Hashing is so cool. It’s lightning-fast at speed and requires only a small amount of memory unlike a large amount of disk space and CPU storage.
You need to make a simple calculation, dive into your memory location, and then a short in-memory sequential search with an incredibly fast search and you make your way to a protected outcome.
What is hashing?
Hashing is one important way to enable data security during the process of message transmission when you intend to send the message for a particular recipient only.
Whether it is about storing passwords or in computer graphics using hash-codes, hashing is the ultimate solution.
You can compress your data using a chosen hash algorithm of a fixed size. These are highly suitable when you want to identify or compare a file or database.
Instead of comparing the data in its original form, hashcodes make it much easier for computers to compare the hash values.
Hashing in data structure enables the security of the data transmission and protects the data from any damages. And as a result, you get a secure data which is a better revenue-generating opportunity.
Understanding Hashing with an example:
Imagine a hypothetical situation. Suppose, you want to send a file or a message to someone, and it should reach the recipient in the same format. How will you be able to do this?
One way out could be sending it multiple times and verifying that it did not face any damage. But, what if the message or file you’re sending happens to be too long? It is of no logic to verify every single letter of the message. Isn’t it?
So, what you can do is, you can hash the value with which you want to compare the input and then compare both the input hash and hashed value to compare.
Because hashing is a one way process, you can hash the message or text. And then the hashed text becomes irreversible, thus helping your concerned file or message to reach the recipient in the same format.
Well, this is how Hashing comes into play.
What is a Hashing Function?
A hash function is the core of hashing. Precisely, behind every successful hash algorithm, there is a great hash function.
So, then what is a hashing function?
“A hash function is an algebraic function which converts a given input into a compressed numeric value, i.e. a hash or hash value.”
A hash function is a processing unit that takes in data of a random length and provides you with the output of a fixed length, i.e. the hash value or hashed text.
Properties Of A Useful Hash Function:
Different classes of hash functions share the same four features. Here are the four must-have qualities of a useful hash function:
1) Computationally Efficient
If a hashing algorithm will take several minutes to process a hash function and receive the output, it won’t be convenient for you.
So, in the first place, hash functions must be computationally efficient i.e., it should be possible for hashing algorithms to compute a hash function in a short period.
If you put in the same input a million times in a row, a hash function must give the same output a million times over. Because if a hash function produce different outputs each time, the hash function would turn useless.
So, a hash function must be deterministic and always give the same result for any given input.
3) Pre- Image Resistant
The output of a hash function must not reveal any information about the input. This is known as pre-image resistance.
Hash algorithms receive any input like letters, numbers, words or punctuation marks. However, a hash function will always bring out an alphanumeric output of a fixed-length.
Suppose, if an input always produces an output 1.5 times its length, then the hash function can give away valuable information to the hackers.
Because when the hackers saw an output of maybe 36 characters, they would quickly figure out that the input was of 24 characters. (As 36=24*1.5)
The Hash function should give fixed-length output no matter how big the input text is.
So, a useful hash function must cover up any clue about what the input may look like, and it should make it impossible to determine what the input could have been.
4) Collision Resistant
The final property of hash functions is known as collision resistance. In simple words, it means that it should be practically impossible to find two different inputs that give out output with the same value to avoid risks.
What are the common Hashing Functions?
Let’s go through the essential points about various hashing functions:
1) Division Remainder hashing functions:
This uses the table size as the divisor. It computes the hash value from the key using the % operator.
Table size, which is a power of 2 like 32 and 1024 leads to more collisions and hence you should avoid them.
The powers of 10 are not suitable for table sizes when the keys rely on decimal integers.
Prime numbers which are not close to powers of 2 are known to be better table size values.
2) Digit or Character Extraction hashing function:
This works on the basis of distribution of digits or characters in the key.
This hash function extracts the more evenly distributed digit positions and uses them for hashing purposes.
It is swift, but digits or characters distribution in keys may not be very even.
3) Folding hashing function:
Folding is handy if you have large keys. It is fast and straightforward, especially with bit patterns.
A significant advantage of folding is its ability to transform non-integer keys into integer values.
It involves the procedure of splitting keys into two or more parts and then to combine the pieces and form the hash addresses.
Example: To map the key 26456715 to a range between 0 and 9999, you can: split the number into two as 2645 and 6715, and then add these two to obtain 9360 as the hash value.
4) Radix Conversion hashing function:
Radix function transforms a key into another number base, and you thus obtain the hash value.
It typically uses number base other than base 10 and base 2 to calculate the hash addresses.
5) Mid-Square hashing function:
Mid-Square function works well if the keys do not contain a lot of leading zeros.
In this hash function, the key is squared, and we take the middle part of the result obtained as the hash value.
Example: To map the key 3111 into a hash table of size 1000, we square (3111)²= 9678321 and extract 783(the middle part of the result obtained) as the hash value.
6) Digit rearrangement method:
These include the message-digesting hash functions MD2, MD4, and MD5.
You can use this for hashing the digital signatures into a shorter value called a message-digest.
