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)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 40 | Privacy-Preserving Data Mining | 2000 | SIGMOD | 0.00074213516 |
| 137 | Revealing Information while Preserving Privacy | 2003 | PODS | 0.00042381562 |
| 2,579 | Simulatable Auditing | 2005 | PODS | 8.5010694e-05 |
| 3,264 | Towards Robustness in Query Auditing | 2006 | VLDB | 7.3073465e-05 |
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