The Cryptographic Cauldron: How Hashing Brews Stronger Security
May 27, 2025
8 min read
Table Of Content
- What is Hashing in Data Structure?
- How Does Hashing in Data Structure Work?
- Hashing in Data Structure: How to Reduce Collision?
Data is a critical part of the modern day world as it is helping in the efficient functioning of worldwide operations. Since terabytes of data are generated every day, it is important to have a mechanism that streamlines its storage and ensures effective usage.
Hashing in data structure is a technique that makes the data management process easy. It helps data to be assigned and stored in the right location. The aim of hashing is to make data management operations straightforward and faster when the datasets grow.
So, this article will explain hashing in data structure and its benefits and limitations.
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What is Hashing in Data Structure?
Hashing in data structure is a technique that is used to efficiently store data/key to a specific index in a table or array. In hashing, a key or character string is transformed into another value. This value has a fixed length, smaller or variable value that is easy to locate.
The hash data structure refers to a tabular data model that stores data. Within the table, data is preserved in an array format. Unlike a simple array, each data is assigned to a unique index number. When users want to access data, they should search for it with the index. It makes the data retrieval process quicker.
The hash architecture utilizes an array as a storage and a hashing algorithm to build an index that helps to locate data.
For instance, here is a key value, Clara, and it includes content that is a phone number. Next, the key value should be passed through the hash function to generate an index for the key. So, it will look like –
Hash(key) = index;
After proceeding with the key through the function, the index will be generated. The index is also known as hash code or hash value.
Hash(Clara) = 2104;
Now, the key has been added to Clara, and it has an index of 2104.
The entire process of hashing in data structure works with three major components. These are the key, hash function, and hash table.
Key
The key is any text or number that should be provided as input to the hash function. Each key is the starting point for generating a hash code. This key should maintain consistency throughout the procedure so that every time the same key generates the same output.
Hash Function
The function transforms each key to a numeric output that is known as the hash code or hash value. A well-structured hash function always reduces collisions and distributes the keys well across the table.
The simple version of the hash function is used as a modulo arithmetic format that is key % tableSize. However, the advanced hash functions use encryption-based or complex arithmetic formulas.
Hash Table
The data structure hash table is a structure-like array that encompasses the pace of each hash code or index. Now, the size of the table depends upon the data volume and targeted efficiency. Every index points to the location where the data is stored.
Also, in other words, the hash tables contain key-value pairs. As the key values are converted to indexes, it becomes pretty easy to search for data.
How Does Hashing in Data Structure Work?
Here is a simple step-by-step guidance on how hashing in data structure works –
- Get the key: First, it requires a key that might be a string, product code, or student ID. It should be stored.
- Pass the key through the hash function: The key should be passed through a hash function in this step. It helps to calculate a number, which is the hash code, to secure a position for the key in the table.
- Get the hash code: Now, the function has generated the hash code.
- Assign data within the hash table: At this step, data is assigned to the right place within the table. Also, the hash code will be used as an index from this step.
- Managing collisions (if required): A collision will occur if two different keys generate the same code. The system will store the same codes without overwriting them. They store similar codes by linking them in a list or storing them in the next free slot.
Here is a simple example of hashing in data structure –
Suppose there are five numbers: 33, 41, 49, 52, and 60. It has a simple hash function h(key) = key mod 6. So, the table below shows each keymap into a slot of a hash table size 6. That means it has indexed from 0 to 5.
| Key | H (key) = key mod 6 | Output Index |
|---|---|---|
| 33 | 33%6 = 3 | 3 |
| 41 | 41%6 = 5 | 5 |
| 49 | 49%6 = 1 | 1 |
| 52 | 52%6 = 4 | 4 |
| 60 | 60%6 = 0 | (0) |
So, the data structure hash table indicates that –
- 33 would be placed at the position 3.
- 41 would be placed at the position 5.
- 49 would be placed at the position 1.
- 52 would be placed at the position 4.
- 60 would be placed at the position 0.
Hashing in Data Structure: How to Reduce Collision?
Here are some strategies for how to reduce ‘collisions’ in hashing –
Separate Chaining (Open Hashing/Chained Hashing)
Separate chaining includes the items within a linked list that have the same index. Every slot within the hash table can point towards the individual chain of entries. As the items keep the linked list extending instead of overwriting, it helps eliminate collisions of hashing in data structure.
So, every index keeps a pointer to the linked list. When a collision occurs, the newly entered item either goes to the end or the start of the list.
Open Addressing (Closed Hashing)
This technique keeps all data within the original array. When a collision takes place, the hash table starts examining if there is any empty slot. The table uses a technique named ‘probing’ to search for empty slots. There are three probing techniques a table can use. These are linear probing, quadratic probing, and double hashing.
- Linear probing: In this case, the next slot should be checked linearly until any free position is found. So, the probing should go as index + 1, index + 2, etc.
- Quadratic probing: The slot checking for this probing becomes grown with square values such as 1, 4, 9, 16, 25, and so on.
- Double hashing: It is a technique used to mitigate collisions.
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Types of Hashing in Data Structure
There are four types of hashing in data structure: trivial hashing, double hashing, chained hashing, and closed hashing. The last two types of hashing have already been discussed in the previous section, so, only the trivial and double hashing are added here.
Trivial Hashing
For trivial hashing in data structure, the actual information is directly translated to an index within a hash table. This is also called index mapping. This concept provides the identity of a hash function that translates input data to itself. Here, the data key is consumed as the index within the hash table. The corresponding value is saved within the position.
