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

arXiv: https://arxiv.org/abs/2503.05675 · HTML: https://mlsanity.com/paper/2503.05675/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 50 references that are in the map.
- First posted on arXiv 7 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [NetLLM: Adapting Large Language Models for Networking](https://mlsanity.com/paper/2402.02338/) (2024)
- [IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT](https://mlsanity.com/paper/1611.04880/) (2016)
- [New Directions in Automated Traffic Analysis](https://mlsanity.com/paper/2008.02695/) (2020)
- [netFound: Principled Design for Network Foundation Models](https://mlsanity.com/paper/2310.17025/) (2023)
- [Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures](https://mlsanity.com/paper/2205.04713/) (2022)
- [Measuring the Performance and Network Utilization of Popular Video Conferencing Applications](https://mlsanity.com/paper/2105.13478/) (2021)
- [LEAF: Navigating Concept Drift in Cellular Networks](https://mlsanity.com/paper/2109.03011/) (2021)
- [The Data Minimization Principle in Machine Learning](https://mlsanity.com/paper/2405.19471/) (2024)
- [AC-DC: Adaptive Ensemble Classification for Network Traffic Identification](https://mlsanity.com/paper/2302.11718/) (2023)
- [Privacy-Preserving Feature Selection with Fully Homomorphic Encryption](https://mlsanity.com/paper/2110.05088/) (2021)
- [CATO: End-to-End Optimization of ML-Based Traffic Analysis Pipelines](https://mlsanity.com/paper/2402.06099/) (2024)
- [EdgeServe: A Streaming System for Decentralized Model Serving](https://mlsanity.com/paper/2303.08028/) (2023)

## Abstract (arXiv)

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.