How to Use Homomorphic Encryption for Secure Data Processing

November 26, 20234 min read

Homomorphic encryption is a form of encryption that allows computation on ciphertexts, generating an encrypted result which, when decrypted, matches the result of operations performed on the plaintext. This property enables secure data processing in untrusted environments.

Understanding Homomorphic Encryption

Before diving into the method of using homomorphic encryption, it’s essential to have a basic understanding of the concept:

  • Encryption: The process of converting data into a form that cannot be understood without a key to decrypt it.
  • Homomorphic Property: This refers to the system’s ability to perform operations on encrypted data without decryption.
  • Ciphertext: The encrypted version of the plaintext (original data).
  • Plaintext: The original data before encryption.

Key Components in Homomorphic Encryption

  • Public Key: Used to encrypt data. In the homomorphic context, it must also support operations on the ciphertext.
  • Private Key: Used to decrypt data. This remains secret and is necessary to interpret the results of computations.
  • Evaluation Key: Sometimes needed depending on the scheme, this key allows certain operations to be performed on the ciphertexts.

Types of Homomorphic Encryption

  • Partially Homomorphic Encryption (PHE): Supports only one type of operation (either addition or multiplication) an unlimited number of times.
  • Somewhat Homomorphic Encryption (SHE): Supports both additions and multiplications, but only a limited number of operations in sequence.
  • Fully Homomorphic Encryption (FHE): Supports an unlimited number of both additions and multiplications.

Steps to Secure Data Processing using Homomorphic Encryption

Step 1: Choose the Homomorphic Encryption Scheme

  • Understand the limitations and capabilities of different homomorphic encryption schemes.
  • Choose between PHE, SHE, or FHE based on the required operations.

Step 2: Generate Keys

  • Public and Private Keys are generated through a key generation algorithm.
  • The security of the encryption largely depends on the complexity of the key generation process; hence it should be robust.

Step 3: Encrypt the Data

  • The sender or data owner uses the public key to encrypt the plaintext data.
  • The encryption process should ensure that data remains confidential.

Step 4: Transfer the Ciphertext

  • Encrypted data (ciphertext) can be safely transmitted to an untrusted third-party processing environment or cloud.

Step 5: Perform Operations on the Ciphertext

  • Use the chosen homomorphic encryption scheme to perform calculations directly on the encrypted data.
  • Ensure that the evaluation key (if required) is accessible for performing operations.

Step 6: Return the Encrypted Result

  • The third-party environment processes the data and returns the encrypted result back to the data owner or an authorized entity.

Step 7: Decrypt the Result

  • The data owner uses their private key to decrypt the received result.
  • The decrypted result will match the computation as if it had been performed on the plaintext.

Considerations When Using Homomorphic Encryption

  • Performance: Homomorphic Encryption can be significantly slower than traditional encryption methods due to complex computations on ciphertexts.
  • Data Size: Encrypted data will be larger than the plaintext which can present challenges for bandwidth and storage.
  • Security: Key management is vital for security; losing the private key can make encrypted data unrecoverable.
  • Use Cases: Due to its computational intensity, consider using Homomorphic Encryption primarily for high-value calculations where data privacy cannot be compromised.


Homomorphic encryption is a sophisticated tool that enables performing computations on encrypted data without exposing the underlying plaintext. This technology has significant implications for secure data processing, privacy-preserving computation, and cloud computing. However, the choice of homomorphic encryption methods, understanding of their capabilities and limitations, performance, security considerations, and practicality in specific use cases must be carefully weighed before deployment.