What is Homomorphic Encryption? [A Detailed Guide]

Homomorphic encryption stands at the forefront in cryptography, due to its unique solution to a longstanding challenge of performing computations on encrypted data without decryption.

As we know, traditional encryption methods require decrypted data before performing any computations, leaving it vulnerable to potential breaches.

However, homomorphic encryption allows direct computation on encrypted data, preserving its confidentiality throughout the process. It has the potential to revolutionize industries that rely on sensitive data, such as healthcare, finance, and cloud computing.

This innovative technology has the potential to transform the way data is handled and processed, particularly in sensitive domains such as healthcare, finance, and cloud computing.

In this blog post, we will explore the concept of homomorphic encryption, its applications, and the impact it can have on data security and privacy.

What is Homomorphic Encryption?

Data has three states. It includes at rest, in transit, and in use. Almost all encryption is involved in the first two of these. The reason is that data at rest or in transit stays the same. It is of the same value when it’s decrypted as when it was encrypted. This is different for “data in use” (which is an often-used term in this context).

Almost all mathematical operations on the ciphertext would change the value of the corresponding plaintext. It is hard to ensure that the plaintext changes in the “right way.” Especially because encryption algorithms are designed to quickly and permanently remove any relationships between the plaintext and the corresponding ciphertext.

Also Read: What is Data Encryption? How does it Work?

A good encryption algorithm will produce a ciphertext indistinguishable from a random number; the only way to determine which plaintext stands behind a certain ciphertext is to use the proper key to decrypt it.

It is possible to perform a mathematical operation on the encrypted data; there must be some relationship between the plain and ciphertexts. It has to be likely to add or multiply together two ciphertexts and have the result be the same as performing the operation on the plaintexts and then encrypting it.

At the same time, this relationship must be hidden from the observation. Encryption is broken if watching mathematical operations on ciphertexts reveal any information about the corresponding plaintexts.

These are mutual goals. Strong encryption and the possibility to perform the maths on the ciphertext and get the correct answer. Homomorphic encryption algorithms have managed it.

Types of Homomorphic Encryption:

The differences in the types of homomorphic encryption depend on how many times mathematical functions can be performed to get the plaintext from ciphertext.

Partially Homomorphic Encryption:

In Partially homomorphic encryption, single mathematical actions can be executed on encrypted values, while addition or multiplication can be performed on the values as many times as desired.

Somewhat Homomorphic Encryption:

These operations are used to execute on plain text and ciphertext. In this way, addition and multiplication are simple operations, and the difference is that execution can be done a fixed number of times.

Fully Homomorphic Encryption:

It supports both PHE and SCH, so all can be performed without any limit. It can be performed without letting anyone decrypt it, and the data processing will always result in the same output.

Applications of Fully Homomorphic Encryption

Craig Gentry pointed out in his graduation thesis that “Fully homomorphic encryption has numerous applications.

For example, it enables private queries to a search engine—the user submits an encrypted query, and the search engine computes a succinct encrypted answer without looking at the query in the clear. It also enables searching on encrypted data—users store encrypted files on a remote server. 

They can later have the server retrieve only files that (when decrypted) satisfy some Boolean constraint, even though the server cannot decrypt them independently.

More broadly, fully homomorphic encryption improves the efficiency of secure multiparty computation.” The subsequent practitioners have already listed a significant number of practical applications of FHE, some of which are discussed below:

Securing Data Stored in the Cloud:

First of all, using homomorphic encryption, you can store data on the cloud without fear of the security of your data: you will be able to compute, search, and analyze ciphered information and then decrypt the one you need without compromising the entire dataset.

Enabling Data Analytics in Regulated Industries:

Businesses will be able to utilize homomorphic encryption to send the data to the commercial cloud for analysis while still abiding by the tight privacy regulations in their specific industry.

For example, it is essential for the financial sector and retail, IT companies, and medical facilities: the solution makes data usable for people without allowing them to see the actual ciphered data.

Improving Election Security and Transparency:

The principles of homomorphic encryption are used to consider how voting in democratic states can become more secure and transparent.

