Hybrid Publicly Verifiable Computation

Publicly Verifiable Outsourced Computation (PVC) allows weak devices to delegate com-putations to more powerful servers, and to verify the correctness of results. Delegation and verification rely only on public parameters, and thus PVC lends itself to large multi-user systems where entities need not be registered. In such settings, individual user requirements may be diverse and cannot be realised with current PVC solutions. In this paper, we in-troduce Hybrid PVC (HPVC) which, with a single setup stage, provides a flexible solution to outsourced computation supporting multiple modes: (i) standard PVC, (ii) PVC with cryptographically enforced access control policies restricting the servers that may perform a given computation, and (iii) a reversed model of PVC which we call Verifiable Delegable Computation (VDC) where data is held remotely by servers. Entities may dynamically play the role of delegators or servers as required

How to Delegate Computations Publicly

We construct a delegation scheme for all polynomial time computations. Our scheme is publicly verifiable and completely non-interactive in the common reference string (CRS) model. Our scheme is based on an efficiently falsifiable decisional assumption on groups with bilinear maps. Prior to this work, publicly verifiable non-interactive delegation schemes were only known under knowledge assumptions (or in the Random Oracle model) or under non-standard assumptions related to obfuscation or multilinear maps. We obtain our result in two steps. First, we construct a scheme with a long CRS (polynomial in the running time of the computation) by following the blueprint of Paneth and Rothblum (TCC 2017). Then we bootstrap this scheme to obtain a short CRS. Our bootstrapping theorem exploits the fact that our scheme can securely delegate certain non-deterministic computations

On Publicly Verifiable Delegation From Standard Assumptions

We construct a publicly verifiable non-interactive delegation scheme for log-space uniform bounded depth computations in the common reference string (CRS) model, where the CRS is long (as long as the time it takes to do the computation). The soundness of our scheme relies on the assumption that there exists a group with a bilinear map, such that given group elements $g,h,h^t,h^{t^2},$ it is hard to output $g^a,g^b,g^c$ and $h^a,h^b,h^c$ such that $a \cdot t^2 + b \cdot t + c = 0$, but $a,b,c$ are not all zero. Previously, such a result was only known under knowledge assumptions (or in the Random Oracle model), or under non-standard assumptions related to obfuscation or zero-testable homomorphic encryption. We obtain our result by converting the interactive delegation scheme of Goldwasser, Kalai and Rothblum (J. ACM 2015) into a publicly verifiable non-interactive one. As a stepping stone, we give a publicly verifiable non-interactive version of the sum-check protocol of Lund, Fortnow, Karloff, Nisan (J. ACM 1992)

Hybrid Publicly Verifiable Computation

Publicly Verifiable Outsourced Computation (PVC) allows weak devices to delegate computations to more powerful servers, and to verify the correctness of results. Delegation and verification rely only on public parameters, and thus PVC lends itself to large multi-user systems where entities need not be registered. In such settings, individual user requirements may be diverse and cannot be realised with current PVC solutions. In this paper, we introduce Hybrid PVC (HPVC) which, with a single setup stage, provides a flexible solution to outsourced computation supporting multiple modes: (i) standard PVC, (ii) PVC with cryptographically enforced access control policies restricting the servers that may perform a given computation, and (iii) a reversed model of PVC which we call Verifiable Delegable Computation (VDC) where data is held remotely by servers. Entities may dynamically play the role of delegators or servers as required

Interactive certificate for the verification of Wiedemann's Krylov sequence: application to the certification of the determinant, the minimal and the characteristic polynomials of sparse matrices

Certificates to a linear algebra computation are additional data structures for each output, which can be used by a-possibly randomized- verification algorithm that proves the correctness of each output. Wiede-mann's algorithm projects the Krylov sequence obtained by repeatedly multiplying a vector by a matrix to obtain a linearly recurrent sequence. The minimal polynomial of this sequence divides the minimal polynomial of the matrix. For instance, if the $n\times n$ input matrix is sparse with n 1+o(1) non-zero entries, the computation of the sequence is quadratic in the dimension of the matrix while the computation of the minimal polynomial is n 1+o(1), once that projected Krylov sequence is obtained. In this paper we give algorithms that compute certificates for the Krylov sequence of sparse or structured $n\times n$ matrices over an abstract field, whose Monte Carlo verification complexity can be made essentially linear. As an application this gives certificates for the determinant, the minimal and characteristic polynomials of sparse or structured matrices at the same cost

Non-Interactive Publicly-Verifiable Delegation of Committed Programs

In this work, we present the first construction of a fully non-interactive publicly-verifiable delegation scheme for committed programs. More specifically, we consider a setting where Alice is a trusted author who delegates to an untrusted worker the task of hosting a program $P$, represented as a Boolean circuit. Alice also commits to a succinct value based on $P$. Any arbitrary user/verifier without knowledge of $P$ should be convinced that they are receiving from the worker an actual computation of Alice\u27s program on a given input $x$. Before our work, the only object known to imply this challenging form of delegation was a SNARG/SNARK for $\mathcal{NP}$. This is because from the point of view of the user/verifier, the program $P$ is an unknown witness to the computation. However, constructing a SNARG for $\mathcal{NP}$ from standard assumptions remains a major open problem. In our work, we show how to achieve delegation in this challenging context assuming only the hardness of the Learning With Errors (LWE) assumption, bypassing the apparent need for a SNARG for $\mathcal{NP}$

Private Outsourcing of Polynomial Evaluation and Matrix Multiplication using Multilinear Maps

{\em Verifiable computation} (VC) allows a computationally weak client to outsource the evaluation of a function on many inputs to a powerful but untrusted server. The client invests a large amount of off-line computation and gives an encoding of its function to the server. The server returns both an evaluation of the function on the client's input and a proof such that the client can verify the evaluation using substantially less effort than doing the evaluation on its own. We consider how to privately outsource computations using {\em privacy preserving} VC schemes whose executions reveal no information on the client's input or function to the server. We construct VC schemes with {\em input privacy} for univariate polynomial evaluation and matrix multiplication and then extend them such that the {\em function privacy} is also achieved. Our tool is the recently developed {mutilinear maps}. The proposed VC schemes can be used in outsourcing {private information retrieval (PIR)}.Comment: 23 pages, A preliminary version appears in the 12th International Conference on Cryptology and Network Security (CANS 2013