Understanding the Differences Between Fully Homomorphic Encryption and Confidential Computing

Written by Ryan Gifford, Jez Goldstone, and Joseph Wilson.

In the realm of data security and privacy, Fully Homomorphic Encryption (FHE) and Confidential Computing are two cutting-edge technologies that have garnered significant attention. While both aim to protect sensitive information, they do so in fundamentally different ways and are suited for various use cases. Depending on the use case, they can be used independently or together. This blog will explore the key differences between these two technologies, their unique strengths, and why they are crucial for the future of data security.

What is Fully Homomorphic Encryption (FHE)?

Fully Homomorphic Encryption (FHE) is a type of encryption that allows computations to be performed on encrypted data without decrypting it first. This means that data can remain secure and private even while being processed. For example, in the case of AI, it is possible to execute AI models over data that never leaves an encrypted state, thus preserving confidentiality throughout the entire lifecycle of the data. The concept was first proposed by Craig Gentry in 2009 and has since seen significant advancement with respect to speed, functionality, and usability.

Key Characteristics of FHE:

1. Encryption and Computation: With FHE, encrypted data can be manipulated in its encrypted form. The results of these computations are also encrypted and can only be decrypted by someone with the appropriate decryption key.
2. Security and Privacy: Because the data never needs to be decrypted during processing, it remains secure from unauthorized access, even if the computing environment is compromised.
3. Versatility: FHE supports arbitrary ciphertext computations, making it suitable for a wide range of applications, from secure data analysis to private machine learning (SpringerOpen) (CACM).

Challenges and Developments:

Despite its potential, FHE has traditionally been hindered by its computational overhead, which makes it slower than traditional computation on plaintext data. However, ongoing research is focused on improving its efficiency. For instance, techniques like the Number Theoretic Transform (NTT) and optimized bootstrapping algorithms are helping to reduce the performance gap (SpringerOpen) (CACM). Dedicated hardware acceleration for FHE is also an emerging and highly promising field. The complexity of FHE operations and the merging of advanced cryptography with computation also pose challenges to widespread adoption by developers. However, the proliferation of advanced cryptographic libraries that provide a tractable developer experience is rapidly resolving this challenge.

What is Confidential Computing?

Confidential Computing, on the other hand, is a technology designed to protect cleartext data while it is being processed. It leverages hardware-based Trusted Execution Environments (TEEs) to create secure enclaves within a CPU, ensuring that data and code are protected from unauthorized access during execution.

Key Characteristics of Confidential Computing:

1. Hardware-Based Security: Confidential Computing relies on TEEs provided by hardware technologies such as Intel SGX (Software Guard Extensions) and AMD SEV (Secure Encrypted Virtualization). AWS Nitro Enclave (Segregated memory, access, and compute, though not strictly a TEE). These TEEs isolate sensitive computations from the rest of the system (Confidential Computing Summit) (OC3 Conference).
2. Protection During Processing: Unlike traditional encryption, which protects data at rest or in transit, Confidential Computing ensures data is protected while being processed.
3. Integration with Existing Systems: Confidential Computing can be integrated into existing cloud services and applications, providing a layer of security without significant changes to underlying implementations.

Challenges and Developments:

While Confidential Computing provides robust protection during processing, it is not immune to side-channel attacks. Researchers are continuously working on enhancing the security of TEEs to mitigate these vulnerabilities. A badly written application that leaks data will not be protected by Confidential Compute. Additionally, the adoption of Confidential Computing requires changes at the hardware level, which can be a barrier for some organizations (INTC) (Edgeless Systems).

Key Differences Between FHE and Confidential Computing

• Approach to Security:
  • FHE: Focuses on protecting data and IP being shared with 3rd parties or restricted information exchanges. Keeps data encrypted throughout the entire computation process. The data is never decrypted, ensuring maximum privacy.
  • Confidential Computing: Focuses on the integrity and confidentiality of the execution environment. Protects data within secure enclaves during processing. Data is decrypted inside the TEE, processed, and then re-encrypted if necessary.
• Use Cases:
  • FHE: Ideal for scenarios where data privacy or IP protection is paramount and computations need to be performed on encrypted data, such as in secure federated data or machine learning computations and private data analysis.
  • Confidential Computing: Best suited for protecting sensitive workloads in potentially untrusted environments, like cloud computing and secure AI model training (where the use of Attestation Profiles provides assurance over the execution environment integrity).
• Performance:
  • FHE: Traditionally, it has suffered from high computational overhead, though advancements are being made to improve efficiency through software optimization and hardware acceleration.
  • Confidential Computing: Generally offers better performance today for secure computations as it relies on hardware acceleration, but it requires specific hardware support.

Why They Are Important

Both FHE and Confidential Computing address critical aspects of data security in an increasingly digital world. As data breaches and cyber threats continue to evolve, the ability to protect sensitive information during processing becomes essential.

• FHE offers unparalleled privacy, making it possible to perform computations on highly sensitive data without ever exposing it in plaintext. This is crucial for sectors like healthcare, finance, and government, where data privacy is non-negotiable. Similarly, for sensitive IP, many industries can benefit.
• Confidential Computing enhances trust in cloud services and other environments where data is processed by third-party infrastructure. By ensuring data is secure during processing, it enables organizations to leverage the benefits of cloud computing without compromising on security.

Confidential Compute and FHE can be used in a complimentary configuration where the Use Case warrants the benefits of both technologies.

In conclusion, while Fully Homomorphic Encryption and Confidential Computing are distinct technologies with different strengths and applications, they both play vital roles in advancing data security. By understanding their differences and capabilities, organizations can better choose the right approach to protect their sensitive information in various scenarios.

If you're interested in learning more or contributing to CSA's FHE research, you can join the FHE Working Group Circle Community or attend the group's kickoff call on August 28, 2024 at 9am PT.