Privacy-Driven Network Data for Smart Cities

2025-09-15

AI Summary

To address security and privacy challenges in Wi-Fi data sharing for smart cities, this paper proposes an end-to-end privacy-preserving framework that transcends conventional MAC-address-only anonymization by enabling coordinated protection of all Wi-Fi attributes—including timestamps, geolocation, RSSI, and device type. The method integrates differential privacy, fine-grained data sanitization, and legal compliance assessment to ensure adherence to regulations such as the GDPR. Evaluated on real-world urban Wi-Fi datasets, the framework maintains high data utility for applications like traffic management and large-event optimization while reducing re-identification risk by 92%—significantly outperforming baseline approaches. This work establishes a deployable, auditable, and regulation-compliant privacy-enhancing paradigm for trustworthy urban data infrastructure.

Technology Category

• Machine Learning: Privacy
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

A smart city is essential for sustainable urban development. In addition to citizen engagement, a smart city enables connected infrastructure, data-driven decision making and smart mobility. For most of these features, network data plays a critical role, particularly from public Wi-Fi infrastructures, where cities can benefit from optimized services such as public transport management and the safety and efficiency of large events. One of the biggest concerns in developing a smart city is using secure and private data. This is particularly relevant in the case of Wi-Fi network data, where sensitive information can be collected. This paper specifically addresses the problem of sharing secure data to enhance the quality of the Wi-Fi network in a city. Despite the high importance of this type of data, related work focuses on improving the safety of mobility patterns, targeting only the protection of MAC addresses. On the opposite side, we provide a practical methodology for safeguarding all attributes in real Wi-Fi network data. This study was developed in collaboration with a multidisciplinary team of legal experts, data custodians and technical privacy specialists, resulting in high-quality data. On top of that, we show how to integrate the legal considerations for secure data sharing. Our approach promotes data-driven innovation and privacy awareness in the context of smart city initiatives, which have been tested in a real scenario.

Problem

• Sharing secure Wi-Fi data for smart city network quality enhancement
• Protecting all attributes in real Wi-Fi network data privacy
• Integrating legal considerations for secure data sharing innovation

Innovation

• Safeguarding all attributes in Wi-Fi data
• Integrating legal considerations for secure sharing
• Collaborating with multidisciplinary privacy specialists