Proceeding ahead, we’ll talk more about the popular hash functions below.
What are some of the Modern hashing algorithms?
Some hashing algorithms that you may come across are:
The MD family of hashing algorithms comprises of hash functions MD2, MD4, MD5 and MD6.
MD-5 is a widely used hash algorithm . However, it has flaws of being prone to collisions. But MD-5 does not fail regarding properties of pre-images.
MD-5 faced collision attacks in 1996 and its security risk recommends it lowly.
CRC32- Cyclic Redundancy Check
CRC32 is a hashing algorithm used to check if a compressed file undergoes any damage while you tranfer it.
You particularly use them in industrial networks, where real cryptographic hashes are a considerable choice.
SHA or Secure Hashing Algorithm
Secure Hashing Algorithm is a family of hash functions which was published as a U.S. Federal Information Processing Standard (FIPS) by the National Institute of Standards and Technology (NIST).
As of now, SHA defines three algorithms. They are:
SHA-1 is a 160-bit-sized hash function which resembles the earlier MD-5 algorithm. It was designed to be part of the Digital Signature Algorithm by the National Security Agency (NSA).
SHA-1 faced specific cryptographic weaknesses, and the standard was not used more often after 2010.
SHA-2 is a family consisting of two similar hash functions known as SHA-256 and SHA-512. It has different block sizes. SHA-256 involves using 32-bit words, whereas SHA-512 uses 64-bit words.
SHA-3 is currently undefined hashing algorithm and is something the world is still working on with the exact useful parameters.
Tiger is a 192-bit-sized cryptographic hashing algorithm which usually truncates its output to form 160-bit and 128-bit hash functions.
What are the benefits of Hashing in data structure?
The main benefit of hash tables in comparison to any other table data structures is that of speed, especially when there are a large number of entries. The main benefits you can derive by performing hashing are:
- Hashing in data structure helps to retrieve data in a more reliable and flexible method than any other data structure.
- It is comparatively faster than searching arrays and lists.
- With hashing in data structure, you can control the space by picking the speed of retrieval. You can also control the speed by picking the amount of space for the hash table instead of the former case.
What are the applications of Hashing?
The hashcode contains a value which is not permanent. The goal of hash codes is to help in efficient lookup and insertion in data collections on the basis of a hash table.
Hashing is of great use and serves a variety of purposes. Let’s talk about some of its applications below:
1) Message Digest:
Cryptographic hashcodes and hashing algorithms are useful in message digesting, i.e., they produce an output from which reaching the input is next to impossible.
For eg., Suppose you need to store files on any of the available cloud services. You have to ensure that the files you store do not face any damage by a third party.
So, how do you do it? You do it by hashing in data structure.
Now, at the time of downloading the files, you compute the hash again using hashcode and match it with the previously computed hash.
Thus, you can know whether your files were damaged or not. If there’s any tampering with the file, the hash value now will change.
2) Password Verification:
You can commonly use the hashing algorithms and hashcodes for the purpose of password verification.
Let’s suppose you are using any online website. It requires your user login. So, you enter your email id and password to authenticate that this account belongs to you.
When you enter the password, a hash of the password is measured and sent to the server to verify it. The passwords that are stored on the server are the computed hash values of the password you feed into.
The input that you provide undergoes hashing in data structure, and a hashed value of output gets stored in the database.
So, this is done using hash functions and hashcodes to make sure that a password is not easily detected when being sent from client to server and helps in password protection.
3) Rabin Karp hashing algorithm:
This is one of the most popular applications of hashing. It is a string-searching algorithm which uses hashcodes to find any one set of patterns in a string. This is practically used in detecting plagiarism.
4) Linking File name and path together:
When you are trying to move through files on your local system, you observe two important file components, i.e. file_name and file_path.
Hashcodes in hash tables are used in these cases to maintain the harmony between file_name and file_path.
5) Data Structures(Programming Languages):
Programming languages have a hash table on the basis of Data Structures, where the basic idea is to create a key-value pair. C++, java, python etc. uses hash keys.
6) Compiler Operation:
We cannot process the keywords of a programming language the same as other identifiers. But a hash table using hash codes can differentiate between the keywords of a programming language and other identifiers.
Alongwith these applications, hashing provides constant-time search, inserting and deleting operations in particular too. This is why hash functions are one of the most useful data structures.
In conclusion, hashing is a useful tool which verifies correct copying of files between two resources. It can also check if the files are identical without opening and comparing them.
Primarily you can use hashing in data structure for retrieval of items in a database. This is because it becomes quicker to find the item using short hashed key than to locate it using the original value.
Along with faster data retrieval, you can also use hashing in data structure to encrypt and decrypt digital signatures using hashcodes.
Hashing ensures that the messages during transmission don’t undergo tampering and thus plays a vital role in the data security system.
You can also make significant profits through data monetization by coding your algorithm and utilizing arrays for node storage.
So, when are you enabling data security using hashing?
Or have you done so?
Tell me in the comments.