标题： 隐私保护的时间序列物联网传感器数据质量评估 显示英文标题

标题： Privacy-Preserving Data Quality Assessment for Time-Series IoT Sensors

摘要： 物联网（IoT）传感器的数据已成为各种领域决策过程中的关键贡献者。 然而，数据的质量对于基于其构建的应用程序的有效性至关重要，而数据质量的评估高度依赖于上下文。 此外，在敏感数据普遍存在的领域中，数据质量评估过程中保护数据隐私至关重要。 本文提出了一种新颖的框架，用于对部署在智慧城市中的物联网传感器的时间序列数据进行自动化、客观且保护隐私的数据质量评估。 我们利用自定义的、可自主计算的指标，通过参数化时间性能和对声明性模式文档的遵循程度来实现客观性。 此外，我们使用可信执行环境创建一个“数据盲”模型，以确保个体隐私，消除评估者偏差，并提高跨数据类型的适应性。 本文描述了针对物联网传感器的数据质量评估方法，强调其在智慧城市背景下的相关性，同时应对广泛数据收集实践带来的隐私需求增长。 显示英文摘要

摘要： Data from Internet of Things (IoT) sensors has emerged as a key contributor to decision-making processes in various domains. However, the quality of the data is crucial to the effectiveness of applications built on it, and assessment of the data quality is heavily context-dependent. Further, preserving the privacy of the data during quality assessment is critical in domains where sensitive data is prevalent. This paper proposes a novel framework for automated, objective, and privacy-preserving data quality assessment of time-series data from IoT sensors deployed in smart cities. We leverage custom, autonomously computable metrics that parameterise the temporal performance and adherence to a declarative schema document to achieve objectivity. Additionally, we utilise a trusted execution environment to create a "data-blind" model that ensures individual privacy, eliminates assessee bias, and enhances adaptability across data types. This paper describes this data quality assessment methodology for IoT sensors, emphasising its relevance within the smart-city context while addressing the growing need for privacy in the face of extensive data collection practices.