Maxim Zhilyaev

dblp:161/4970 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Privacy and data protection
differential privacy
1.022022
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
0.612022
Differentially Private Histograms in the Shuffle Model from Fake Users · SP 2022
Privacy and data protection › differential privacy › distributed differential privacy
shuffle model
0.612022
Differentially Private Histograms in the Shuffle Model from Fake Users · SP 2022
Privacy and data protection › differential privacy
distributed differential privacy
0.412019
Distributed Differential Privacy via Shuffling · EUROCRYPT (1) 2019
Privacy and data protection › randomization
shuffling
0.412019
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
YearPublicationVenuePosition
2022 Differentially Private Histograms in the Shuffle Model from Fake Users
abstract
There 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
SP2
2019 Distributed Differential Privacy via Shuffling
Albert Cheu, Adam D. Smith 0001, Jonathan R. Ullman, David Zeber, Maxim Zhilyaev
EUROCRYPT (1)5