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In this paper we present research results from the multi-disciplinary EU research project USEMP (USEMP is a project funded from EU research framework, additional information about project scope and deliverables are available at project’s public website at: http://www.usemp-project.eu/). In particular, we look at the legal aspects of personal data licensing and profile transparency, the development of a personal data value model in Online Social Networks (OSNs) and the development of disclosure scoring and personal data value frameworks. In the first part of the paper we show how personal data usage licensing and profile transparency for OSN activities provides for Data Protection by Design (DPbD). We also present an overview of the existing personal data monetization ecosystem in OSNs and its possible evolutions for increasing privacy and transparency for consumers about their OSN presence. In the last part of the paper, we describe the USEMP scoring framework for personal information disclosure and data value that can assist users to better perceive how their privacy is affected by their OSN presence and what the value of their OSN activities is.

Contains fulltext : 158077.pdf (Author’s version preprint ) (Open Access)

Security and privacy often seem to be at odds with one another. In this paper, we revisit the design principle of revocable privacy which guides the creation of systems that offer anonymity for people who do not violate a predefined rule, but can still have consequences for people who do violate the rule. We first improve the definition of revocable privacy by considering different types of sensors for users’ actions and different types of consequences of violating the rules (for example blocking). Second, we explore some use cases that can benefit from a revocable privacy approach. For each of these, we derive the underlying abstract rule that users should follow. Finally, we describe existing techniques that can implement some of these abstract rules. These descriptions not only illustrate what can already be accomplished using revocable privacy, they also reveal directions for future research. © Springer International Publishing Switzerland 2016.

ispartof: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) vol:9484 status: published

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National audience

In this paper we present a distributed algorithm to implement a task pool. The algorithm can be used to implement a processor farm, i.e., a collection of processes that consume tasks from the task pool and possibly produce tasks into it. There are no restrictions on which process consumes which task nor on the order in which tasks are processed. The algorithm takes care of the distribution of the tasks over the processes and ensures load balancing. We derive the algorithm by transforming a sequential algorithm into a distributed one. The transformation is guided by the distribution of the data over processes. First we discuss the case of two processes, and then the general case of one or more processes.