Title: Detecting Silent Data Corruption Using an Auxiliary Method and External Observer
Speaker: Hadrien Croubois
Abstract: HPC platforms and application are becoming increasingly complex. Consequently, protecting results against all forms of corruption and ensuring trustworthiness are becoming more important. While previous work focuses on application-specific detectors, the dataflow manager in our current work in the Decaf project aims to have an efficient generic mechanism. We address those issues using new replication patterns that rely on the use of an auxiliary method and an external learning observer. In this talk, we present both the theoretical validation mechanisms and different use cases where our mechanism can be applied to detect silent data corruption.
Title : Building Selfish-Resilient Distributed Systems
Speaker: Sonia Ben Mokhtar
Abstract: Collaborative systems (e.g., peer-to-peer instant messaging, file sharing, live streaming applications) generate among the largest amounts of traffic of today’s Internet. Common to all these systems is the assumption that, in return to the service offered by the collaborative system, users are willing to participate by sharing their resources with others. However, in practice, these systems suffer from selfish users that strategically free-ride the system whenever it is convenient for them. Albeit a number of solutions have been devised in the literature to deal with this problem, most of them are tailored to specific systems and thus lack flexibility and re-usability. During this seminar I will discuss methods for building selfish resilient distributed systems and future directions towards the automatic transformation of a given collaborative system into a system resilient to selfish behaviors.
Title : Category Theory 101, Graph Transformation and Social Data anonymisation.
Speaker: Frédéric Prost
Abstract: We will briefly introduce the basics of category theory in order to have a self-contained talk on Graph Transformation and an application to social data anonymisation. We will present the research field of social data anonymization: Huge network data sets, like social networks (describing personal relationships and cultural preferences) or communication networks (the graph of phone calls or email correspondents) become more and more common. These data sets are analyzed in many ways varying from the study of disease transmission to targeted advertising. Selling network data set to third-parties is a significant part of the business model of major internet companies. Usually, in order to preserve the confidentiality of the sold data set, only “anonymized” data are released: the original social networks is modified in order to avoid re-identification. The aim is to anonymize the data while keeping its use for the analyzes. We will review the most important results in this field, and we will show how graph rewriting techniques based on category theory can be used to design a more formal approach to tackle these issues.