For example, the Paillier encryption scheme is ideal for various sums of values without having access to the actual sum. In the long run, the technology will protect the data from manipulation but still allow an authorized third party to verify the activities.

Limitations of Fully Homomorphic Encryption

Despite of being the advanced and efficient solution, homomorphic encryption lacks in few areas. Here are these two:

Support for Multiple Users: 

If many users share the same system, where they want to perform computations based on the internal database while preventing the provider from getting the clear data.

The solution with a separate database for each desk-user, encrypted with the user’s public key, may seem efficient in practice, but with a substantial size of the database and a significant amount of users, it becomes unpractical.

Limitations on Complex Algorithms: 

Even with the successful implementation of this technology and its working principle, some operations involving extensive and complex algorithms have limits in the sense of performance.

All current fully homomorphic encryption schemes introduce a significant computational overhead. Overhead is the amount of computation time that is needed to run computation on the encrypted data compared to running it on clear text.

Although this number is polynomial, it can be quite substantial, making the fully homomorphic computation of complex functions impractical.

Real-World Examples of Homomorphic Encryption

Homomorphic encryption is a disruptive cryptographic technology that completely changes data security by allowing data computations under encryption without the need to decipher.

The implications of the technology in sectors such as healthcare, finance, and government, which are characterized by extraordinary amounts of private data, are numerous.

Healthcare Sector:

Homomorphic encryption guarantee that millions of patient data cannot be intercepted by unauthorized individuals. What’s more, it could safely send the data to various providers across each other, which means that the way patient data is shared and exchanged is changed too.

This might also imply that there would be efficiency in terms of service delivery and, more significantly, patient information would always stay private.

Financial Sector:

Stabilizing the financial sector necessitates the continuous monitoring of secure touchpoints and platforms. Financial institutions can adopt homomorphic encryption as a technological solution to ensure the secure transmission of information systems.

This approach not only secures data but also fortifies the infrastructure against potential breaches, thereby maintaining the sector’s integrity and trust.

Government:

In the realm of government operations, homomorphic encryption is instrumental in protecting sensitive information. Moreover, it enhances the security of national records, thus fostering a stronger sense of trust among citizens towards the administration.

By implementing this encryption method, the government demonstrates its commitment to safeguarding the public’s data, thereby enhancing national security and individual privacy.

Future Outlook: Advancements and Impact of Homomorphic Encryption

The future outlook for homomorphic encryption is bright, marked by significant advancements and impactful research directions:

Research and Advancements

Homomorphic encryption, while established, has seen notable progress in recent years, focusing primarily on enhancing its efficiency.

The main thrust of current research is to mitigate the computational overhead accompanying various forms of homomorphic encryption, a crucial step given the vast number of computations involved in its practical applications. 

Efforts are underway to refine these processes, ensuring they become faster and more feasible for real-world data handling without compromising the robustness of encryption standards.

Concurrently, there’s a push towards developing standardized homomorphic encryption schemes designed for seamless integration into diverse systems, enhancing user accessibility without necessitating significant adjustments.

Impact on Data Security and Privacy

Homomorphic encryption is set to redefine data security and privacy landscapes. It enables computations on encrypted data, offering a robust shield against unauthorized access and data breaches—a boon for sectors like healthcare and finance, where data confidentiality is paramount. 

Moreover, it paves the way for innovative applications in secure cloud computing and data sharing. Homomorphic encryption is thus positioned to be a cornerstone technology in the evolution of data security and privacy, opening new avenues for secure data processing and analysis that were previously unattainable.

This progression signifies a transformative phase in cryptography, with homomorphic encryption at the forefront of facilitating secure, efficient, and private data handling across various sectors.

Final Thoughts

Homomorphic encryption marks a significant leap forward in cryptography, offering a robust mechanism for the secure processing and analysis of encrypted data.

This advancement opens the door to new possibilities in data handling while ensuring the protection of sensitive information.

As the importance of data security escalates across organizations, homomorphic encryption is poised to become a pivotal tool in combating cyber threats and preserving data confidentiality.

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