Time Series Compressibility and Privacy
Summary: Explores the trade-off between time-series compressibility and privacy, formalizing perturbations that mimic original data to avoid detection and preserve structure. Real-data experiments and streaming-ready schemes illustrate practical trade-offs and on-the-fly data hiding with a tunable leakage-utility balance. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Spiros Papadimitriou (IBM)
- 2. Feifei Li (Boston University)
- 3. George Kollios (Boston University)
- 4. Philip S. Yu (IBM)
BibTeX Citation
@article{papadimitriou_vldb07,
title = {{Time Series Compressibility and Privacy}},
author = {Papadimitriou, Spiros and Li, Feifei and Kollios, George and Yu, Philip S.},
journal = {PVLDB},
series = {{VLDB} '07},
volume = {30},
pages = {459--470},
year = {2007}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 757 | Differentially Private Aggregation of Distributed Time-Series with Transformation and Encryption | 2010 | SIGMOD | 0.00014306168 |
| 7,608 | Uncertain Time-Series Similarity: Return to the Basics | 2012 | VLDB | 5.5845913e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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