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The Power of the Dinur-Nissim Algorithm: Breaking Privacy of Statistical and Graph Databases

Summary: Extends Dinur–Nissim: phi-weighted random-query attacks (iid φ with finite third moment) can reconstruct Ω(n) entries over much larger domains, preserving the quadratic perturbation–reconstruction tradeoff with stronger guarantees for Gaussian/Poisson/bounded φ. Also gives a matching upper bound for bit databases and extends the attack to recover many graph edges from subgraph statistics, delineating limits and new application domains. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1566
Venue
PODS
Year
2012
Pagerank
4.1945683e-05
Overall Rank
12,104 | 15.80%
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
40 Privacy-Preserving Data Mining 2000 SIGMOD 0.00074232718
136 Revealing Information while Preserving Privacy 2003 PODS 0.0004241101
2,577 Simulatable Auditing 2005 PODS 8.5099821e-05
3,258 Towards Robustness in Query Auditing 2006 VLDB 7.3150323e-05
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