An Efficient Privacy-preserving Approach for Secure Verifiable Outsourced Computing on Untrusted Platforms

2. Related Works

The wide variety of small computationally weak devices and the growing number of computationally intensive tasks makes the delegation of computation to large data centers a desirable solution. Much research had been done on outsourcing computation, few of them delve into how computation can be outsourced to honest workers while other proposed different ways to verify outsourced computation results from semi-honest workers. However, in some of these outsourcing and verification schemes, users lose direct access to the computational tasks, and may experience possible threats like data privacy and invalidity of results.

For example, Anmin et al. (2018) proposed two efficient algorithms for outsourcing multiple and single composite modular exponentiations. Their proposed scheme remarkably improved checkability by allowing user to discover any misbehavior and inconsistency. The scheme has a subroutine to realize identity-based signatures and identity-based multi-signatures schemes. However, their scheme only checks the integrity of the result not the correctness of the result. Xing and Chunming (2014) proposed a protocol for outsourcing characterization of polynomials and computation of Eigen values of a matrix. The protocol engaged disguise approach for the construction of efficient, verifiable outsource computation scheme without much cryptography assumption. Their scheme is application centric. Also, Kai et al. (2017) proposed a scalable verifiable outsourcing computation protocol for marine cloud computing to combat the problem of low storage and computation associated with the ocean-going vessels. Their protocol allows users who have verification tokens to verify the correctness of the computational results returned by the cloud. They engaged a non-interactive method for the proof of correctness using an oracle random model.