Privacy in Data Systems

Summary: Perturb individual records and reconstruct original value distributions to build decision-tree classifiers and enable association-rule mining with accuracy comparable to using raw data. Introduce “Hippocratic databases” — principles, challenges, and solution directions for embedding privacy responsibility into database systems. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 1277
Venue: PODS
Year: 2003
Pagerank: 4.1905499e-05
Overall Rank: 12,625 | 12.26%
DOI: -

Incoming Non-self Citations Over Time

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Authors

1. Rakesh Agrawal

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
40	Privacy-Preserving Data Mining	2000	SIGMOD	0.00074213516
148	On the Design and Quantification of Privacy Preserving Data Mining Algorithms	2001	PODS	0.00041196325
357	Hippocratic Databases	2002	VLDB	0.00026062775
1,853	Information Sharing Across Private Databases	2003	SIGMOD	0.00010323434
2,401	Watermarking Relational Databases	2002	VLDB	8.8782049e-05

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6,480	Differential Privacy in Data Publication and Analysis	2012	SIGMOD	5.0396886e-05
12,168	Database Access Control & Privacy: Is There A Common Ground?	2011	CIDR	4.1905499e-05
4,038	Differential Privacy in Telco Big Data Platform	2015	VLDB	6.5067029e-05
357	Hippocratic Databases	2002	VLDB	0.00026062775
177	Limiting Privacy Breaches in Privacy Preserving Data Mining	2003	PODS	0.00037858416
559	Maintaining Data Privacy in Association Rule Mining	2002	VLDB	0.0002012549
148	On the Design and Quantification of Privacy Preserving Data Mining Algorithms	2001	PODS	0.00041196325
40	Privacy-Preserving Data Mining	2000	SIGMOD	0.00074213516