Batch Verifiable Computation of Polynomials on Outsourced Data

Abstract

AbstractSecure outsourcing of computation to cloud servers has attracted much attention in recent years. In a typical outsourcing scenario, the client stores its data on a cloud server and later asks the server to perform computations on the stored data. The verifiable computation (VC) of Gennaro, Gentry, Parno (Crypto 2010) and the homomorphic MAC (HomMAC) of Backes, Fiore, Reischuk (CCS 2013) allow the client to verify the server’s computation with substantially less computational cost than performing the outsourced computation. The existing VC and HomMAC schemes that can be considered practical (do not required heavy computations such as computing fully homomorphic encryptions), are limited to compute linear and quadratic polynomials on the outsourced data. In this paper, we introduce a batch verifiable computation (BVC) model that can be used when the computation of the same function on multiple datasets is required, and construct two schemes for computing polynomials of high degree on the outsourced data. Our schemes allow efficient client verification, efficient server computation, and composition of computation results. Both schemes allow new elements to be added to each outsourced dataset. The second scheme also allows new datasets to be added. A unique feature of our schemes is that the storage required at the server for storing the authentication information, stays the same as the number of outsourced datasets is increased, and so the server storage overhead (the ratio of the server storage to the total size of the datasets) approaches 1. In all existing schemes this ratio is \(\ge 2\). Hence, our BVC can effectively halve the required server storage.KeywordsCloud ServerData ElementCloud StorageSecurity ParameterStorage OverheadThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.