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