Homomorphic encryption is a cryptographic technique that allows data to remain encrypted while it is being processed. It makes it possible to perform calculations or analyze encrypted data without accessing the original form.

It forms part of a broader category known as Privacy-Enhancing Technologies (PETs), which are designed to protect sensitive data while it is used, key to private AI deployment. As homomorphic encryption does not require data to be decrypted for computation or analysis, it is beneficial when working across untrusted systems or third-party infrastructure.

With global information security spending expected to reach $212 billion in 2025, driven in part by rising demand for PETs, methods like homomorphic encryption are becoming increasingly relevant in enterprise security strategies.

How does homomorphic encryption work?

Homomorphic encryption follows a multi-step process, which is designed to protect data while preserving its value for analysis or decision-making. 

Here is how it works:

Preparing keys and input data

First, a public and private key pair is generated. The public key is used to encrypt data, while the private key is kept secure and used later to decrypt results. Before any encryption takes place, the input data is prepared in a format that the homomorphic system can process. Depending on the type of encryption used, the input may need to be reformatted so the system can recognize and process it correctly.

Encrypting and securing the input

Next, the prepared data is encrypted using the public key. The encryption process transforms the data into ciphertext, an unreadable version that conceals the original content. Encrypted data is structured in a way that still allows certain operations, such as addition or multiplication, to be performed without exposing the underlying values. The private key remains separate and is not involved at this stage, which helps to keep the input secure during processing.

Computing on encrypted data

Once encrypted, the ciphertext can be sent to another system, such as a cloud server, for processing — a common setup in enterprise environments like finance, healthcare, or retail. The server performs the required computations directly on the encrypted data. Because it never sees the original input or has access to the private key, the data remains secure even in untrusted environments.

Reconstructing and decrypting the output

After computation, the encrypted result is returned to the holder of the private key. Decryption is then used to convert the encrypted output into a readable format. The final result reflects exactly what would have been produced if the same operation had been carried out on the original data. The process keeps the data secure at every stage while still delivering a meaningful and accurate result.

Types of homomorphic encryption 

Homomorphic encryption can be grouped into three categories. Each type allows a different level of processing to take place while the data remains encrypted. 

Here is an overview:

Partially homomorphic encryption

Partially homomorphic encryption supports only one type of operation, such as addition or multiplication, but not both. It works well in straightforward use cases, such as simple financial calculations, where a single kind of calculation is enough to answer a query or generate a result. As it handles fewer operations, it is generally more efficient to run. 

Somewhat homomorphic encryption

Somewhat homomorphic encryption would allow both addition and multiplication, but only for a limited number of steps. With each new operation, the encrypted data becomes more difficult to manage. After a certain point, it can no longer be decrypted accurately. This method suits tasks that involve a small number of steps but require more flexibility than a single operation, such as limited diagnostic scoring or data filtering.

Fully homomorphic encryption

Fully homomorphic encryption supports an unlimited number of operations on encrypted data. Unlike the other types, the encrypted output remains stable and decryptable even after many rounds of computation. Although more resource-intensive, this approach offers greater flexibility for processing sensitive data in complex environments.

Homomorphic encryption use cases 

Homomorphic encryption is gaining traction, with more industries recognizing its value for use cases when sensitive data must remain private during processing.

Here are some examples of how the technique works in sector-specific scenarios:

Data analytics

Hospitals and research institutions can run encrypted analyses on patient data to identify treatment trends or detect health risks without accessing raw medical records. For example, encrypted patient histories can be shared with third-party analytics platforms that generate population-level insights without exposing individual details. Organizations can collaborate effectively without breaching patient confidentiality.

Retailers often rely on third-party platforms to process customer data and personalize experiences. Homomorphic encryption makes it possible to implement federated search capabilities and sort encrypted records, such as purchase history or preferences, without decrypting them. A retailer could query customer behavior patterns or product popularity using encrypted data that is hosted externally, helping to prevent data exposure or unauthorized access.

Secure predictive modeling

Banks and financial services use predictive models to detect fraud and forecast market trends. Homomorphic encryption allows those models to run directly on encrypted financial records. For example, a credit risk model could score loan applications using encrypted income and repayment data. The approach helps financial institutions to gain value from their data without risking a deviation from user privacy rules or regulatory requirements.

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