I'm reading the paper "Concerto: A High Concurrency Key-Value Store with Integrity
".
For memory checking, they said they used AES-based pseudo random function, which has two input and outputs hash.
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My question is: How to implement this AES-based pseudo random function in C or Go? Is this just use AES to encrypt？ Someone can give me an example of implementation.
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Other than collision detection and throwing a LinkedList in a hashtable, what are some other ways that a Hash Table can be implemented? Is collision detection the only way to achieve an efficient hash table?
Ultimately a finite sized hash table is going to have collisions, at least any generally programmed one. If your key is type string then the hash table has an infinite number of possible keys, but with a hash table, you have just a finite number of buckets. So fundamentally there has to be collisions. If you were to implement a hash table where it ignores collisions, then you would have a very strange, indeterministic data structure that would appear to remove elements at random.
Now, the data structure used on the backend doesn't have to be a linked list. You could implement it as a red-black tree and get log(n) performance out of a collision. You should checkout the article 5 Myths About Hash Tables and also this Stack Overflow question about HashMaps vs Maps.
Now, if you know something about you key type, say the key is a 2 character long string, then there are only a finite number of possible keys, you can then proceed to create a "hash" function that converts the key to a relatively small integer, you could create a look-up table that is guaranteed to not have collisions.
It is important to note that a well-implemented hash table will not suffer very much from collisions. There are bigger problems in the world like world hunger (or even how to implement an efficient hash function) than the computer having to traverse three nodes in a linked list once every 5 days.
Other than collision detection and throwing a LinkedList in a hashtable, what are some other ways that a Hash Table can be implemented?
Other ways include:
having another container type linked from the nodes where elements have collided, such as a balanced binary tree or vector/array
GCC's hash table underpinning std::unordered_X uses a single singly-linked list of values, and a contiguous array of buckets container iterators into the list; that's got some great characteristics including optimal iteration speed regardless of the current load_factor()
using open addressing / closed hashing, which - when an insert/find/erase finds another key in the bucket it has hashed to, uses some algorithm to find another bucket to look in instead (and so on until it finds the key, a deleted element it can insert over, or an unused bucket); there are a number of options for this kind of "probing", the simplest being a try-the-next-bucket approach, another being quadratic 1, 4, 9, 16..., another the use of alternative hash functions.
perfect hash functions (below)
Is collision detection the only way to achieve an efficient hash table?
sometimes it's possible to find a perfect hash function that won't have collisions, but that's generally only true for very limited input sets, whether due to the nature of the inputs (e.g. month and year of birth of living people only has order-of a thousand possible values), or because a small number are known at compile time (e.g. a set of 200 keywords for a compiler).
I am interested in a modification of the usual idea of random number generators. That is, typical generators generate long strings of reasonably independent, uniformly distributed numbers from that space. This is intended to be used with one seed, repeatedly.
However, for my purpose, I want a way of generating a "random number" from another number (actually from a grid of integers) in a way that is "independent," in the sense that knowing the outputs for nearby points don't help you predict the value at your point.
In practice, using traditional random number generators works reasonably well, but I'd be interested in any work that was actually done for this purpose.
It sounds like you are looking for a cryptographic hash function.
The ideal cryptographic hash function has four main properties:
it is easy to compute the hash value for any given message
it is infeasible to generate a message that has a given hash
it is infeasible to modify a message without changing the hash
it is infeasible to find two different messages with the same hash
Some commonly used hash functions are SHA-1 and SHA-512. One called MD5 is still being used even though it has been shown to be insecure.
I need to create small-mid sized static hash tables from it. Typically, those will have 5-100 entries. When the hash table is created, all keys hashes are known up-front (i.e. the keys are already hashes.) Currently, I create a HashMap, which is I sort the keys so I get O(log n) lookup which 3-5 lookups on average for the sizes I care. Wikipedia claims that a simple hash table with chaining will result in 3 lookups on average for a full table, so that's not yet worth the trouble for me (i.e. taking hash%n as the first entry and doing the chaining.) Given that I know all hashes up-front, it seems to be that there should be an easy way to get a fast, static perfect hash -- but I couldn't find a good pointer how. I.e. amortized O(1) access with no (little?) additional overhead. How should I implement such a static table?
Memory usage is important, so the less I need to store, the better.
Edit: Notice that it's fine if I have have to resolve one collision or so manually. I.e. if I could do some chaining which on average has direct access and worst-case 3 indirections for instance, that's fine. It's not that I need a perfect hash.
