=Paper=
{{Paper
|id=Vol-2491/abstract99
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2491/abstract99.pdf
|volume=Vol-2491
|dblpUrl=https://dblp.org/rec/conf/bnaic/PopescuY19
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==None==
https://ceur-ws.org/Vol-2491/abstract99.pdf
PARCo: a Knowledge-Based Agent for
Context-Sensitive Reasoning and
Decision-Making Regarding Privacy
Andrei Popescu1 Supervisor: Pınar Yolum1
Utrecht University, Princetonplein 5, 3584CC. Utrecht, The Netherlands,
a.popescu@students.uu.nl
Keywords: privacy, intelligent agent, ontology, argumentation, Internet of Things
Substantial technological developments are pushing digital devices into vir-
tually all spheres of humans’ lives. While these devices exploit users’ generated
data to offer a number of valuable services, the exchange of such information has
increasingly raised concerns regarding users’ privacy. In particular, privacy con-
cerns are likely to arise due to massive and uncontrolled data exchange among
interconnected devices in the scope of the Internet of Things (IoT).
Traditional legal approaches, which to date are still used to settle privacy
disputes, such as the US Constitution and the GDPR, have obtained particu-
larly little success with respect to dynamic scenarios like Online Social Networks
and the IoT. These environments share the common characteristic of allowing
privacy violations to occur implicitly. With respect to implicit privacy violations
however, studies show that the dichotomy distinguishing between private and not
private information, mainly used by legal approaches, provides no satisfactory
tool to prevent violations.
Alternatively, Contextual Integrity has been advanced as another account
to privacy [3]. This definition focuses on the appropriateness of information ex-
change with respect to contextual norms. In other words, privacy is maintained
when information exchange respects the appropriateness and distribution norms
related to a certain context. Such concept has inspired a number of scholars to
provide privacy-preserving approaches in the environment of OSNs and the IoT.
These approaches share the common intuition of the need of a context concept
to be used towards achieving appropriate information exchange. Nevertheless,
to date, there is no established privacy preserving approach applicable to On-
line Social Networks nor for the Internet of Things environments. In particular,
due to its scale and heterogeneity, the IoT environment in particular, has been
addressed by a limited number of efficient methods.
The literature suggests that available approaches would benefit from a def-
inition of contexts which can capture contexts’ relations, allowing contexts’ in-
ference from fragmentary information. Furthermore, these approaches should
display decision-making capabilities in partially observable and incomplete in-
formation environments.
Copyright c 2019 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
We therefore envision an Internet of Things environment, in which partici-
pating entities are represented by agents. That is the case, because as pointed
out by the literature, it is unfeasible that humans would be able to cope with the
overwhelming number of surrounding connected devices. Instead, agents provide
a convenient support for autonomous reasoning and decision-making.
To tackle appropriate information exchange with respect to context, we pro-
pose PARCo, a knowledge-based agent which reasons on its internal context
representation implemented by way of an OWL ontology, and subsequently uses
argumentation to take an information sharing decision in the IoT environment.
Specifically, the main contributions of our work lie in: i. exploiting OWL expres-
sivity for representing contexts and by means of a reasoner, infer new knowledge;
ii. using a knowledge-based agent which manipulates such a domain knowledge
in a convenient way, in this case by assigning degrees of belief, and using it in
the ASPIC argumentation engine in order to reach a share decision.
For instance, consider a smart work environment having a surveillance system
which which runs an instance of our agent PARCo. Consider that such agent
can exchange information with other surrounding agents, for example, those of
employees. In a scenario in which one employee, Bob, would like to access video
information concerning another employee, Alice, thanks to our approach, the
surveillance system would be able to reason on available information coming
from available agents, and decide whether to share the video by taking into
consideration Alice’s context [2]. By using its internal representation of contexts,
specified in terms of an ontology of concepts, PARCo is able to infer Alice’s active
contexts. Consequently, PARCo will use argumentation to reach the a final share
decision, based on the arguments built for each active context [1].
The decisions taken by PARCo were finally evaluated within a selection of
IoT scenarios and compared to a previous approach. Within each scenario the
human intuition based decision was assessed by means of personal interviews and
further used as a benchmark for comparison. PARCo has showed improvements
in the decisions taken with respect to previous approaches within the selected
IoT scenarios.
We have concluded that by exploiting an ontology of concepts for knowledge
inference, and by manipulating the inferred information, PARCo achieved better
information sharing decisions according to human intuition, with respect to a
previous approach.
References
1. Dung, P.: On the acceptability of arguments and its fundamental role in nonmono-
tonic reasoning, logic programming and n-person games* 1. Artificial Intelligence
77, 321–357 (11 1995)
2. Kökciyan, N., Yolum, P.: Context-based reasoning on privacy in internet of things.
pp. 4738–4744. International Joint Conference on Artificial Intelligence (IJCAI-17)
(08 2017)
3. Nissenbaum, H.: Privacy as contextual integrity. Washington Law Review 79, 119
(05 2004)