Local Differentially Private Heavy Hitter Detection in Data Streams with Bounded Memory
Summary: HG-LDP jointly optimizes local differential privacy, bounded memory, and Top-k accuracy for streaming heavy hitters. It introduces three LDP randomization schemes for large domains under bounded memory, achieving 2300x memory savings at 41,270 and favorable privacy/accuracy tradeoffs; code released. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xiaochen Li
- 2. Weiran Liu
- 3. Jian Lou
- 4. Yuan Hong
- 5. Lei Zhang
- 6. Zhan Qin
- 7. Kui Ren
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,664 | Federated Heavy Hitter Analytics with Local Differential Privacy | 2025 | SIGMOD | 6.1962536e-05 |
| 9,162 | SPAS: Continuous Release of Data Streams under w-Event Differential Privacy | 2025 | SIGMOD | 5.371468e-05 |
| 10,153 | Defense against Poisoning Attacks under Shuffle-DP | 2026 | SIGMOD | 5.1725247e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 17 of 17 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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