A privacy-preserving statistics marketplace using local differential privacy and blockchain: An application to smart-grid measurements sharing
Nikos Fotiou, Iakovos Pittaras, Vasilios A. Siris, George C. Polyzos, Priit Anton
Abstract
Service providers usually require detailed statistics in order to improve their services. On the other hand, privacy concerns are intensifying and sensitive data is protected by legislation, such as GDPR (General Data Protection Regulation). In this paper, we present the design, implementation, and evaluation of a marketplace that allows “data consumers” to buy information from “data providers”, which can then be used for generating meaningful statistics. Additionally, our system enables “system operators” that can select which data providers are allowed to provide data, based on filtering criteria specified by the data consumer. We leverage local differential privacy to protect the data provider's privacy against data consumers, as well as against system operators, and we build a blockchain-based solution for ensuring fair exchange, and immutable data logs. Our design targets use cases that involve hundreds or even thousands of data providers. We prove the feasibility of our approach through a proof-of concept implementation of a measurement sharing application for smart-grid systems.