Optimizing Security of Radio Frequency Identification Systems in Assistive Devices: A Novel Unidirectional Systolic Design for Dickson-Based Field Multiplier
Bibliographic record
Abstract
The emergence of the Internet of Things (IoT) technologies has greatly enhanced the lives of individuals with disabilities by leveraging radio frequency identification (RFID) systems to improve autonomy and access to essential services. However, these advancements also pose significant security risks, particularly through side-channel attacks that exploit weaknesses in the design and operation of RFID tags and readers, potentially jeopardizing sensitive information. To combat these threats, several solutions have been proposed, including advanced cryptographic protocols built on cryptographic algorithms such as elliptic curve cryptography. While these protocols offer strong protection and help minimize data leakage, they often require substantial computational resources, making them impractical for low-cost RFID tags. Therefore, it is essential to focus on the efficient implementation of cryptographic algorithms, which are fundamental to most encryption systems. Cryptographic algorithms primarily depend on various finite field operations, including field multiplication, field inversion, and field division. Among these operations, field multiplication is especially crucial, as it forms the foundation for executing other field operations, making it vital for the overall performance and security of the cryptographic framework. The method of implementing field multiplication operation significantly influences the system’s resilience against side-channel attacks; for instance, implementation using unidirectional systolic array structures can provide enhanced error detection capabilities, improving resistance to side-channel attacks compared to traditional bidirectional multipliers. Therefore, this research aims to develop a novel unidirectional systolic array structure for the Dickson basis multiplier, which is anticipated to achieve lower space and power consumption, facilitating the efficient and secure implementation of computationally intensive cryptographic algorithms in RFID systems with limited resources. This advancement is crucial as RFID technology becomes increasingly integrated into various IoT applications for individuals with disabilities, including secure identification and access control.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".