TCKKS: An Efficient TEE-Assistance CKKS Scheme Without Bootstrapping
Bibliographic record
Abstract
Fully homomorphic encryption (FHE) is a powerful technique that allows unlimited computations on encrypted data without decryption. However, FHE will incur huge computation and storage costs, making it difficult to be applied in real environments. To improve the efficiency of FHE, some optimized schemes have been proposed based on the trusted execution environment (TEE), which offer a lighter and lower overhead solution for FHE optimizations to a certain extent. However, they heavily rely on the confidentiality of the TEE, and their performance is still limited. To solve the above problems, we propose an efficient TEE-assistance CKKS scheme without bootstrapping, named TCKKS, which has the characteristics of security, efficiency, low memory, and scalability. First, to weaken the trust assumption of TEE, we consider TEE to be honest-but-curious, meaning the enclave's algorithm provider will execute the algorithm honestly but might monitor the data in the enclave. Based on this assumption, we design a lightweight secure multiplication protocol (SMP) and a secure rotation protocol (SRP) for TCKKS to efficiently achieve ciphertext multiplication and rotation operations. Then, to further improve the performance of TCKKS, we optimize the arithmetic operations and encryption/decryption operations based on the characteristics of our protocols. Moreover, we prove the security of SMP and SRP under the simulation-based real/ideal worlds model and further demonstrate the security of TCKKS based on the RLWE problem. In addition, extensive experiments indicate that TCKKS has a better performance than mainstream libraries, such as RNS-HEAAN, PALISADE and SEAL.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".