Privacy-protected denoising for signals on graphs from distributed systems

The fast-growing networked computing devices create many distributed systems and generate new signals on a large scale. Typical applications include peer-to-peer streaming of multimedia data, crowdsourcing, and measurement by sensor networks. Therefore, the massive amount of networked data is a form of big data, calling for new data structures and algorithms different from classical ones suitable for small data sizes. We consider a vital data format for recording information from networked distributed systems: signals on graphs. A significant concern is to protect the privacy of large scales of signals when processed at third parties, such as cloud data centers. A de-facto solution is to outsource encrypted data before they arrive at the third-parties. We propose a novel and efficient privacy-protected outsourced denoising algorithm based on the information-theoretic secure multi-party computation (secure MPC). Among the operations of signals on graphs, denoising is useful before further meaningful processing can occur. We experiment with our algorithms in a popular platform of secure MPC and compare it with Paillier's homomorphic encryption approach. The results demonstrate a better efficiency of our approach.