DataSeal: Ensuring the Verifiability of Private Computation on Encrypted Data

TL;DR

DataSeal is a novel method that combines algorithm-based fault tolerance with Fully Homomorphic Encryption to efficiently verify the integrity of encrypted computations, significantly reducing overhead compared to existing techniques.

Contribution

We introduce DataSeal, a new approach that ensures verifiability of FHE computations with low overhead by integrating ABFT, addressing a key challenge in secure outsourced computation.

Findings

DataSeal achieves lower overhead than MAC, ZKP, and TEE-based methods.

Overheads decrease to nearly negligible as problem size increases.

Demonstrated effectiveness across diverse contexts.

Abstract

Fully Homomorphic Encryption (FHE) allows computations to be performed directly on encrypted data without needing to decrypt it first. This "encryption-in-use" feature is crucial for securely outsourcing computations in privacy-sensitive areas such as healthcare and finance. Nevertheless, in the context of FHE-based cloud computing, clients often worry about the integrity and accuracy of the outcomes. This concern arises from the potential for a malicious server or server-side vulnerabilities that could result in tampering with the data, computations, and results. Ensuring integrity and verifiability with low overhead remains an open problem, as prior attempts have not yet achieved this goal. To tackle this challenge and ensure the verification of FHE's private computations on encrypted data, we introduce DataSeal, which combines the low overhead of the algorithm-based fault tolerance (ABFT) technique with the confidentiality of FHE, offering high efficiency and verification capability. Through thorough testing in diverse contexts, we demonstrate that DataSeal achieves much lower overheads for providing computation verifiability for FHE than other techniques that include MAC, ZKP, and TEE. DataSeal's space and computation overheads decrease to nearly negligible as the problem size increases.