TurboHE: Accelerating Fully Homomorphic Encryption Using FPGA Clusters
Bibliographic record
Abstract
With the burgeoning demands for cloud computing in various fields followed by the rising attention to sensitive data exposure, Fully Homomorphic Encryption (FHE) is gaining popularity as a potential solution to privacy protection. By performing computations directly on the ciphertext (encrypted data) without decrypting it, FHE can guarantee the security of data throughout its lifecycle without compromising the privacy. However, the excruciatingly slow speed of FHE scheme makes adopting it impractical in real life applications. Therefore, hardware accelerators come to the rescue to mitigate the problem. Among various hardware platforms, FPGA clusters are particularly promising because of their flexibility and ready availability at many cloud providers such as FPGA-as-a-Service (FaaS). Hence, reusing the existing infrastructure can greatly facilitate the implementation of FHE on the cloud.In this paper, we present TurboHE, the first hardware accelerator for FHE operations based on an FPGA cluster. TurboHE aims to boost the performance of CKKS, one of the fastest FHE schemes which is most suitable to machine learning applications, by accelerating its computationally intensive and frequently used operation: relinearization. The proposed scalable architecture based on hardware partitioning can be easily configured to accommodate high acceleration requirements for relinearization with very large CKKS parameters. As a demonstration, an implementation, which supports 32,768 polynomial coefficients and a coefficient bitwidth of 594 decomposed into 11 Residue Number System (RNS) components, was deployed on a cluster consisting of 9 Xilinx VU13P FPGAs. The cluster operated at 200 MHz and achieved 1096 times throughput compared with a single threaded CPU implementation. Moreover, the low level hardware components implemented in this work such as the NTT module can also be applied to accelerate other lattice-based cryptography schemes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".