Algorithmic Data Minimization for Machine Learning over Internet-of-Things Data Streams

• URL: http://arxiv.org/abs/2503.05675v1
• Date: Fri, 07 Mar 2025 18:35:11 GMT
• Title: Algorithmic Data Minimization for Machine Learning over Internet-of-Things Data Streams
• Authors: Ted Shaowang, Shinan Liu, Jonatas Marques, Nick Feamster, Sanjay Krishnan,
• Abstract summary: Machine learning can analyze vast amounts of data generated by IoT devices to identify patterns, make predictions, and enable real-time decision-making.<n> IoT systems are often deployed in sensitive environments such as households and offices, where they may inadvertently expose identifiable information.<n>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.
• Score: 10.61303879393919
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning can analyze vast amounts of data generated by IoT devices to identify patterns, make predictions, and enable real-time decision-making. By processing sensor data, machine learning models can optimize processes, improve efficiency, and enhance personalized user experiences in smart systems. However, IoT systems are often deployed in sensitive environments such as households and offices, where they may inadvertently expose identifiable information, including location, habits, and personal identifiers. 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.

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