For c or c++ you can use gperf
GNU gperf is a perfect hash function generator. For a given list of strings, it produces a hash function and hash table, in form of C or C++ code, for looking up a value depending on the input string. The hash function is perfect, which means that the hash table has no collisions, and the hash table lookup needs a single string comparison only.
GNU gperf is highly customizable. There are options for generating C or C++ code, for emitting switch statements or nested ifs instead of a hash table, and for tuning the algorithm employed by gperf.
Small hashes are also possible in C without an external lib using the pre-processor, for example:
swich (hash_string(*p))
{
case HASH_S16("test"):
...
break;
case HASH_S256("An example with a long text!!!!!!!!!!!!!!!!!"):
...
break;
}
Have a look for the code # http://www.heeden.nl/statichashc.htm
You can use Sux4j to generate a minimal perfect hash in Java or C++. (I'm not sure you are using Java, but you mentioned HashMap, so I'm assuming.) For C, you can use the cmph library.
I've been reading a lot about Hash Tables and how to implement on in C and I think I have almost all the concepts in my head so I can start to code my own, I just have a couple of questions that I have yet to properly understand.
As a reference, I've been reading this:
http://eternallyconfuzzled.com/jsw_home.aspx
1) As I've read on the site above, a power of two or a prime number is recommended for the Hash Table size. This is basically an array and an array has a fixed size so I can quickly look up for the value I'm looking for. I can't declare a small array if I have a large input as it won't fit and I can't declare a very large array if my input data is not that large cause it's wasted memory.
What is the optimum size for the Hash Table? What should I base my decision on?
2) Also, on that site, there's a couple of hashing functions which I have yet to read them all. It also states that it's always best to use a good known algorithm and to roll my own. And I might do just that, I'll pick one from that site and test it out on my code and see if it minimizes collisions based on my input data.
What's bugging me is how I control the hash range? The hash can't return and integer larger than the Hash Table size or we'll have a serious problem. How do I deal with this?
1) What you are referring to is the load factor of the hash table - the percentage of buckets that are expected to be filled. Wikipedia has this to say:
With a good hash function, the average
lookup cost is nearly constant as the
load factor increases from 0 up to 0.7
or so. Beyond that point, the
probability of collisions and the cost
of handling them increases.
I believe the Java implementation (and probably others) resizes periodically to keep the load factor within an acceptable range.
2) Just use the modulo operator (%) to keep the bucket index legal. The second operator should be the size of your bucket array.
Pick a small size for your hash table. As you add stuff to your table, check to see what percentage of the table is being used; when it is greater than 70% full, make the table bigger. This also holds true as you remove elements-- make the table smaller when it is less than 60% full, for instance. Wikipedia has a good description of some strategies for dynamic resizing, but that's the general idea.
I only say this because you seem to have known input data:
If you know the rough order of magnitude of the amount of data you will be storing in the hash table, it's generally good enough to just create a table about that big. (You shouldn't worry about whether everything will fit. Instead, the right thing to think about is how many collisions you will have and how you will handle them.)
As for the right hash function, it's possible that the structure of your input will suggest which one will be correct. For instance, what aspects of your input are likely to be evenly distributed?
What is the difference between Obfuscation, Hashing, and Encryption?
Here is my understanding:
Hashing is a one-way algorithm; cannot be reversed
Obfuscation is similar to encryption but doesn't require any "secret" to understand (ROT13 is one example)
Encryption is reversible but a "secret" is required to do so
Hashing is a technique of creating semi-unique keys based on larger pieces of data. In a given hash you will eventually have "collisions" (e.g. two different pieces of data calculating to the same hash value) and when you do, you typically create a larger hash key size.
obfuscation generally involves trying to remove helpful clues (i.e. meaningful variable/function names), removing whitespace to make things hard to read, and generally doing things in convoluted ways to make following what's going on difficult. It provides no serious level of security like "true" encryption would.
Encryption can follow several models, one of which is the "secret" method, called private key encryption where both parties have a secret key. Public key encryption uses a shared one-way key to encrypt and a private recipient key to decrypt. With public key, only the recipient needs to have the secret.
