Concurrent Composition for Differentially Private Continual Mechanisms
Summary: First general concurrent composition for continual DP under adaptive, interleaved queries and dataset updates, supporting event- and user-level neighboring. Characterizes when parallel composition survives updates—and its failure for approximate DP—with modular extensions to Rényi/f-DP and interactive LDP. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Monika Henzinger (Institute of Science and Technology)
- 2. Roodabeh Safavi (Institute of Science and Technology)
- 3. Salil Vadhan (Harvard University)
BibTeX Citation
@inproceedings{henzinger_pods26,
address = {New York, NY, USA},
series = {{PODS} '26},
title = {{Concurrent Composition for Differentially Private Continual Mechanisms}},
url = {https://dl.acm.org/doi/10.1145/3801895},
doi = {10.1145/3801895},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Henzinger, Monika and Safavi, Roodabeh and Vadhan, Salil},
year = {2026}
}
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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 |
|---|---|---|---|---|
| 2,012 | Understanding the Sparse Vector Technique for Differential Privacy | 2017 | VLDB | 9.3073552e-05 |
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