Cryptology ePrint Archive: Report 2015/353

Matrix Computational Assumptions in Multilinear Groups

Paz Morillo and Carla Ràfols and Jorge L. Villar

Abstract: We put forward a new family of computational assumptions, the Kernel Matrix Diffie-Hellman Assumption. Given some matrix $\mathbf{A}$ sampled from some distribution $\mathcal{D}$, the kernel assumption says that it is hard to find "in the exponent" a nonzero vector in the kernel of $\mathbf{A}^\top$. This family is the natural computational analogue of the Matrix Decisional Diffie-Hellman Assumption (MDDH), proposed by Escala et al. As such it allows to extend the advantages of their algebraic framework to computational assumptions.

The $k$-Decisional Linear Assumption is an example of a family of decisional assumptions of strictly increasing hardness when $k$ grows. We show that for any such family of MDDH assumptions, the corresponding Kernel assumptions are also strictly increasingly weaker. This requires ruling out the existence of some black-box reductions between flexible problems (i.e., computational problems with a non unique solution).

Category / Keywords: foundations / matrix assumptions, computational problems, black-box reductions, structure preserving cryptography

Original Publication (with minor differences): IACR-ASIACRYPT-2016

Date: received 20 Apr 2015, last revised 1 Feb 2017

Contact author: jorge villar at upc edu

Available format(s): PDF | BibTeX Citation

Note: This is the full version of the paper with the same title presented in Asiacrypt 2016.

Version: 20170201:140601 (All versions of this report)

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