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

TL;DR

This paper introduces a privacy-preserving framework for objective data quality assessment of IoT sensor time-series data in smart cities, ensuring privacy and adaptability.

Contribution

It presents a novel, automated, privacy-preserving data quality assessment method using custom metrics and trusted execution environments for IoT data.

Findings

Effective privacy preservation in data quality assessment

Objective metrics for temporal data performance

Enhanced adaptability across data types

Abstract

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.