VLDB 2026 Research / reviewers in the wild / expert
Maxim Zhilyaev
dblp:161/4970
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 2 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
2 papers |
Privacy and data protection · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Privacy and data protection
differential privacy |
1.0 | 2 | 2022 | Differentially Private Histograms in the Shuffle Model from Fake Users · SP 2022 Distributed Differential Privacy via Shuffling · EUROCRYPT (1) 2019 |
Privacy and data protection › privacy-preserving data analysis
histogram estimation |
0.6 | 1 | 2022 | Differentially Private Histograms in the Shuffle Model from Fake Users · SP 2022 |
Privacy and data protection › differential privacy › distributed differential privacy
shuffle model |
0.6 | 1 | 2022 | Differentially Private Histograms in the Shuffle Model from Fake Users · SP 2022 |
Privacy and data protection › differential privacy
distributed differential privacy |
0.4 | 1 | 2019 | Distributed Differential Privacy via Shuffling · EUROCRYPT (1) 2019 |
Privacy and data protection › randomization
shuffling |
0.4 | 1 | 2019 | Distributed Differential Privacy via Shuffling · EUROCRYPT (1) 2019 |
Methods — techniques the papers use, named apart from their topics
randomization · 0.6fake users · 0.6differential privacy · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Differentially Private Histograms in the Shuffle Model from Fake UsersabstractThere has been much recent work in the shuffle model of differential privacy, particularly for approximate d-bin histograms. While these protocols achieve low error, the number of messages sent by each user—the message complexity—has so far scaled with d or the privacy parameters. The message complexity is an informative predictor of a shuffle protocol’s resource consumption. We present a protocol whose message complexity is two when there are sufficiently many users. The protocol essentially pairs each row in the dataset with a fake row and performs a simple randomization on all rows. We show that the error introduced by the protocol is small, using rigorous analysis as well as experiments on real-world data. We also prove that corrupt users have a relatively low impact on our protocol’s estimates. Albert Cheu, Maxim Zhilyaev |
SP | 2 |
| 2019 | Distributed Differential Privacy via Shuffling
Albert Cheu, Adam D. Smith 0001, Jonathan R. Ullman, David Zeber, Maxim Zhilyaev |
EUROCRYPT (1) | 5 |