CICI: Data Provenance: Data Quality and Security Evaluation Framework for Mobile Devices Platform

Rochester Institute Of Tech, Rochester NY

Investigators

Abstract

Cyberinfrastructure advancements over the last decade laid a strong foundation for data generation and their communication on a staggering scale, opening up exciting opportunities for setting up a new collaboration between data providers and users, ordinary citizens and government or commercial agencies. Nowadays, one can easily collect data via a mobile device's sensors and use a smartphone to report dangerous (for example, road slickness or construction hazard) conditions to a government agency. But not all data have equal quality, fidelity, and value. The data may originate from a poor quality camera. The high quality sensor data may be maliciously altered during its transfer over a network with low security. This project develops a framework to calculate integral data quality and security (DQS) indicators and provide them to the user along with data itself. This innovation has a high potential to significantly improve a wide spectrum of science and technology applications as it fuses different types of data based on a new quality and security information application. The project builds a proof-of-concept design platform to develop, verify and promote a comprehensive methodology for DQS evaluation. It focuses on the integration of cybersecurity metrics with other diverse metrics, such as accuracy, reliability, timeliness and safety, into a single methodological and technological framework. The framework includes generic data structures and algorithms implementing a DQS evaluation. While the developed evaluation techniques cover a wide range of data sources, from cloud based data systems to embedded sensors, the framework's implementation concentrates on using an ordinary user's Android smartphone. The developed methodologies will be verified by their implementation on the eNeighborhoodWatch platform, an application that not only enables interaction between users and government but also, with the user's permission, perform data collection for scientific studies (e.g. noise pollution, earthquake prediction), facilitating collaboration between thousands of ordinary users with the research community.