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Universally Optimal Privacy Mechanisms for Minimax Agents

Summary: Proves the geometric mechanism is universally optimal for all minimax (risk-averse) information consumers for any fixed count query, generalizing prior Bayesian-only optimality to a broader consumer class. Also yields collusion-resistant multi-level privacy releases when agents rationally combine mechanism output with side information. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1514
Venue
PODS
Year
2010
Pagerank
4.4196402e-05
Overall Rank
8,965 | 37.64%
DOI
-

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
6,691 Information Preservation in Statistical Privacy and Bayesian Estimation of Unattributed Histograms 2013 SIGMOD 4.9613269e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

Rank Cited Paper Year Venue Pagerank
136 Revealing Information while Preserving Privacy 2003 PODS 0.0004241101
178 Boosting the Accuracy of Differentially Private Histograms Through Consistency 2010 VLDB 0.00037697111
4,794 Optimal Random Perturbation at Multiple Privacy Levels 2009 VLDB 5.9161511e-05
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