Randomize the Future: Asymptotically Optimal Locally Private Frequency Estimation Protocol for Longitudinal Data
Summary: Introduces an online LDP frequency-estimation protocol for longitudinal binary data with error O((1/ε)·log d·√(k n log(d/β))), removing prior linear-in-k dependence and matching the lower bound up to log factors. Key novelty: FutureRand, a randomizer that correlates noise across nonzeros and leverages input-space symmetry precomputation to produce on-the-fly outputs without future knowledge, closing the online/offline error gap. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Olga Ohrimenko (University of Melbourne)
- 2. Anthony Wirth (University of Melbourne)
- 3. Hao Wu (University of Melbourne)
BibTeX Citation
@inproceedings{ohrimenko_pods22,
address = {New York, NY, USA},
series = {{PODS} '22},
title = {{Randomize the Future: Asymptotically Optimal Locally Private Frequency Estimation Protocol for Longitudinal Data}},
url = {https://dl.acm.org/doi/10.1145/3517804.3526226},
doi = {10.1145/3517804.3526226},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Ohrimenko, Olga and Wirth, Anthony and Wu, Hao},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,130 | Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections | 2024 | PODS | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,678 | Heavy Hitters and the Structure of Local Privacy | 2018 | PODS | 0.00010032135 |
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