Rhodes, Greece. September 12-18, 2020.
ISSN: 2334-1033
ISBN: 978-0-9992411-7-2
Copyright © 2020 International Joint Conferences on Artificial Intelligence Organization
We study the design of data publishing mechanisms
that allow a collection of autonomous distributed datasources to collaborate to support queries.
A common mechanism for data publishing is via views: functions that expose
derived data to users, usually specified as declarative queries. Our autonomy assumption is
that the views must be on individual sources, but with the intention of supporting integrated
queries.
In deciding what data to expose to users, two considerations must be balanced. The views must be sufficiently expressive
to support queries that users want to ask -- the utility of the publishing mechanism.
But there may also be some expressiveness restriction. Here we consider two restrictions,
a minimal information requirement, saying that the views should reveal as little as possible while supporting the utility
query, and a non-disclosure requirement, formalizing the need to prevent external users from computing information that data owners do not want revealed.
We investigate the problem of designing views that satisfy both an expressiveness and an inexpressiveness requirement,
for views in a restricted declarative language
(conjunctive queries), and for arbitrary views.