18,526 research outputs found
The control over personal data: True remedy or fairy tale ?
This research report undertakes an interdisciplinary review of the concept of
"control" (i.e. the idea that people should have greater "control" over their
data), proposing an analysis of this con-cept in the field of law and computer
science. Despite the omnipresence of the notion of control in the EU policy
documents, scholarly literature and in the press, the very meaning of this
concept remains surprisingly vague and under-studied in the face of
contemporary socio-technical environments and practices. Beyond the current
fashionable rhetoric of empowerment of the data subject, this report attempts
to reorient the scholarly debates towards a more comprehensive and refined
understanding of the concept of control by questioning its legal and technical
implications on data subject\^as agency
A Generic Information and Consent Framework for the IoT
The Internet of Things (IoT) raises specific issues in terms of information
and consent, which makes the implementation of the General Data Protection
Regulation (GDPR) challenging in this context. In this report, we propose a
generic framework for information and consent in the IoT which is protective
both for data subjects and for data controllers. We present a high level
description of the framework, illustrate its generality through several
technical solutions and case studies, and sketch a prototype implementation
Is the Quantity-Quality Trade-off a Trade-off for All, None, or Some?
Although the theoretical trade-off between the quantity and quality of children is well-established, empirical evidence supporting such a causal relationship − particularly on child health − is limited. We use two measures of child health to asses the quantity-quality trade-off across the entire distribution. Using data from the Indonesia Family Life Survey and controlling for the potential endogeneity of child quantity, we find evidence of a causal trade-off only for some and only in the short-run.intrahousehold allocation, health, human capital, fertility, quantile treatment effects, stochastic dominance
On the Complexity of Optimization Problems based on Compiled NNF Representations
Optimization is a key task in a number of applications. When the set of
feasible solutions under consideration is of combinatorial nature and described
in an implicit way as a set of constraints, optimization is typically NP-hard.
Fortunately, in many problems, the set of feasible solutions does not often
change and is independent from the user's request. In such cases, compiling the
set of constraints describing the set of feasible solutions during an off-line
phase makes sense, if this compilation step renders computationally easier the
generation of a non-dominated, yet feasible solution matching the user's
requirements and preferences (which are only known at the on-line step). In
this article, we focus on propositional constraints. The subsets L of the NNF
language analyzed in Darwiche and Marquis' knowledge compilation map are
considered. A number of families F of representations of objective functions
over propositional variables, including linear pseudo-Boolean functions and
more sophisticated ones, are considered. For each language L and each family F,
the complexity of generating an optimal solution when the constraints are
compiled into L and optimality is to be considered w.r.t. a function from F is
identified
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