October 24 ~ 25, 2026, Vienna, Austria
Meir Ariel School of Electrical and Computer Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
We present a novel post-quantum cryptographic framework based on masked high-memory convolutional codes in the Niederreiter paradigm. The public key is derived from a high memory polynomial parity check matrix augmented by dense random masking and additional invertible linear transformations. The resulting matrix exhibits strong random like properties, effectively concealing its algebraic structure and resisting known structural and algebraic attacks. Legitimate recipients retain polynomial time decoding capability, whereas adversaries appear to be limited to generic information set decoding with exponential complexity. Under standard cryptanalytic estimates, the proposed construction achieves security margins exceeding those of classical McEliece and Niederreiter-based systems by factors greater than 2²⁰⁰. Beyond its enhanced security profile, the construction offers considerable design flexibility, supporting arbitrary plaintext lengths, linear-time decryption, and a uniform per-bit computational cost, enabling efficient scalability to very long messages. Practical implementation is facilitated by a parallel array of syndrome trellis decoders, dynamically instantiated according to candidate syndrome values, enabling efficient hardware and software implementations with high throughput. Overall, the proposed scheme is a promising candidate for robust, scalable, quantum-resistant public-key cryptography.
Code based cryptography, post quantum cryptography, convolutional codes, syndrome trellis.
Xintong (Tony) Jiang1, David T. Garcia2, 1Chino Hills High School, Chino Hills, CA 91709 2University of California, Los Angeles, CA, 90095
Multi-cat households often manage several litter boxes that require frequent manual inspection, a burden that grows with the number of cats in the home and that existing fully automated litter boxes address only at a cost that is prohibitive for many owners needing multiple units. This paper proposes LitterCheck, an affordable litter box monitoring device that pairs a Raspberry Pi, a YOLOv8-based camera system, and a BME680 volatile organic compound (VOC) gas sensor to notify owners when a litter box needs cleaning, without an automated cleaning mechanism. Development required resolving compatibility issues between the detection model and the Raspberry Pi's software environment, integrating a third-party camera module, and iterating on the physical enclosure across multiple board and camera changes. A bench session comparing the gas sensor's readings indoors and outdoors, evaluated using a two-sample decision-boundary procedure, revealed that the sensor's raw resistance reading does not stabilize quickly after power-on, and that the large difference observed between the two conditions was confounded with this unfinished warm-up rather than attributable to location alone. This finding indicates that a documented sensor settling period is necessary before a fixed VOC threshold can be deployed with confidence, and it is identified here as the primary direction for follow-up work. Compared to prior infrared- and ultrasonic-based designs, LitterCheck's camera-based detection avoids known ambient-temperature sensitivity and does not risk emitting a signal that could discourage the cat from using the litter box, positioning it as a low-cost, non-invasive complement to existing litter box monitoring approaches pending further validation of its gas-sensing threshold.
Litter box monitoring; Internet of Things; volatile organic compounds; computer vision; multi-cat households.