PrivData Network: A Privacy-Preserving On-Chain Data Factory and Trading Market

Abstract

Privacy concerns often raise when sensitive data are collected, traded, and processed. The data owner typically loses her ultimate control of the data after data-outsourcing. In this work, we present the PrivData Network – a community-controlled privacy-preserving data factory and trading market. It can be viewed as a standalone data ecosystem that enables privacy-preserving data-driven workflows in a controlled environment for orchestrating and automating data movement and data transformation. In particular, we design a data encapsulation mechanism with privacy assurance, which can guarantee data privacy, usage policy compliance and metadata validity. We also design a privacy policy language and utilize a static analysis library that transfers the program to the defined policy language. To ensure the correctness of data processing, we propose a publicly verifiable secure multiparty computation protocol for mixed circuits, which guarantees the output correctness even if all parties are corrupted. Its online efficiency is comparable to conventional semi-honest secret-sharing-based MPC schemes. Finally, we implemented a prototype of our system in C++ and benchmark it on various tasks, such as biometric matching, logistic regression, and decision trees, etc.