Winnowing: Local Algorithms for Document Fingerprinting
Summary: Introduces local document fingerprinting—algorithms guaranteed to detect copies in large corpora. Proves a lower bound for any local approach and presents winnowing, an efficient method within 33% of that bound; validates on Web data and discusses MOSS usage. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Saul Schleimer (University of Illinois Chicago)
- 2. Daniel S. Wilkerson (University of California Berkeley)
- 3. Alex Aiken (University of California Berkeley)
BibTeX Citation
@inproceedings{schleimer_sigmod03,
title = {{Winnowing: Local Algorithms for Document Fingerprinting}},
author = {Schleimer, Saul and Wilkerson, Daniel S. and Aiken, Alex},
series = {{SIGMOD} '03},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/872757.872770},
url = {https://dl.acm.org/doi/10.1145/872757.872770},
year = {2003}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
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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 |
|---|---|---|---|---|
| 1,284 | Copy Detection Mechanisms for Digital Documents | 1995 | SIGMOD | 0.00011332822 |
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