That's a high level explanation. I'll try to refine them:
Hashing - in a perfect world, it's a random oracle. For the same input X, you always recieve the same output Y, that is in NO WAY related to X. This is mathematically impossible (or at least unproven to be possible). The closest we get is trapdoor functions. H(X) = Y for with H-1(Y) = X is so difficult to do you're better off trying to brute force a Z such that H(Z) = Y
Obfuscation (my opinion) - Any function f, such that f(a) = b where you rely on f being secret. F may be a hash function, but the "obfuscation" part implies security through obscurity. If you never saw ROT13 before, it'd be obfuscation
Encryption - Ek(X) = Y, Dl(Y) = X where E is known to everyone. k and l are keys, they may be the same (in symmetric, they are the same). Y is the ciphertext, X is the plaintext.
A hash is a one way algorithm used to compare an input with a reference without compromising the reference.
It is commonly used in logins to compare passwords and you can also find it on your reciepe if you shop using credit-card. There you will find your credit-card-number with some numbers hidden, this way you can prove with high propability that your card was used to buy the stuff while someone searching through your garbage won't be able to find the number of your card.
A very naive and simple hash is "The first 3 letters of a string".
That means the hash of "abcdefg" will be "abc". This function can obviously not be reversed which is the entire purpose of a hash. However, note that "abcxyz" will have exactly the same hash, this is called a collision. So again: a hash only proves with a certain propability that the two compared values are the same.
Another very naive and simple hash is the 5-modulus of a number, here you will see that 6,11,16 etc.. will all have the same hash: 1.
Modern hash-algorithms are designed to keep the number of collisions as low as possible but they can never be completly avoided. A rule of thumb is: the longer your hash is, the less collisions it has.
Obfuscation in cryptography is encoding the input data before it is hashed or encrypted.
This makes brute force attacks less feasible, as it gets harder to determine the correct cleartext.
That's not a bad high-level description. Here are some additional considerations:
Hashing typically reduces a large amount of data to a much smaller size. This is useful for verifying the contents of a file without having to have two copies to compare, for example.
Encryption involves storing some secret data, and the security of the secret data depends on keeping a separate "key" safe from the bad guys.
Obfuscation is hiding some information without a separate key (or with a fixed key). In this case, keeping the method a secret is how you keep the data safe.
From this, you can see how a hash algorithm might be useful for digital signatures and content validation, how encryption is used to secure your files and network connections, and why obfuscation is used for Digital Rights Management.
This is how I've always looked at it.
Hashing is deriving a value from
another, using a set algorithm. Depending on the algo used, this may be one way, may not be.
Obfuscating is making something
harder to read by symbol
replacement.
Encryption is like hashing, except the value is dependent on another value you provide the algorithm.
A brief answer:
Hashing - creating a check field on some data (to detect when data is modified). This is a one way function and the original data cannot be derived from the hash. Typical standards for this are SHA-1, SHA256 etc.
Obfuscation - modify your data/code to confuse anyone else (no real protection). This may or may not loose some of the original data. There are no real standards for this.
Encryption - using a key to transform data so that only those with the correct key can understand it. The encrypted data can be decrypted to obtain the original data. Typical standards are DES, TDES, AES, RSA etc.
All fine, except obfuscation is not really similar to encryption - sometimes it doesn't even involve ciphers as simple as ROT13.
Hashing is one-way task of creating one value from another. The algorithm should try to create a value that is as short and as unique as possible.
obfuscation is making something unreadable without changing semantics. It involves value transformation, removing whitespace, etc. Some forms of obfuscation can also be one-way,so it's impossible to get the starting value
encryption is two-way, and there's always some decryption working the other way around.
So, yes, you are mostly correct.
Obfuscation is hiding or making something harder to understand.
Hashing takes an input, runs it through a function, and generates an output that can be a reference to the input. It is not necessarily unique, a function can generate the same output for different inputs.
Encryption transforms the input into an output in a unique manner. There is a one-to-one correlation so there is no potential loss of data or confusion - the output can always be transformed back to the input with no ambiguity.
Obfuscation is merely making something harder to understand by intruducing techniques to confuse someone. Code obfuscators usually do this by renaming things to remove anything meaningful from variable or method names. It's not similar to encryption in that nothing has to be decrypted to be used.
Typically, the difference between hashing and encryption is that hashing generally just employs a formula to translate the data into another form where encryption uses a formula requiring key(s) to encrypt/decrypt. Examples would be base 64 encoding being a hash algorithm where md5 being an encryption algorithm. Anyone can unhash base64 encoded data, but you can't unencrypt md5 encrypted data without a key.