Continual Observation of Joins under Differential Privacy
Summary: Differentially private continual-observation mechanism for arbitrary join queries/predicates, extending beyond prior graph-pattern-only work. Key novelty: no predeclared degree/frequency bounds; error adapts to the current instance’s max degree/frequency, yielding instance-specific utility over infinite streams. (summarized by gpt-5.4-mini on May 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Wei Dong
- 2. Zijun Chen
- 3. Qiyao Luo
- 4. Elaine Shi
- 5. Ke Yi
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,041 | A General Framework for Per-record Differential Privacy | 2026 | SIGMOD | 4.1945683e-05 |
| 10,094 | N2E: A General Framework to Reduce Node-Differential Privacy to Edge-Differential Privacy for Graph Analytics | 2026 | SIGMOD | 4.1945683e-05 |
| 10,101 | Privacy-preserving and Verifiable Causal Prescriptive Analytics | 2026 | SIGMOD | 4.1945683e-05 |
| 10,480 | Efficient and Accurate Differentially Private Cardinality Continual Releases | 2025 | SIGMOD | 4.1945683e-05 |
| 11,112 | DOP-SQL: A General-purpose, High-utility, and Extensible Private SQL System | 2024 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 19 of 19 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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