Matrix computational assumptions in multilinear groups

We put forward a new family of computational assumptions, the Kernel Matrix Di e- Hellman Assumption. Given some matrix A sampled from some distribution D `;k , the kernel as- sumption says that it is hard to nd \in the exponentPreprin

Constant size ciphertexts in threshold attribute-based encryption

Attribute-based cryptography has emerged in the last years as a promising primitive for digital security. For instance, it provides good solutions to the problem of anonymous access control. In a ciphertextpolicy attribute-based encryption scheme, the secret keys of the users depend on their attributes. When encrypting a message, the sender chooses which subset of attributes must be held by a receiver in order to be able to decrypt. All current attribute-based encryption schemes that admit reasonably expressive decryption policies produce ciphertexts whose size depends at least linearly on the number of attributes involved in the policy. In this paper we propose the first scheme whose ciphertexts have constant size. Our scheme works for the threshold case: users authorized to decrypt are those who hold at least t attributes among a certain universe of attributes, for some threshold t chosen by the sender. An extension to the case of weighted threshold decryption policies is possible. The security of the scheme against selective chosen plaintext attacks can be proven in the standard model by reduction to the augmented multi-sequence of exponents decisional Diffie-Hellman (aMSE-DDH) problem

The Kernel Matrix Diffie-Hellman assumption

The final publication is available at https://link.springer.com/chapter/10.1007%2F978-3-662-53887-6_27We put forward a new family of computational assumptions, the Kernel Matrix Diffie-Hellman Assumption. Given some matrix A sampled from some distribution D, the kernel assumption says that it is hard to find “in the exponent” a nonzero vector in the kernel of A¿ . This family is a 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).Peer Reviewe

Matrix computational assumptions in multilinear groups

We put forward a new family of computational assumptions, the Kernel Matrix Di e- Hellman Assumption. Given some matrix A sampled from some distribution D `;k , the kernel as- sumption says that it is hard to nd \in the exponen

The Kernel Matrix Diffie-Hellman Assumption

The final publication is available at link.springer.comWe put forward a new family of computational assumptions, the Kernel Matrix Diffie-Hellman Assumption. Given some matrix A sampled from some distribution D, the kernel assumption says that it is hard to find “in the exponent” a nonzero vector in the kernel of A>. This family is a 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).Peer Reviewe

An algebraic framework for Diffie–Hellman assumptions

We put forward a new algebraic framework to generalize and analyze Diffie-Hellman like Decisional Assumptions which allows us to argue about security and applications by considering only algebraic properties. Our D`,k-MDDH assumption states that it is hard to decide whether a vector in ¿ìs linearly dependent of the columns of some matrix in ¿`×k sampled according to distribution D`,k. It covers known assumptions such as DDH, 2-Lin (linear assumption), and k-Lin (the k-linear assumption). Using our algebraic viewpoint, we can relate the generic hardness of our assumptions in m-linear groups to the irreducibility of certain polynomials which describe the output of D`,k. We use the hardness results to find new distributions for which the D`,k-MDDH-Assumption holds generically in m-linear groups. In particular, our new assumptions 2-SCasc and 2-ILin are generically hard in bilinear groups and, compared to 2-Lin, have shorter description size, which is a relevant parameter for efficiency in many applications. These results support using our new assumptions as natural replacements for the 2-Lin Assumption which was already used in a large number of applications. To illustrate the conceptual advantages of our algebraic framework, we construct several fundamental primitives based on any MDDH-Assumption. In particular, we can give many instantiations of a primitive in a compact way, including public-key encryption, hash-proof systems, pseudo-random functions, and Groth-Sahai NIZK and NIWI proofs. As an independent contribution we give more efficient NIZK and NIWI proofs for membership in a subgroup of ¿` . The results imply very significant efficiency improvements for a large number of schemes.Peer Reviewe

An algebraic framework for Diffie-Hellman assumptions

The final publication is available at Springer via http://dx.doi.org/10.1007/s00145-015-9220-6We put forward a new algebraic framework to generalize and analyze Di e-Hellman like Decisional Assumptions which allows us to argue about security and applications by considering only algebraic properties. Our D`;k-MDDH assumption states that it is hard to decide whether a vector in G` is linearly dependent of the columns of some matrix in G` k sampled according to distribution D`;k. It covers known assumptions such as DDH, 2-Lin (linear assumption), and k-Lin (the k-linear assumption). Using our algebraic viewpoint, we can relate the generic hardness of our assumptions in m-linear groups to the irreducibility of certain polynomials which describe the output of D`;k. We use the hardness results to nd new distributions for which the D`;k-MDDH-Assumption holds generically in m-linear groups. In particular, our new assumptions 2-SCasc and 2-ILin are generically hard in bilinear groups and, compared to 2-Lin, have shorter description size, which is a relevant parameter for e ciency in many applications. These results support using our new assumptions as natural replacements for the 2-Lin Assumption which was already used in a large number of applications. To illustrate the conceptual advantages of our algebraic framework, we construct several fundamental primitives based on any MDDH-Assumption. In particular, we can give many instantiations of a primitive in a compact way, including public-key encryption, hash-proof systems, pseudo-random functions, and Groth-Sahai NIZK and NIWI proofs. As an independent contribution we give more e cient NIZK and NIWI proofs for membership in a subgroup of G`. The results imply very signi cant e ciency improvements for a large number of schemes.Peer Reviewe