Securing an Efficient Lightweight AES Accelerator

Securing an Efficient Lightweight AES Accelerator

Huang, Ruoyu (TU Delft Electrical Engineering, Mathematics and Computer Science)

Gaydadjiev, G. (mentor)
Taouil, M. (mentor)
Ma, Kezheng (mentor)
Venkatesha Prasad, Ranga Rao (graduation committee)

Delft University of Technology

Computer Engineering

2023-09-26

Internet of Things (IoT) devices regularly process sensitive data, including personal information. Therefore, ensuring their security is crucial to avoid damage and prevent data breaches. The Advanced Encryption Standard (AES) is generally regarded as one of the most popular cryptographic algorithms for ensuring data security. Typical lightweight implementations of the algorithm published in the literature focus on area and power optimization, while neglecting the performance. This paper presents a novel lightweight approach for the AES algorithm and considers both encryption and decryption. In terms of performance per unit area and performance per unit power, our 32-bit design outperforms the state-of-the-art by 1.69x and 1.27x, respectively. These improvements become even larger when implementing higher data-path designs, such as 64-bit or 128-bit designs. Our non-DOM AES design is secure against Correlation Power Analysis (CPA) but vulnerable to Template Based Attack (TBA) when more than 1500 traces are considered. To enhance its resilience against side channel attacks (SCAs), we modified our design by adopting and further improving on the most recent countermeasure, i.e., Domain-Oriented Masking (DOM). The results demonstrate that incorporating DOM into our design enables it to withstand against both CPA and TBA. Besides, our simplified eight-stage and five-stage 1st-order DOM SBOX designs achieve a reduction in area of 9.9% and 6.9% compared to the original proposed designs, respectively.

Advanced Encryption Standard
Domain Oriented Masking
Lightweight Accelerator
Internet of Things
Side Channel Attacks

http://resolver.tudelft.nl/uuid:b009e5b3-03a8-4b2f-b89b-d8471fbdc30c

2023-12-31

Student theses

master thesis

© 2023 Ruoyu Huang