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Linked data privacy

Published online by Cambridge University Press:  18 March 2015

SVETLANA JAKŠIĆ ,
JOVANKA PANTOVIĆ  and
SILVIA GHILEZAN
SVETLANA JAKŠIĆ
Affiliation:
University of Novi Sad, Trg Dositeja Obradovica 6, Novi Sad, Serbia
JOVANKA PANTOVIĆ
Affiliation:
University of Novi Sad, Trg Dositeja Obradovica 6, Novi Sad, Serbia
SILVIA GHILEZAN
Affiliation:
University of Novi Sad, Trg Dositeja Obradovica 6, Novi Sad, Serbia
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Abstract

Web of Linked Data introduces common format and principles for publishing and linking data on the Web. Such a network of linked data is publicly available and easily consumable. This paper introduces a calculus for modelling networks of linked data with encoded privacy preferences.

In that calculus, a network is a parallel composition of users, where each user is named and consists of data, representing the user's profile, and a process. Data is a parallel composition of triples with names (resources) as components. Associated with each name and each triple of names are their privacy protection policies, that are represented by queries. A data triple is accessible to a user if the user's data satisfies the query assigned to that triple.

The main contribution of this model lies in the type system which together with the introduced query order ensures that static type-checking prevents privacy violations. We say that a network is well behaved if

  • — access to a triple is more restrictive than access to its components and less restrictive than access to the user name it is enclosed with,

  • — each user can completely access their own profile,

  • — each user can update or partly delete profiles that they own (can access the whole profiles), and

  • — each user can update the privacy preference policy of data of another profile that they own or write data to another profile only if the newly obtained profile stays fully accessible to their owner.

We prove that any well-typed network is well behaved.

Type
Paper
Information
Mathematical Structures in Computer Science , Volume 27 , Issue 1 , January 2017 , pp. 33 - 53
Copyright
Copyright © Cambridge University Press 2015 

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