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Pan-private Algorithms Via Statistics on Sketches

Summary: First pan-private algorithms for fully dynamic streaming tasks (distinct counts, frequency moments, heavy hitters) by adapting sketches—calibrating noise to sketch projections, maintaining statistics on sketches, and designing new sketches. Also gives the first pan-privacy lower bounds via a noisy-decoding/sanitization connection, nearly matching the upper bounds and strictly stronger than bounds from differential privacy or streaming alone. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1535
Venue
PODS
Year
2011
Pagerank
7.9474698e-05
Overall Rank
2,894 | 79.87%
DOI
-

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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
383 An Optimal Algorithm for the Distinct Elements Problem 2010 PODS 0.00024820873
3,050 Comparing Data Streams Using Hamming Norms (How to Zero In) 2002 VLDB 7.6512619e-05
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