Midori: A block cipher for low energy

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Abstract

In the past few years, lightweight cryptography has become a popular research discipline with a number of ciphers and hash functions proposed. The designers’ focus has been predominantly to minimize the hardware area, while other goals such as low latency have been addressed rather recently only. However, the optimization goal of low energy for block cipher design has not been explicitly addressed so far. At the same time, it is a crucial measure of goodness for an algorithm. Indeed, a cipher optimized with respect to energy has wide applications, especially in constrained environments running on a tight power/energy budget such as medical implants. This paper presents the block cipher Midori (The name of the cipher is the Japanese translation for the word Green.) that is optimized with respect to the energy consumed by the circuit per bt in encryption or decryption operation. We deliberate on the design choices that lead to low energy consumption in an electrical circuit, and try to optimize each component of the circuit as well as its entire architecture for energy. An added motivation is to make both encryption and decryption functionalities available by small tweak in the circuit that would not incur significant area or energy overheads. We propose two energy-efficient block ciphers Midori128 and Midori64 with block sizes equal to 128 and 64 bits respectively. These ciphers have the added property that a circuit that provides both the functionalities of encryption and decryption can be designed with very little overhead in terms of area and energy. We compare our results with other ciphers with similar characteristics: it was found that the energy consumptions of Midori64 and Midori128 are by far better when compared ciphers like PRINCE and NOEKEON.

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APA

Banik, S., Bogdanov, A., Isobe, T., Shibutani, K., Hiwatari, H., Akishita, T., & Regazzoni, F. (2015). Midori: A block cipher for low energy. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9453, pp. 411–436). Springer Verlag. https://doi.org/10.1007/978-3-662-48800-3_17

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