Enabling personal consent in databases
Enabling personal consent in databases
Users have the right to consent to the use of their data, but current methods are limited to very coarse-grained expressions of consent, as "opt-in/opt-out" choices for certain uses. In this paper we identify the need for fine-grained consent management and formalize how to express and manage user consent and personal contracts of data usage in relational databases. Unlike privacy approaches, our focus is not on preserving confidentiality against an adversary, but rather cooperate with a trusted service provider to abide by user preferences in an algorithmic way. Our approach enables data owners to express the intended data usage in formal specifications, that we call consent constraints, and enables a service provider that wants to honor these constraints, to automatically do so by filtering query results that violate consent; rather than both sides relying on "terms of use" agreements written in natural language. We provide formal foundations (based on provenance), algorithms (based on unification and query rewriting), connections to data privacy, and complexity results for supporting consent in databases. We implement our framework in an open source RDBMS, and provide an evaluation against the most relevant privacy approach using the TPC-H benchmark, and on a real dataset of ICU data.
375–387
Konstantinidis, Georgios
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Holt, Jet
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Chapman, Age
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October 2021
Konstantinidis, Georgios
f174fb99-8434-4485-a7e4-bee0fef39b42
Holt, Jet
8bfcf1d8-2fb6-4d1d-bdef-f9171d3b5aeb
Chapman, Age
721b7321-8904-4be2-9b01-876c430743f1
Konstantinidis, Georgios, Holt, Jet and Chapman, Age
(2021)
Enabling personal consent in databases.
Proceedings of the VLDB Endowment, 15 (2), .
(doi:10.14778/3489496.3489516).
Abstract
Users have the right to consent to the use of their data, but current methods are limited to very coarse-grained expressions of consent, as "opt-in/opt-out" choices for certain uses. In this paper we identify the need for fine-grained consent management and formalize how to express and manage user consent and personal contracts of data usage in relational databases. Unlike privacy approaches, our focus is not on preserving confidentiality against an adversary, but rather cooperate with a trusted service provider to abide by user preferences in an algorithmic way. Our approach enables data owners to express the intended data usage in formal specifications, that we call consent constraints, and enables a service provider that wants to honor these constraints, to automatically do so by filtering query results that violate consent; rather than both sides relying on "terms of use" agreements written in natural language. We provide formal foundations (based on provenance), algorithms (based on unification and query rewriting), connections to data privacy, and complexity results for supporting consent in databases. We implement our framework in an open source RDBMS, and provide an evaluation against the most relevant privacy approach using the TPC-H benchmark, and on a real dataset of ICU data.
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3489496.3489516
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Published date: October 2021
Additional Information:
Funding Information:
George Konstantinidis was supported by the Alan Turing Institute through a Fellowship and an Enhancement Project. Adriane Chapman was partially supported by EPSRC (EP/SO28366/1). We deeply thank Paolo Pareti and Muhammed Qaid for helping with some of the experiments.
Publisher Copyright:
© 2021, VLDB Endowment. All rights reserved.
Identifiers
Local EPrints ID: 467954
URI: http://eprints.soton.ac.uk/id/eprint/467954
PURE UUID: d3cd5820-5a39-4677-912f-333501d8717c
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Date deposited: 26 Jul 2022 16:53
Last modified: 06 Jun 2024 01:59
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Author:
Georgios Konstantinidis
Author:
Jet Holt
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