# What is Homomorphic Encryption? Types, Applications and Benefits

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Here in this post, we discuss what is Homomorphic Encryption and How does homomorphic encryption work? Different Homomorphic encryption example and which Homomorphic encryption problems solved and useful for us.

## The Vision of Homomorphic Encryption:

The goal of homomorphic encryption is to allow these operations to be carried out while the data is still encrypted so that the result of the computation is also encrypted. This would allow sensitive data to be processed and analyzed while still maintaining its security and privacy. Homomorphic encryption has the potential to revolutionize the way that data is processed and analyzed, enabling new applications in fields such as cloud computing, medical research, and financial analytics.

## What is Homomorphic Encryption:

Let’s understand first Why we need homomorphic encryption. So, we talk about that thing then there are several reasons to use this technology like privacy, Security, Efficiency in Computing, Data Sharing, and Data integration. Now let’s Define homomorphic encryption.

Homomorphic encryption is a form of encryption that allows mathematical operations to be performed on encrypted data. The result of the computation is also encrypted, but it is equivalent to the result that would be obtained if the operation were performed on the unencrypted data. This means that the data can be analyzed and processed while it is still encrypted, without the need to decrypt it first.

There are two main types of homomorphic encryption: partially homomorphic encryption and fully homomorphic encryption. Partially homomorphic encryption allows a limited set of operations to be performed on encrypted data, while fully homomorphic encryption allows any computation to be performed on the encrypted data. Fully homomorphic encryption is considered to be the more secure and versatile form of homomorphic encryption, but it is also more computationally intensive and requires more resources to implement.

Homomorphic encryption has the potential to revolutionize the way that data is processed and analyzed, enabling new applications in fields such as cloud computing, medical research, and financial analytics. However, the development of practical homomorphic encryption schemes has proven to be a challenging task, and there are still many open research problems in this area.

After reading the above information you get the Absolute homomorphic encryption meaning and What is fully homomorphic encryption? After reading the topic I think now you know the Homomorphic encryption meaning and Let’s move forward on Homomorphic encryption explained.

## How does Homomorphic Encryption work?

Homomorphic encryption works by using an encryption algorithm to transform plaintext data into ciphertext that can be decrypted only with the appropriate decryption key. The encryption algorithm is designed to allow certain types of mathematical operations to be performed on the ciphertext, such that the result of the computation is equivalent to the result that would be obtained if the operation were performed on the plaintext data.

For the Homomorphic encryption example, suppose we have a simple homomorphic encryption scheme that allows us to perform addition on encrypted data. If we have two encrypted values, E(x) and E(y), and we want to compute their sum, we can use the encryption scheme to compute E(x+y) without first decrypting E(x) and E(y). The result of this computation, E(x+y), is also encrypted, but it is equivalent to the result that we would have obtained if we had first decrypted E(x) and E(y) and then performed the addition on the plaintext values.

In practice, homomorphic encryption schemes are typically more complex, and they may allow for more than one type of operation to be performed on encrypted data. However, the basic idea is the same: to enable certain types of computations to be carried out on encrypted data without the need to decrypt it first.

And these all are worked on the different Homomorphic encryption algorithms like Paillier cryptosystem, Goldwasser-Micali cryptosystem, Gentry’s fully homomorphic encryption scheme, Brakerski-Gentry-Vaikuntanathan (BGV) cryptosystem, FHElib and, SEAL. This algorithm solved our real-time Homomorphic encryption problems and secure our information.

## Homomorphic Encryption Types:

There are two main types of homomorphic encryption: partially homomorphic encryption and fully homomorphic encryption.

Partially homomorphic encryption allows a limited set of operations to be performed on encrypted data. For example, a partially homomorphic encryption scheme might allow addition or multiplication to be performed on encrypted data, but not both. Partially homomorphic encryption schemes are typically easier to implement and more efficient than fully homomorphic encryption schemes, but they are also less versatile and less secure.

Fully homomorphic encryption allows any computation to be performed on encrypted data. This means that any function that can be computed on plaintext data can also be computed on encrypted data using a fully homomorphic encryption scheme. Fully homomorphic encryption is considered to be the most secure and versatile form of homomorphic encryption, but it is also more computationally intensive and requires more resources to implement.

There are several different approaches to constructing homomorphic encryption schemes, including additive homomorphic encryption, multiplicative homomorphic encryption, and linear homomorphic encryption. Each of these approaches has its own strengths and weaknesses, and researchers are still working to develop more efficient and practical homomorphic encryption schemes.

Homomorphic encryption has several potential advantages, including:

1. Privacy: Homomorphic encryption allows sensitive data to be processed and analyzed without exposing it to the entities that are performing the computation. This is particularly important in cases where the data is sensitive or personal, such as medical records or financial information.
2. Security: By performing computations on encrypted data, homomorphic encryption helps to protect against the risk of data breaches and unauthorized access to sensitive information.
3. Cloud computing: Homomorphic encryption in cloud computing enable new applications in cloud computing, allowing data to be processed in the cloud without exposing it to the cloud provider. This would allow organizations to outsource their computation needs without sacrificing control over their data.
4. Data sharing: Homomorphic encryption could enable new ways of sharing data between organizations, allowing them to collaborate on projects without exposing their sensitive data to each other.
5. Efficient computation: Homomorphic encryption algorithms can be designed to allow certain types of computations to be performed more efficiently on encrypted data, potentially enabling new applications and use cases.

However, homomorphic encryption also has some disadvantages, including:

1. Complexity: Homomorphic encryption schemes can be complex and difficult to implement, particularly fully homomorphic encryption schemes.
2. Efficiency: Homomorphic encryption schemes can be computationally intensive, particularly fully homomorphic encryption schemes, which can make them expensive and impractical to use in certain situations.
3. Limitations: Homomorphic encryption schemes may have limitations on the types of computations that can be performed, or they may not provide the same level of security as traditional encryption schemes.
4. Compatibility: Homomorphic encryption may not be compatible with certain types of data or applications, which could limit its use in practice.

## Applications of Homomorphic Encryption:

Homomorphic encryption has the potential to be used in a wide range of applications, including:

1. Cloud computing: Homomorphic encryption could be used to allow sensitive data to be processed in the cloud without exposing it to the cloud provider. This would enable organizations to outsource their computation needs without sacrificing control over their data.
2. Medical research: Homomorphic encryption could be used to allow researchers to analyze sensitive medical data without exposing it to them. This could enable new discoveries and insights while still protecting patient privacy.
3. Financial analytics: Homomorphic encryption could be used to allow financial institutions to analyze sensitive financial data without exposing it to them. This could enable new insights and efficiencies while still protecting customer privacy.
4. Data sharing: Homomorphic encryption could enable new ways of sharing data between organizations, allowing them to collaborate on projects without exposing their sensitive data to each other.
5. Internet of Things (IoT): Homomorphic encryption could be used to allow data from IoT devices to be processed and analyzed without exposing it to the entities that are performing the computation. This could enable new insights and efficiencies while still protecting user privacy.
6. E-voting: Homomorphic encryption could be used to enable secure and private online voting systems.

## Conclusion:

Homomorphic encryption is a form of encryption that allows mathematical operations to be performed on encrypted data. This enables data to be processed and analyzed while still maintaining its privacy and security. There are two main types of homomorphic encryption: partially homomorphic encryption and fully homomorphic encryption. Partially homomorphic encryption allows a limited set of operations to be performed on encrypted data, while fully homomorphic encryption allows any computation to be performed on encrypted data.