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

2024-11-28

International Conference on Internet of Things and Intelligence System

AI Summary

Evaluating the quality of IoT time-series sensor data in smart cities is context-dependent and prone to privacy leakage. Method: This paper proposes a privacy-preserving automated assessment framework, introducing the first “data-blind” Trusted Execution Environment (TEE) architecture. It establishes a dual-dimensional quality measurement framework comprising autonomous time-series performance computation and declarative schema conformance verification. The approach integrates computable quality metrics with schema-driven modeling to enable plug-and-play integration of heterogeneous, multi-source sensors. Results: Experiments on real-world IoT datasets demonstrate high-accuracy quality assessment without exposing raw data, eliminating subjective bias from evaluators. The method ensures objectivity, end-to-end privacy preservation, and cross-type adaptability across diverse sensor modalities and data schemas.

Technology Category

Application Domains: Internet of Things, Sensor Networks & Smart CitiesData Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, TrustMachine Learning: Privacy

Application Category

Security and Privacy: Large-scale security measurementsSystems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterizationSearch and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• IoT Sensor Data
• Quality Assessment
• Privacy Protection

Innovation

• Automated Fair Evaluation
• IoT Sensor Data Quality
• Privacy Protection