Year: 2025
Type: article
Abstract: Machine learning can analyze vast amounts of data generated by IoT devices to identify patterns, make predictions, and enable real-time decision-making. This raises significant privacy concerns, necessitating the application of data minimization - a foundational principle in emerging data regulations, which mandates that service providers only collect data that is directly relevant and necessary for a specified purpose. Despite its importance, data minimization lacks a precise technical definition in the context of sensor data, where collections of weak signals make it challenging to apply a binary "relevant and necessary" rule. This paper provides a technical interpretation of data minimization in the context of sensor streams, explores practical methods for implementation, and addresses the challenges involved. Through our approach, we demonstrate that our framework can reduce user identifiability by up to 16.7% while maintaining accuracy loss below 1%, offering a viable path toward privacy-preserving IoT data processing.
Institutions: University of Chicago, University of Hong Kong
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7118538140