The trivial hash function is simple to implement and interpret. Moreover, the data retrieval process from the trivial hashing in data structure is simple. However, it has certain restrictions, like the requirement that hash table length should be equal to the number of keys. Thus, this hashing can’t be implemented for small data sets. Also, collisions can’t be handled in this hashing.
Double Hashing
Double hashing in data structure is a collision resolution strategy that is used by hash tables. This technique uses two hash functions to generate two separate hash results for a single key. The first hash function determines the initial hash value and the second hash function determines the step length of the probing sequence. The step length in the probing sequence and the two hash functions reduce the collision rate.
However, the double hashing technique incorporates some disadvantages. As it necessitates the usage of two hashing functions, it requires more computational effort for inserting and searching operations. Also, it’s essential to select well-structured hash algorithms. Otherwise, there would be a chance of an increment in the collision rate.
Hash Function in Data Structure: Some Popular Hashing Techniques
This section will describe some notable hash functions –
Division Method
Suppose there is a hash table of size ‘Q’ to store the key-value pair. So, as per the division method, the hash function will be H(k) = key mod N. Here, N should be larger than Q and be a positive integer. Sometimes, Q might be a substitute for N.
The division method is the same as the previous example of a simple hashing in data structure.
Mid-Square Method
The Mid-square method of hashing in data structure needs two steps to generate a hash value. In this method, the hash function is computed by performing the key’s square. That means it should be (key*key). Then, the code selects some digits from the center of the integer to generate the hash value.
For example, the table size is 100, and the key is 11.
The square of 11 is 121. Here, 2 is the middle digit. So, the index is 2.
Digital Folding Method
This hashing in data structure requires the key to be divided into equal parts. Then, the parts will be summed to get the index. This method becomes helpful, particularly for number segments such as phone numbers.
For instance, there is a segment 53389732. Now, it requires dividing by two digits, such as 53, 38, 97, and 32. So, the sum will be 53 + 38 + 97 + 32 = 220.
Now, let’s assume the table size is 8. Then the index will be –
220 mod 8 = 220%8 = 4.
So, the index is 4.
Multiplication Method
This method requires multiplying the key with a constant that should lie between 0 and 1. Then, the fractional part should be extracted and multiplied by the table size.
For example, the constant = C = 0.5, table size = 20, and key = 11.
The process goes as follows –
11*0.5 = 5.5
The fractional part = 0.5
So, the index will be = 0.5*20 = 10
Benefits of Hashing in Data Structure
Here are the benefits of hashing in data structure –
Password Storage
Hashing in ds is widely used to store passwords securely. Instead of storing passwords with plain text, hashing algorithms are used to convert the passwords into hash values. So, when the users enter their passwords, they are converted to the hash value and directly compared to the hash value that was stored. If these match, the password becomes authenticated. This approach saves the original passwords from being exposed.
Data Integrity
Hashing in data structure enables a method of verification that authenticates the original data. Any bit of change in data will result in a different outcome for the hash value. As a result, the users can easily detect data. This way, it supports data integrity.
Blockchain and Cryptocurrency
In blockchain and cryptocurrency, the hashing plays a crucial role. Each block in a blockchain stores the hash value of the previous block. If the hash value of one block changes, then hash values for other blocks will also get altered. This way, every block manages its unique identity and helps to create a tamper-evident chain of blocks.
Also, hashing helps minors with proof-of-work, through which they validate every transaction in the blockchain.
Data Anonymization
Hashing in data structure enables the anonymization of sensitive data without harming its uniqueness. It helps to transform personally identifiable information (PII) such as names, addresses, ID numbers, and others into a fixed-size hash value. Thus, it becomes helpful for data processing and analysis without revealing sensitive data.
Limitations of Hashing in Data Structure
Here are some limitations of hashing in ds –
- Hash collisions: Though hash functions are designed to reduce collisions, still there are chances of collisions. This happens due to poorly designed hash functions. These lead to potential security issues.
- Irreversibility: As a one-way process, hashing in a data structure never allows retrieval of the original input from the hash value. Though it becomes helpful for sensitive data storage, it creates difficulties for retrieval tasks in case of loss of the original data. Only applying encryption or hashing is not enough to get back the data. Thus, additional mechanisms such as encryption, security data storage, or backup for original data are mandatory.
- Possibility of brute-force attacks: Hashing includes the risk of brute-force attacks. Specifically, the probability increases when the input scope is absolutely predictable or relatively minor. So, the attackers easily generate all the possible inputs and compare the hash values to find a match. Thus, hashing in data structure becomes vulnerable in the case of passwords from other sensitive data storage.
- Key management: Key management is a crucial task for hashing in a data structure. It becomes necessary for specific applications such as digital signatures or message authentication codes (MACs). So, proper management and protection techniques are required for these keys. These include secured key generation, distribution, and storage. However, these again add complexity to the entire system.
The Next Step
Interested in deep-dive in hashing in data structure? Enroll in the Online MCA Programme – Manipal University Jaipur and grasp the basic to advanced concepts of data structure. This program also includes vital concepts of cloud technology, ML, big data, and more. Also, it offers advanced digital learning platforms and immersive learning. So, take the next step with this online MCA course.
Takeaway
Hashing in data structure is a core cryptography concept that helps protect the original data within a pseudo-code. It’s an absolutely reliable method to store huge amounts of data in a database.
Today, this technique is used in several enterprise applications, search engines, and cloud services. Now, IBM has modified some hash functions and built new functions for security enhancement.
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