What Is Homomorphic Encryption?

Homomorphic encryption is a method of encoding that keeps data encrypted during storage, transfer, and processing. Computations can be performed directly on encrypted data without decrypting it first, with the results being the same as if the data were decrypted before processing.

In classic mysteries, sleuths must have a key to decode encrypted messages and reveal the hidden clues. Homomorphic encryption flips the narrative, eliminating the need for a key to work on the puzzle.

This type of data encryption has powerful implications for security in heavily digitized industries with robust regulations related to data privacy. In healthcare, for instance, patient data must be protected under the Health Insurance Portability and Accountability Act (HIPAA).

Homomorphic encryption can also support everyday business operations that require sending data back and forth, such as those found in supply chain management and cloud computing environments.

Whether you operate in a highly regulated industry or not, homomorphic encryption is a valuable technique for safeguarding data. Read on to learn more about its different types, use cases, benefits, and potential challenges so you can make the right decision for your business.

• Homomorphic encryption is a technique for maintaining data privacy throughout the lifecycle by keeping it encrypted while it’s being processed.
• There are three homomorphic encryption schemes with different capabilities and levels of limitation: partially homomorphic encryption, somewhat homomorphic encryption, and fully homomorphic encryption.
• While fully homomorphic encryption is generally reserved for highly complex computations or enterprise organizations with ample resources, many organizations will use partially or somewhat homomorphic encryption in their data operations.
• Homomorphic encryption offers significant benefits for the financial, healthcare, and government sectors that handle sensitive and private data.

There are three main types of homomorphic encryption. Each offers a trade-off between computational capacity and efficiency. Since encryption involves operational resources like infrastructure and energy, it’s critical to understand the capabilities and limitations of all three types.

Partially homomorphic encryption

Partially homomorphic encryption (PHE) is the simplest and fastest type. It allows unlimited computations, but these are limited to one kind — either addition or multiplication.

PHE is the most practical choice for repetitive, single-operation tasks, including:

• Secure email processing
• Tallying votes in an election
• Basic financial transactions

Since complex operations like machine learning and database queries require more than one type of computation, the applications of PHE are relatively restricted. 

Compare PHE to somewhat homomorphic encryption (SHE), which supports a limited number of both operations, and fully homomorphic encryption (FHE), which allows unlimited use of both addition and multiplication. Essentially, you’re trading computational capacity for speed and efficiency.

Somewhat homomorphic encryption

Somewhat homomorphic encryption (SHE) is a step up from PHE in that it supports both addition and multiplication operations on encrypted data, but only for a limited number of times. After that, its reliability falters as the mathematical “noise” in the data builds up.

SHE’s balance of functionality and efficiency makes it the best choice for moderately intensive applications that require a small, fixed number of computations on encrypted data. For example, a market research firm might use it to seek commonalities among customers to create audience segments, or an accounting platform might analyze revenue or average expenses for a client.

Fully homomorphic encryption

Fully homomorphic encryption (FHE) is the most advanced type among homomorphic encryption schemes. It enables unlimited computations of both addition and multiplication on encrypted data.

FHE ensures that data remains confidential even during complex processing, making it ideal for applications like private machine learning, collaborative data analytics, and advanced queries to encrypted databases.

However, because FHE is computationally intensive and requires more power and infrastructure, it’s less efficient than other types. As a result, its use is typically limited to applications where privacy is critical, such as for pharmaceutical companies conducting clinical testing of new cancer drugs involving multiple trials in different countries.

Through consortiums and other initiatives, the technology industry is working to close the gap between FHE's demands and practical applications. These efforts focus on accelerating hardware innovation and outlining technical standards to ensure FHE systems align with regulatory frameworks worldwide.

Homomorphic encryption enhances data security during storage, transmission, and analysis, ensuring that sensitive or private information is never exposed, even when processed by third-party vendors or public cloud solutions.

With so much data being processed every day in an increasingly digital and diverse business environment, more organizations are incorporating it into their technology operations. In fact, it’s projected that nearly 90% of large enterprises will incorporate homomorphic encryption into their data protection processes by 2026.

Homomorphic encryption is also vital for compliance with industry regulations regarding data privacy and security. For example, nearly 80% of banks and fintech companies are using the technology.

It strengthens Zero Trust security architecture by hiding the details and meaning of the data from the people and systems processing it. And even if networks are breached, homomorphic encryption can prevent unauthorized access to data by rendering it unreadable. data by rendering it unreadable.

Additionally, homomorphic encryption supports secure data sharing and processing, whether internal or external. For example:

• Healthcare providers can share encrypted patient records with universities or pharmaceutical companies for vital research while maintaining compliance with privacy laws and regulations.
• Businesses can send data related to intellectual property or proprietary information to vendors or supply chain partners, knowing that it will remain encrypted at all times.
• Government agencies can use homomorphic encryption to protect classified projects and communications from foreign hackers.

Although homomorphic encryption offers undeniable benefits, it also presents obstacles.

Operations on encrypted data are thousands of times slower than on plaintext. FHE, in particular, requires extremely high computational power, storage, and energy to manage the complexity of the mathematics and algorithms involved. This makes it impractical to use regularly for large-scale operations or real-time processing of large datasets.

To get around these challenges, organizations often use PHE or SHE when possible, or they turn to batching techniques to reduce runtimes. As computer processing capacity continues to advance, we will likely see improvements in the efficiency of homomorphic encryption schemes and more organizations using it for more tasks.

Homomorphic encryption is used widely across various industries.

Supply chain management

Homomorphic encryption enables partners to securely share and process sensitive or proprietary data such as inventory levels, production schedules, or shipments. Each party across the chain can perform computations or validations on encrypted data, reducing the risk of leaks or breaches that could compromise competitive intelligence or result from other nefarious activities.

Regulated industries

Thanks to homomorphic encryption, businesses in regulated industries such as healthcare, finance, insurance, energy, and telecommunications can analyze data internally to identify trends and anomalies without exposing confidential information related to patients or customers.

They can also outsource data analysis to cloud providers without exposing the raw data, enabling them to benefit from scalable resources for operations like statistical analysis, fraud detection, or machine learning.

Retailers and e-commerce

Retailers and e-commerce platforms use homomorphic encryption to target specific customers and customer segments with personalized ads or offers without accessing their personal information, supporting privacy-by-design principles.

As homomorphic encryption evolves, future applications could include applying it to deep learning in AI, a technology that has faced security challenges. It could also be used to track money laundering and other cybercrimes that involve sensitive data from multiple sources. challenges. It could also be used to track money laundering and other cybercrimes that involve sensitive data from multiple sources.

Homomorphic encryption represents the leading edge of cryptographic technology, allowing computation on encrypted data without decryption. And as the need for comprehensive cryptographic management evolves in complexity, so must the security solutions that protect your organizations’ most sensitive data.

For more insights into the top trends and security practices leading brands are adopting to protect their critical assets, download the 2024 State of Zero Trust and Encryption Study or explore the Entrust Cryptographic Security Platform firsthand.