Algorithmic Data Minimization for Machine Learning over Internet-of-Things Data Streams
Summary: Defines a formal, algorithmic notion of data minimization for IoT sensor streams and proposes practical stream-side techniques to remove identifying signal components while preserving task utility. Shows up to 16.7% reduction in user identifiability with <1% model accuracy loss. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Ted Shaowang
- 2. Shinan Liu
- 3. Jonatas Marques
- 4. Nick Feamster
- 5. Sanjay Krishnan
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,921 | Apache IoTDB: Time-series Database for Internet of Things | 2020 | VLDB | 0.00010082827 |
| 4,687 | Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures | 2023 | VLDB | 5.9986055e-05 |
| 6,898 | Disclosure-Compliant Query Answering | 2024 | SIGMOD | 4.8925595e-05 |
| 9,415 | Declarative Data Serving: The Future of Machine Learning Inference on the Edge | 2021 | VLDB | 4.3441378e-05 |
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