FrodoKEM: A CCA-Secure Learning With Errors Key Encapsulation Mechanism
Bibliographic record
Abstract
Large-scale quantum computers capable of implementing Shor's algorithm pose a significant threat to the security of the most widely used public-key cryptographic schemes. This risk has motivated substantial efforts by standards bodies and government agencies to identify and standardize quantum-safe cryptographic systems. Among the proposed solutions, lattice-based cryptography has emerged as the foundation for some of the most promising protocols. This paper describes FrodoKEM, a family of conservative key-encapsulation mechanisms (KEMs) whose security is based on generic, “unstructured” lattices. FrodoKEM is proposed as an alternative to the more efficient lattice schemes that utilize algebraically structured lattices, such as the recently standardized ML-KEM scheme. By relying on generic lattices, FrodoKEM minimizes the potential for future attacks that exploit algebraic structures while enabling simple and compact implementations. Our plain C implementations demonstrate that, despite its conservative design and parameterization, FrodoKEM remains practical. For instance, the full protocol at NIST security level 1 runs in approximately 0.97 ms on a server-class processor, and 4.98 ms on a smartphone-class processor. FrodoKEM obtains (single-target) IND-CCA security using a variant of the Fujisaki-Okamoto transform, applied to an underlying public-key encryption scheme called FrodoPKE. In addition, using a new tool called the Salted Fujisaki-Okamoto (SFO) transform, FrodoKEM is also shown to tightly achieve multi-target security, without increasing the FrodoPKE message length and with a negligible performance impact, based on the multi-target IND-CPA security of FrodoPKE.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".