Sven Argo
Ruhr University Bochum, Bochum, Germany

Marloes Venema
University of Wuppertal, Wuppertal, Germany

Doreen Riepel
CISPA Helmholtz Center for Information Security, Saarbrücken, Germany

Tim Güneysu
Ruhr University Bochum, Bochum, Germany; Deutsches Forschungszentrum für Künstliche Intelligenz, Bremen, Germany

Diego F. Aranha
Aarhus University, Aarhus, Denmark

Keywords: attribute-based encryption, multi-authority ABE, implementation

Abstract

Since attribute-based encryption (ABE) was proposed in 2005, it has established itself as a valuable tool in the enforcement of access control. For practice, it is important that ABE satisfies many desirable properties such as multi-authority and negations support. Nowadays, we can attain these properties simultaneously, but none of these schemes have been implemented. Furthermore, although simpler schemes have been optimized extensively on a structural level, there is still much room for improvement for these more advanced schemes. However, even if we had schemes with such structural improvements, we would not have a way to benchmark and compare them fairly to measure the effect of such improvements. The only framework that aims to achieve this goal, ABE Squared (TCHES ’22), was designed with simpler schemes in mind.In this work, we propose the ABE Cubed framework, which provides advanced benchmarking extensions for ABE Squared. To motivate our framework, we first apply structural improvements to the decentralized ciphertext-policy ABE scheme supporting negations presented by Riepel, Venema and Verma (ACM CCS ’24), which results in five new schemes with the same properties. We use these schemes to uncover and bridge the gaps in the ABE Squared framework. In particular, we observe that advanced schemes depend on more “variables” that affect the schemes’ efficiency in different dimensions. Whereas ABE Squared only considered one dimension (as was sufficient for the schemes considered there), we devise a benchmarking strategy that allows us to analyze the schemes in multiple dimensions. As a result, we obtain a more complete overview on the computational efficiency of the schemes, and ultimately, this allows us to make better-founded choices about which schemes provide the best efficiency trade-offs for practice.