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A Framework for Adversarially Robust Streaming Algorithms

Summary: Framework to convert space-efficient randomized insertion-only streaming algorithms into adversarially robust (1+ε)-approx algorithms against adaptively chosen streams. Applies to distinct elements, F_p (estimation/heavy hitters), entropy, matching space of non-robust algorithms up to poly(log n,1/ε) factors via generic transformation tools. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1789
Venue
PODS
Year
2020
Pagerank
6.2194225e-05
Overall Rank
4,403 | 69.38%
DOI
10.1145/3375395.3387658

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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
383 An Optimal Algorithm for the Distinct Elements Problem 2010 PODS 0.00024820873
588 Practical Skew Handling in Parallel Joins 1992 VLDB 0.00019604754
1,040 Graph Sketches: Sparsification, Spanners, and Subgraphs 2012 PODS 0.00014488943
2,884 BPTree: an ℓ2 Heavy Hitters Algorithm Using Constant Memory 2017 PODS 7.9620506e-05
4,172 The Adversarial Robustness of Sampling 2020 PODS 6.3879072e-05
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