay for avoiding an exponential problem.
This can be actually done (to a certain degree) without dictionary. Essentially, this is an unsupervised word segmentation problem. You need to collect a large list of domain names, apply an unsupervised segmentation learning algorithm (e.g. Morfessor) and apply the learned model for new domain names. I'm not sure how well it would work, though (but it would be interesting).
This is basically a variation of a knapsack problem, so what you need is a comprehensive list of words and any of the solutions covered in Wiki.
With fairly-sized dictionary this is going to be insanely resource-intensive and lengthy operation, and you cannot even be sure that this problem will be solved.
Create a list of possible words, sort it from long words to short words.
Check if each entry in the list against the first part of the string. If it equals, remove this and append it at your sentence with a space. Repeat this.
A simple Java solution which has O(n^2) running time.
public class Solution {
// should contain the list of all words, or you can use any other data structure (e.g. a Trie)
private HashSet<String> dictionary;
public String parse(String s) {
return parse(s, new HashMap<String, String>());
}
public String parse(String s, HashMap<String, String> map) {
if (map.containsKey(s)) {
return map.get(s);
}
if (dictionary.contains(s)) {
return s;
}
for (int left = 1; left < s.length(); left++) {
String leftSub = s.substring(0, left);
if (!dictionary.contains(leftSub)) {
continue;
}
String rightSub = s.substring(left);
String rightParsed = parse(rightSub, map);
if (rightParsed != null) {
String parsed = leftSub + " " + rightParsed;
map.put(s, parsed);
return parsed;
}
}
map.put(s, null);
return null;
}
}
I was looking at the problem and thought maybe I could share how I did it.
It's a little too hard to explain my algorithm in words so maybe I could share my optimized solution in pseudocode:
string mainword = "stringintowords";
array substrings = get_all_substrings(mainword);
/** this way, one does not check the dictionary to check for word validity
* on every substring; It would only be queried once and for all,
* eliminating multiple travels to the data storage
*/
string query = "select word from dictionary where word in " + substrings;
array validwords = execute(query).getArray();
validwords = validwords.sort(length, desc);
array segments = [];
while(mainword != ""){
for(x = 0; x < validwords.length; x++){
if(mainword.startswith(validwords[x])) {
segments.push(validwords[x]);
mainword = mainword.remove(v);
x = 0;
}
}
/**
* remove the first character if any of valid words do not match, then start again
* you may need to add the first character to the result if you want to
*/
mainword = mainword.substring(1);
}
string result = segments.join(" ");
I have a string, and another text file which contains a list of strings.
We call 2 strings "brotherhood strings" when they're exactly the same after sorting alphabetically.
For example, "abc" and "cba" will be sorted into "abc" and "abc", so the original two are brotherhood. But "abc" and "aaa" are not.
So, is there an efficient way to pick out all brotherhood strings from the text file, according to the one string provided?
For example, we have "abc" and a text file which writes like this:
abc
cba
acb
lalala
then "abc", "cba", "acb" are the answers.
Of course, "sort & compare" is a nice try, but by "efficient", i mean if there is a way, we can determine a candidate string is or not brotherhood of the original one after one pass processing.
This is the most efficient way, i think. After all, you can not tell out the answer without even reading candidate strings. For sorting, most of the time, we need to do more than 1 pass to the candidate string. So, hash table might be a good solution, but i've no idea what hash function to choose.
Most efficient algorithm I can think of:
Set up a hash table for the original string. Let each letter be the key, and the number of times the letter appears in the string be the value. Call this hash table inputStringTable
Parse the input string, and each time you see a character, increment the value of the hash entry by one
for each string in the file
create a new hash table. Call this one brotherStringTable.
for each character in the string, add one to a new hash table. If brotherStringTable[character] > inputStringTable[character], this string is not a brother (one character shows up too many times)
once string is parsed, compare each inputStringTable value with the corresponding brotherStringTable value. If one is different, then this string is not a brother string. If all match, then the string is a brother string.
This will be O(nk), where n is the length of the input string (any strings longer than the input string can be discarded immediately) and k is the number of strings in the file. Any sort based algorithm will be O(nk lg n), so in certain cases, this algorithm is faster than a sort based algorithm.
Sorting each string, then comparing it, works out to something like O(N*(k+log S)), where N is the number of strings, k is the search key length, and S is the average string length.
It seems like counting the occurrences of each character might be a possible way to go here (assuming the strings are of a reasonable length). That gives you O(k+N*S). Whether that's actually faster than the sort & compare is obviously going to depend on the values of k, N, and S.
I think that in practice, the cache-thrashing effect of re-writing all the strings in the sorting case will kill performance, compared to any algorithm that doesn't modify the strings...
iterate, sort, compare. that shouldn't be too hard, right?
Let's assume your alphabet is from 'a' to 'z' and you can index an array based on the characters. Then, for each element in a 26 element array, you store the number of times that letter appears in the input string.
Then you go through the set of strings you're searching, and iterate through the characters in each string. You can decrement the count associated with each letter in (a copy of) the array of counts from the key string.
