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Deriving Private Information from Randomized Data

Summary: Correlations drive leakage in randomized data release; PCA and Bayes reconstructions quantify disclosure risk. Proposes correlated-noise randomization; increasing data-noise similarity reduces reconstruction accuracy; experiments validate privacy gains. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3684
Venue
SIGMOD
Year
2005
Pagerank
6.6318893e-05
Overall Rank
4,559 | 68.73%
DOI
10.1145/1066157.1066163

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{huang_sigmod05,
        title = {{Deriving Private Information from Randomized Data}},
        author = {Huang, Zhengli and Du, Wenliang and Chen, Biao},
        series = {{SIGMOD} '05},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1066157.1066163},
        url = {https://dl.acm.org/doi/10.1145/1066157.1066163},
        year = {2005}
}

Incoming Citations (Sorted by Pagerank)

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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
70 Privacy-Preserving Data Mining 2000 SIGMOD 0.0003804755
218 Limiting Privacy Breaches in Privacy Preserving Data Mining 2003 PODS 0.00024420564
244 On the Design and Quantification of Privacy Preserving Data Mining Algorithms 2001 PODS 0.00023476901
1,217 Maintaining Data Privacy in Association Rule Mining 2002 VLDB 0.00011627624
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