Privacy Preservation of Aggregates in Hidden Databases: Why and How?

Summary: Hidden databases with form-like interfaces; protecting sensitive aggregates from leakage through individual tuple results. Techniques to thwart bot sampling to infer aggregates; theoretical analysis plus extensive experiments demonstrate effectiveness. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: h7c3839bbc26dae4c
Venue: SIGMOD
Year: 2009
Pagerank: 4.9605343e-05
Overall Rank: 12,834 | 13.98%
DOI: 10.1145/1559845.1559863

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Arjun Dasgupta (University of Texas)
2. Nan Zhang (George Washington University)
3. Gautam Das (University of Texas)
4. Surajit Chaudhuri (Microsoft)

BibTeX Citation

@inproceedings{dasgupta_sigmod09,
        title = {{Privacy Preservation of Aggregates in Hidden Databases: Why and How?}},
        author = {Dasgupta, Arjun and Zhang, Nan and Das, Gautam and Chaudhuri, Surajit},
        series = {{SIGMOD} '09},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1559845.1559863},
        url = {https://dl.acm.org/doi/10.1145/1559845.1559863},
        year = {2009}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
8,861	Unbiased Estimation of Size and Other Aggregates Over Hidden Web Databases	2010	SIGMOD	5.2477452e-05
12,649	Aggregate Suppression for Enterprise Search Engines	2012	SIGMOD	4.9605343e-05
12,724	Randomized Generalization for Aggregate Suppression Over Hidden Web Databases	2011	VLDB	4.9605343e-05

Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
69	Privacy-Preserving Data Mining	2000	SIGMOD	0.00037815735
88	WebTables: Exploring the Power of Tables on the Web	2008	VLDB	0.00035500667
840	Minimal Probing: Supporting Expensive Predicates for Top-k Queries	2002	SIGMOD	0.00013521251
1,101	Privacy Preserving OLAP	2005	SIGMOD	0.00011995659
1,519	Distributed Search over the Hidden Web: Hierarchical Database Sampling and Selection	2002	VLDB	0.0001036122
1,584	Google’s Deep-Web Crawl	2008	VLDB	0.00010136933
1,880	Information Sharing Across Private Databases	2003	SIGMOD	9.427621e-05
2,457	Simulatable Auditing	2005	PODS	8.4172033e-05
3,370	Towards Robustness in Query Auditing	2006	VLDB	7.3503641e-05
5,996	A Random Walk Approach to Sampling Hidden Databases	2007	SIGMOD	5.9037651e-05

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5	9,865	Aggregate Estimation Over Dynamic Hidden Web Databases	2014	VLDB
6	9,898	Optimal Algorithms for Crawling a Hidden Database in the Web	2012	VLDB
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8	12,649	Aggregate Suppression for Enterprise Search Engines	2012	SIGMOD
9	8,861	Unbiased Estimation of Size and Other Aggregates Over Hidden Web Databases	2010	SIGMOD
10	12,724	Randomized Generalization for Aggregate Suppression Over Hidden Web Databases	2011	VLDB