GUPT: Privacy Preserving Data Analysis Made Easy
Summary: GUPT enables privacy-preserving data analysis by external agents without reengineering programs or trusting the analysis code. It provides differential privacy for arbitrary code, resists side-channel leaks, and uses a time-decay data-sensitivity model to allocate privacy levels and boost utility. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Prashanth Mohan (University of California Berkeley)
- 2. Abhradeep Thakurta (Pennsylvania State University; University of California Berkeley)
- 3. Elaine Shi (University of California Berkeley)
- 4. Dawn Song (University of California Berkeley)
- 5. David E. Culler (University of California Berkeley)
BibTeX Citation
@inproceedings{mohan_sigmod12,
title = {{GUPT: Privacy Preserving Data Analysis Made Easy}},
author = {Mohan, Prashanth and Thakurta, Abhradeep and Shi, Elaine and Song, Dawn and Culler, David E.},
series = {{SIGMOD} '12},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2213836.2213876},
url = {https://dl.acm.org/doi/10.1145/2213836.2213876},
year = {2012}
}
Incoming Citations (Sorted by Pagerank)
Showing 12 of 12 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 64 | Privacy Integrated Queries: An Extensible Platform for Privacy-Preserving Data Analysis | 2009 | SIGMOD | 0.00038522486 |
| 67 | An Amateur's Introduction to Recursive Query Processing Strategies | 1986 | SIGMOD | 0.00038009523 |
| 121 | Boosting the Accuracy of Differentially Private Histograms Through Consistency | 2010 | VLDB | 0.00031089378 |
| 609 | Private Analysis of Graph Structure | 2011 | VLDB | 0.00015591971 |
| 625 | Optimizing Linear Counting Queries Under Differential Privacy | 2010 | PODS | 0.00015478765 |
| 732 | Differentially Private Aggregation of Distributed Time-Series with Transformation and Encryption | 2010 | SIGMOD | 0.00014383875 |
| 2,296 | Attacks on Privacy and deFinetti's Theorem | 2009 | SIGMOD | 8.6789249e-05 |
| 2,855 | iReduct: Differential Privacy with Reduced Relative Errors | 2011 | SIGMOD | 7.9391282e-05 |
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