Algorithmic Data Minimization for Machine Learning over Internet-of-Things Data Streams
Summary: Defines data minimization for weak-signal IoT streams algorithmically, rather than via binary relevance rules. Selective stream processing reduces user identifiability by up to 16.7% while keeping ML accuracy loss below 1%. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Ted Shaowang (University of Chicago)
- 2. Shinan Liu (University of Hong Kong)
- 3. Jonatas Marques (University of Chicago)
- 4. Nick Feamster (University of Chicago)
- 5. Sanjay Krishnan (University of Chicago)
BibTeX Citation
@article{shaowang_vldb25,
title = {{Algorithmic Data Minimization for Machine Learning over Internet-of-Things Data Streams}},
author = {Shaowang, Ted and Liu, Shinan and Marques, Jonatas and Feamster, Nick and Krishnan, Sanjay},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {13},
pages = {5652--5661},
doi = {10.14778/3773731.3773740},
url = {https://doi.org/10.14778/3773731.3773740},
year = {2025}
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,608 | Apache IoTDB: Time-series Database for Internet of Things | 2020 | VLDB | 0.00010227331 |
| 4,436 | Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures | 2023 | VLDB | 6.7069035e-05 |
| 7,503 | Disclosure-Compliant Query Answering | 2024 | SIGMOD | 5.6029996e-05 |
| 9,566 | Declarative Data Serving: The Future of Machine Learning Inference on the Edge | 2021 | VLDB | 5.2528121e-05 |
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