Trust-aware and incentive-based offloading scheme for secure multi-party computation in Internet of Things

Abstract

Adoption of multi-party computation in IoT provides the required processing power needed by the IoT devices to provide smart services in the shortest time. However, this requires a secure offloading scheme that is capable of fairly offloading the computations of source nodes to a different set of workers, guarantees the privacy of the source nodes, and verifies the correctness of results without a third party at a low overhead.In this paper, we formulate a secure privacy-preserving offloading scheme based on modified secret sharing to offload computations and data to a different set of workers. We also develop incentive and trust models to encourage honesty and willingness and discourage delay among workers during multi-party computation. Last, we develop a low overhead morphism-based verification technique for the self-verification of the correctness of results. We finally present the security analysis of the scheme which shows that the schemes meet up with the necessary security requirements, and the experimental results show the capability of the scheme in terms of its security functionalities, low computation cost, effective verification of results, and generation of incentives and trust values for workers during multi-party computation.