CORE
🇺🇦Â
 make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
Tight Data Access Bounds for Private Top-
k
k
k
Selection
Authors
Olga Ohrimenko
Anthony Wirth
Hao Wu
Publication date
30 May 2023
Publisher
View
on
arXiv
Abstract
We study the top-
k
k
k
selection problem under the differential privacy model:
m
m
m
items are rated according to votes of a set of clients. We consider a setting in which algorithms can retrieve data via a sequence of accesses, each either a random access or a sorted access; the goal is to minimize the total number of data accesses. Our algorithm requires only
O
(
m
k
)
O(\sqrt{mk})
O
(
mk
​
)
expected accesses: to our knowledge, this is the first sublinear data-access upper bound for this problem. Our analysis also shows that the well-known exponential mechanism requires only
O
(
m
)
O(\sqrt{m})
O
(
m
​
)
expected accesses. Accompanying this, we develop the first lower bounds for the problem, in three settings: only random accesses; only sorted accesses; a sequence of accesses of either kind. We show that, to avoid
Ω
(
m
)
\Omega(m)
Ω
(
m
)
access cost, supporting *both* kinds of access is necessary, and that in this case our algorithm's access cost is optimal
Similar works
Full text
Available Versions
arXiv.org e-Print Archive
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:arXiv.org:2301.13347
Last time updated on 28/02/2023