If you finish your loop through the candidate string without having to stop, and you have seen the same number of characters as there were in the input string, it's a match.
This allows you to skip the sorts in favor of a constant-time array copy and a single iteration through each string.
EDIT: Upon further reflection, this is effectively sorting the characters of the first string using a bucket sort.
I think what will help you is the test if two strings are anagrams. Here is how you can do it. I am assuming the string can contain 256 ascii characters for now.
#define NUM_ALPHABETS 256
int alphabets[NUM_ALPHABETS];
bool isAnagram(char *src, char *dest) {
len1 = strlen(src);
len2 = strlen(dest);
if (len1 != len2)
return false;
memset(alphabets, 0, sizeof(alphabets));
for (i = 0; i < len1; i++)
alphabets[src[i]]++;
for (i = 0; i < len2; i++) {
alphabets[dest[i]]--;
if (alphabets[dest[i]] < 0)
return false;
}
return true;
}
This will run in O(mn) if you have 'm' strings in the file of average length 'n'
Sort your query string
Iterate through the Collection, doing the following:
Sort current string
Compare against query string
If it matches, this is a "brotherhood" match, save it/index/whatever you want
That's pretty much it. If you're doing lots of searching, presorting all of your collection will make the routine a lot faster (at the cost of extra memory). If you are doing this even more, you could pre-sort and save a dictionary (or some hashed collection) based off the first character, etc, to find matches much faster.
It's fairly obvious that each brotherhood string will have the same histogram of letters as the original. It is trivial to construct such a histogram, and fairly efficient to test whether the input string has the same histogram as the test string ( you have to increment or decrement counters for twice the length of the input string ).
The steps would be:
construct histogram of test string ( zero an array int histogram[128] and increment position for each character in test string )
for each input string
for each character in input string c, test whether histogram[c] is zero. If it is, it is a non-match and restore the histogram.
decrement histogram[c]
to restore the histogram, traverse the input string back to its start incrementing rather than decrementing
At most, it requires two increments/decrements of an array for each character in the input.
The most efficient answer will depend on the contents of the file. Any algorithm we come up with will have complexity proportional to N (number of words in file) and L (average length of the strings) and possibly V (variety in the length of strings)
If this were a real world situation, I would start with KISS and not try to overcomplicate it. Checking the length of the target string is simple but could help avoid lots of nlogn sort operations.
target = sort_characters("target string")
count = 0
foreach (word in inputfile){
if target.len == word.len && target == sort_characters(word){
count++
}
}
I would recommend:
for each string in text file :
compare size with "source string" (size of brotherhood strings should be equal)
compare hashes (CRC or default framework hash should be good)
in case of equity, do a finer compare with string sorted.
It's not the fastest algorithm but it will work for any alphabet/encoding.
Here's another method, which works if you have a relatively small set of possible "letters" in the strings, or good support for large integers. Basically consists of writing a position-independent hash function...
Assign a different prime number for each letter:
prime['a']=2;
prime['b']=3;
prime['c']=5;
Write a function that runs through a string, repeatedly multiplying the prime associated with each letter into a running product
long long key(char *string)
{
long long product=1;
while (*string++) {
product *= prime[*string];
}
return product;
}
This function will return a guaranteed-unique integer for any set of letters, independent of the order that they appear in the string. Once you've got the value for the "key", you can go through the list of strings to match, and perform the same operation.
Time complexity of this is O(N), of course. You can even re-generate the (sorted) search string by factoring the key. The disadvantage, of course, is that the keys do get large pretty quickly if you have a large alphabet.
Here's an implementation. It creates a dict of the letters of the master, and a string version of the same as string comparisons will be done at C++ speed. When creating a dict of the letters in a trial string, it checks against the master dict in order to fail at the first possible moment - if it finds a letter not in the original, or more of that letter than the original, it will fail. You could replace the strings with integer-based hashes (as per one answer regarding base 26) if that proves quicker. Currently the hash for comparison looks like a3c2b1 for abacca.
This should work out O(N log( min(M,K) )) for N strings of length M and a reference string of length K, and requires the minimum number of lookups of the trial string.
master = "abc"
wordset = "def cba accb aepojpaohge abd bac ajghe aegage abc".split()
def dictmaster(str):
charmap = {}
for char in str:
if char not in charmap:
charmap[char]=1
else:
charmap[char] += 1
return charmap
def dicttrial(str,mastermap):
trialmap = {}
for char in str:
if char in mastermap:
# check if this means there are more incidences
# than in the master
if char not in trialmap:
trialmap[char]=1
else:
trialmap[char] += 1
else:
return False
return trialmap
def dicttostring(hash):
if hash==False:
return False
str = ""
for char in hash:
str += char + `hash[char]`
return str
def testtrial(str,master,mastermap,masterhashstring):
if len(master) != len(str):
return False
trialhashstring=dicttostring(dicttrial(str,mastermap))
if (trialhashstring==False) or (trialhashstring != masterhashstring):
return False
else:
return True
mastermap = dictmaster(master)
masterhashstring = dicttostring(mastermap)
for word in wordset:
if testtrial(word,master,mastermap,masterhashstring):
print word+"\n"