Automated Generation of Masked Hardware

Masking has been recognized as a sound and secure countermeasure for cryptographic implementations, protecting against physical side-channel attacks. Even though many different masking schemes have been presented over time, design and implementation of protected cryptographic Integrated Circuits (I...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: David Knichel, Amir Moradi, Nicolai Müller, Pascal Sasdrich
Formato: article
Lenguaje:EN
Publicado: Ruhr-Universität Bochum 2021
Materias:
Acceso en línea:https://doaj.org/article/b229173ac2bd4128a482609c7ed91586
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Descripción
Sumario:Masking has been recognized as a sound and secure countermeasure for cryptographic implementations, protecting against physical side-channel attacks. Even though many different masking schemes have been presented over time, design and implementation of protected cryptographic Integrated Circuits (ICs) remains a challenging task. More specifically, correct and efficient implementation usually requires manual interactions accompanied by longstanding experience in hardware design and physical security. To this end, design and implementation of masked hardware often proves to be an error-prone task for engineers and practitioners. As a result, our novel tool for automated generation of masked hardware (AGEMA) allows even inexperienced engineers and hardware designers to create secure and efficient masked cryptograhic circuits originating from an unprotected design. More precisely, exploiting the concepts of Probe-Isolating Non-Interference (PINI) for secure composition of masked circuits, our tool provides various processing techniques to transform an unprotected design into a secure one, eventually accelerating and safeguarding the process of masking cryptographic hardware. Ultimately, we evaluate our tool in several case studies, emphasizing different trade-offs for the transformation techniques with respect to common performance metrics, such as latency, area, and randomness.