Garbled Circuits for Leakage-Resilience: Hardware Implementation and Evaluation of One-Time Programs.
Bibliographic record
Abstract
Abstract. The power of side-channel leakage attacks on cryptographic implementations is evident. Today’s practical defenses are typically attack-specific countermeasures against certain classes of side-channel attacks. The demand for a more general solution has given rise to the recent theoretical research that aims to build provably leakage-resilient cryptography. This direction is, however, very new and still largely lacks practitioners ’ evaluation with regard to both efficiency and practical security. A recent approach, One-Time Programs (OTPs), proposes using Yao’s Garbled Circuit (GC) and very simple tamper-proof hardware to securely implement oblivious transfer, to guarantee leakage resilience. Our main contributions are (i) a generic architecture for using GC/OTP modularly, and (ii) hardware implementation and efficiency analysis of GC/OTP evaluation. We implemented two FPGA-based prototypes: a system-on-a-programmable-chip with access to hardware crypto accelerator (suitable for smartcards and future smartphones), and a stand-alone hardware implementation (suitable for ASIC design). We chose AES as a representative complex function for implementation and measurements. As a result of this work, we are able to understand, evaluate and improve the practicality of employing GC/OTP as a leakage-resistance approach. Last, but not least, we believe that our work contributes to bringing together the results of both theoretical and practical communities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".