@inproceedings{bb4d785cf4764e60ad5c99eb259a11ad,
title = "RACSen: Residue Arithmetic and Chaotic Processing in Sensors to Enhance CMOS Imager Security",
abstract = "The widespread adoption of vision sensors raises significant security and privacy concerns. In this paper, we present RACSen as a novel architecture that can increase the security and efficiency of conventional image sensors. RACSen leverages the intricate mathematical properties of the residue number system (RNS) with analog scrambling techniques to create a sophisticated dual-layered encryption mechanism. Incorporating RNS within analog-to-digital converters further strengthens security by mitigating replay attacks and preserving data transmission integrity and confidentiality. Our results demonstrate exceptional encryption, with a perfect pixel change rate of 99.90 and high intensity change of 45.77. This offers robust image data protection with minimal overhead of 11.11%.",
author = "Sepehr Tabrizchi and Nedasadat Taheri and Shaahin Angizi and Arman Roohi",
note = "Publisher Copyright: {\textcopyright} 2024 ACM.; 34th Great Lakes Symposium on VLSI 2024, GLSVLSI 2024 ; Conference date: 12-06-2024 Through 14-06-2024",
year = "2024",
month = jun,
day = "12",
doi = "10.1145/3649476.3658791",
language = "English (US)",
series = "Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI",
publisher = "Association for Computing Machinery",
pages = "551--555",
booktitle = "GLSVLSI 2024 - Proceedings of the Great Lakes Symposium on VLSI 2024",
}