Fully Homomorphic Encryption

What Is Fully Homomorphic Encryption (FHE)?

Fully Homomorphic Encryption (FHE) is a cryptographic technique that allows computations to be performed directly on encrypted data. This means you can perform mathematical operations (addition, subtraction, multiplication, etc.) on data without ever decrypting it.

Let’s say you have a locked box (encryption) containing sensitive information. With traditional encryption, you'd need to unlock the box (decrypt) to perform any calculations on the data inside. FHE, however, lets you perform calculations directly on the locked box, and the final result remains encrypted. Only by unlocking the box with the decryption key can you see the final outcome.

This concept is particularly useful for scenarios where data privacy is paramount, such as cross-chain transactions. For example, cloud computing services can process encrypted user data without ever seeing the underlying information. This helps users to leverage powerful cloud resources while maintaining complete control over their data's confidentiality.

Here are some of the core functionalities of FHE:

• Homomorphic Addition and Multiplication: FHE allows performing both addition and multiplication operations on encrypted data. This enables a wide range of computations without ever revealing the original information.

• Arbitrary Computations: Unlike some encryption schemes limited to specific operations, FHE theoretically supports any kind of computation on encrypted data, making it incredibly versatile.

While FHE is a powerful tool, it's still an evolving field with ongoing research to improve efficiency and practicality. Another potential use case for Fully Homomorphic Encryption (FHE) lies in blockchain infrastructure. By integrating FHE with blockchain technology, secure and private computations on distributed ledgers can be achieved.

Fully Homomorphic Encryption: An Example

Let’s say a DeFi platform wants to offer users a new lending service where borrowers can anonymously prove their creditworthiness without revealing their sensitive financial data.  FHE can solve this problem. Here’s how it will work:

However, sharing raw financial data raises privacy concerns and compliance issues. FHE can solve this problem. Here’s how it will work:

1. Data Encryption: The financial institution encrypts its transaction data using FHE. This ensures customer information like account numbers and transaction amounts remain confidential even after encryption.

2. Secure Analysis in the Cloud: The encrypted data is then transferred to a secure cloud environment for processing.

3. Homomorphic Computations: Within the cloud, the institution can run fraud detection algorithms on the encrypted data. These algorithms might involve analyzing transaction patterns, identifying unusual spending behavior, or flagging suspicious activities. Crucially, all these computations happen on the encrypted data itself, without ever decrypting it.

4. Encrypted Results: The analysis generates encrypted outputs that represent potential fraud indicators.

5. Decryption and Action: The institution downloads the encrypted results and uses its decryption key to reveal the final outcome of the computation, i.e., flagged transactions. This allows them to take appropriate action on potential fraud attempts while safeguarding customer privacy throughout the process.

Advantages and Disadvantages of Fully Homomorphic Encryption

Advantages:

• Enhanced Data Privacy: With FHE, users can leverage external computing resources without compromising the confidentiality of their data. This is crucial for sensitive information processing in cloud environments.

• Secure Outsourcing of Computations: FHE enables outsourcing complex computations to third parties without revealing the underlying data. This can be beneficial for tasks requiring significant processing power.

• Improved Cloud Security: FHE paves the way for more secure cloud storage and computation, establishing trust and adoption of cloud-based services.

Disadvantages:

• Computational Overhead: FHE computations are currently computationally expensive, leading to slower processing times compared to traditional methods.

• Limited Scalability: Implementing FHE for large datasets or complex computations can be challenging due to its resource-intensive nature.

• Ongoing Research: FHE is a relatively new field with ongoing research to improve efficiency and develop practical implementations.

FHE is still in its nascent stage. However, it holds immense potential to revolutionize the current age of computing and blockchain technology.

While FHE deals with single-source data, multi-prover systems enable secure computations of data from multiple parties without revealing their individual contributions. You can read more about it here.

Other cryptographic primitives include Trusted Execution Environments (TEEs), Zero-Knowledge (ZK), and Multi-Party Computation (MPC).

Automata Network is a machine attestation layer that integrates TEEs into AI systems and decentralized networks. Learn more about what we do here.