Models and Methods for Privacy-Preserving Data Publishing and Analysis (Invited Tutorial)
Summary: Survey of models and methods reconciling data sharing and confidentiality: formal privacy/disclosure definitions and algorithmic techniques for safe publishing and analysis. Emphasizes theory and practical enforcement to balance utility against disclosure risk. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Johannes Gehrke (Cornell University)
BibTeX Citation
@inproceedings{gehrke_pods05,
address = {New York, NY, USA},
series = {{PODS} '05},
title = {{Models and Methods for Privacy-Preserving Data Publishing and Analysis (Invited Tutorial)}},
url = {https://dl.acm.org/doi/10.1145/1065167.1065207},
doi = {10.1145/1065167.1065207},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Gehrke, Johannes},
year = {2005}
}
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