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Journal of Logic, Language and Information
Published in: Journal of Logic, Language and Information 4/2020

19-11-2019

Formalizing GDPR Provisions in Reified I/O Logic: The DAPRECO Knowledge Base

Authors: Livio Robaldo, Cesare Bartolini, Monica Palmirani, Arianna Rossi, Michele Martoni, Gabriele Lenzini

Published in: Journal of Logic, Language and Information | Issue 4/2020

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Abstract

The DAPRECO knowledge base is the main outcome of the interdisciplinary project bearing the same name (https://​www.​fnr.​lu/​projects/​data-protection-regulation-compliance). It is a repository of rules written in LegalRuleML, an XML formalism designed to be a standard for representing the semantic and logical content of legal documents. The rules represent the provisions of the General Data Protection Regulation (GDPR), the new Regulation that is significantly affecting the digital market in the European Union and beyond. The DAPRECO knowledge base builds upon the Privacy Ontology (PrOnto) (Palmirani et al in Proceedings of the 7th international conference on electronic government and the information systems perspective: technology-enabled innovation for democracy, government and governance, 2018c), which provides a model for the legal concepts involved in the GDPR, by adding a further layer of constraints in the form of if-then rules, referring either to standard first order logic implications or to deontic statements. If-then rules are formalized in reified Input/Output logic (Robaldo and Sun in J Log Comput 7, 2017) and then codified in LegalRuleML. Reified Input/Output logic is an application of standard Input/Output logic for legal reasoning, in which Input/Output logic is combined with the reification-based approach in Hobbs and Gordon (A formal theory of commonsense psychology, how people think people think. Cambridge University Press, Cambridge, 2017). The DAPRECO knowledge base is then a case study for reified Input/Output logic, and it shows that the formalism indeed appears to be a good candidate to effectively formalize, via uniform and simple (flat) representations, complex linguistic/deontic phenomena that may be found in legal texts. To date, the DAPRECO knowledge base is the biggest knowledge base in LegalRuleML and Input/Output logic freely available online (https://​github.​com/​dapreco/​daprecokb/​blob/​master/​gdpr/​rioKB_​GDPR.​xml).
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Footnotes
3
FinTech (Financial Technology) refers to the use of artificial intelligence and computer science to support or enable banking and financial services. The main functions of FinTech software include regulatory monitoring, reporting, and compliance. RegTech (Regulatory Technology) is a more general term referring to computer technology applied to any kind of regulated business, not only finance.
 
11
Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC.
 
14
A particularly representative example is the “Délibération SAN-2019-001 du 21 janvier 2019”, issued by the Commission nationale de l’informatique et des libertés (CNIL), the Supervisory Authority of France, which sanctioned Google LLC for 50 million euros.
 
17
As discussed in Robaldo and Sun (2017), consider the obligations “If someone kills a dog, s/he has to spend two years in prison” and “If someone robs a bank s/he has to spend two years in prison”. Suppose also that John did one of the two, but there is no way to come to know which one, i.e. whether he killed a dog or robbed a bank. Logically, John must spend two years in prison. But on the perspective of legal reasoning, he must not: only if concrete evidence of what he did is found, obligations apply. The example considered marks an interesting border between legal reasoning and standard logical reasoning.
 
18
The example of paradox discussed in Parent and Leendert (2014b) is: given the obligations “You ought to exercise hard everyday” (\(\top \), Ex) and “If you exercise hard everyday, you ought to eat heartily” (Ex, Eh), we derive via CT that also the obligation “You ought to eat heartily” (\(\top \), Eh) holds. However, this is not clearly the case: you are obliged to eat heartily only if you exercise hard everyday.
 
20
States, facts, and events are reified into FOL individuals, from the Latin word “re(s)” for “thing”: we take states, facts, and events to be things.
 
21
Note that in the formulæ, all terms are FOL constants. They could be FOL variables, provided that we quantify them, e.g., ‘\(\exists _{e_b}[(blond'\,e_b\) John\()]'\).
 
22
In the DAPRECO knowledge base, special LegalRuleML prefixes allow to distinguish the predicates corresponding to concepts in PrOnto from the others. See Sect. 4 below.
 
25
Indeed, the formula in (16) lacks a predicate referring to an exception to Article 6, paragraph 1, point 1. The handling of exceptions in reified Input/Output logic will be illustrated below in Sect. 4.2, so that we avoid that predicate in (16).
 
26
We assume, for simplicity, that each eventuality may be described in a single way, i.e., that only a single description “descr(e)” (functionally) corresponds to an eventuality e.
 
28
LegalRuleML includes a special tag to mark the bearers of obligations and permissions: \(<\texttt {lrml:Bearer/}>\). The DAPRECO knowledge base omits this tag to avoid redundancies: the bearers need to be already specified at the level of the underlying logical formalism, in order to enable nested obligations and nested permissions.
 
29
Idelberger et al. (2016) recently showed that nested obligations and nested permissions may also occur in the formalization of smart contracts.
 
30
In reality, formula (23) is a simplification of the formula stored in the DAPRECO knowledge base and associated with (22), as it does not specify the exceptions of Article 17, paragraph 2. Section 4.2 below illustrates how reified Input/Output logic deals with exceptions.
 
31
As it will be clarified below in Sect. 4.3, the truth value of the predicate ‘reasonable’ depends on context-specific legal interpretations of the corresponding adjective.
 
35
In footnote 30 above, we noted that the two elements x and y of a pair (x, y) belonging to either O or P are always made up of conjunctions of atomic predications, with an important exception. The exception is represented by ‘naf’, which indeed introduces one level of nesting in the formulæ.
 
36
In (39), we formalized “the relevant cloud service customer” occurring in (37) via the predicate PIIController. According to ISO 27018, Article 0.1: “The cloud service customer, who has the contractual relationship with the public cloud PII processor, can range from a natural person, a ‘PII principal’, processing his or her own PII in the cloud, to an organization, a ‘PII controller’, processing PII relating to many PII principals”.
 
38
Note that, in (55), \(\mathtt {e}_\mathtt {caj}\), \(\mathtt {e}_\mathtt {cbj}\), \(\mathtt {e}_\mathtt {daj}\), \(\mathtt {t}_\mathtt {1}\), \(\mathtt {t}_\mathtt {2}\), John, y, and z are FOL constants.
 
40
\(\hbox {BH}_2\) is the class of languages which are the intersection of a language in NP and a language in coNP.
 
41
Personal communication with Leon van der Torre.
 
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Metadata
Title
Formalizing GDPR Provisions in Reified I/O Logic: The DAPRECO Knowledge Base
Authors
Livio Robaldo
Cesare Bartolini
Monica Palmirani
Arianna Rossi
Michele Martoni
Gabriele Lenzini
Publication date
19-11-2019
Publisher
Springer Netherlands
Published in
Journal of Logic, Language and Information / Issue 4/2020
Print ISSN: 0925-8531
Electronic ISSN: 1572-9583
DOI
https://doi.org/10.1007/s10849-019-09309-z

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