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

过去十年来，网络基础设施的进步为数据生成及其大规模通信奠定了坚实的基础，为数据提供商和用户、普通公民和政府或商业机构之间建立新的合作提供了令人兴奋的机会。 如今，人们可以通过移动终端的传感器轻松收集数据，并使用智能手机向政府机构报告危险（例如，道路光滑或施工危险）状况。但并非所有数据都具有相同的质量、保真度和价值。数据可能来自质量差的相机。高质量的传感器数据在其通过具有低安全性的网络传输期间可能被恶意地改变。该项目开发了一个框架来计算完整的数据质量和安全（DQS）指标，并将其与数据本身一起沿着提供给用户。这项创新具有极大的潜力，可以显著改善广泛的科学和技术应用，因为它基于新的质量和安全信息应用融合了不同类型的数据。该项目建立了一个概念验证设计平台，以开发，验证和推广DQS评估的综合方法。它侧重于将网络安全指标与准确性、可靠性、及时性和安全性等其他不同指标整合到一个单一的方法和技术框架中。该框架包括实现DQS评估的通用数据结构和算法。虽然开发的评估技术涵盖了广泛的数据源，从基于云的数据系统到嵌入式传感器，但该框架的实现集中在使用普通用户的Android智能手机上。将通过在eNeighborhoodWatch平台上实施这些方法来验证所制定的方法，该平台是一个应用程序，不仅使用户和政府之间能够进行互动，而且在用户许可的情况下，为科学研究（例如噪音污染、地震预测）收集数据，促进成千上万的普通用户与研究界之间的合作。

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.