The continuous appearance and improvement of mobile devices in the form of smartwatches, smartphones and other similar devices has led to a growing and unfair interest in putting their users under the magnifying glass and control of applications.
We introduce S++, a simple, robust, and deployable framework for training a neural network (NN) using private data from multiple sources, using secret-shared secure function evaluation. In short, consider a virtual third party to whom every data-holder sends their inputs, and which computes the neural network: in our case, this virtual third party is actually a set of servers which individually learn nothing, even with a malicious (but non-colluding) adversary.
“ENISA CSIRT training material was introduced in 2008. In 2012, 2013 and 2014 it was complemented with new exercise scenarios containing essential material for success in the CSIRT community and in the field of information security. In these pages you will find the ENISA CSIRT training material, containing Handbooks for teachers, Toolsets for students and Virtual Images to support hands on training sessions. ” The materials continue to be updated in 2020 and are appropriate for use by cybersecurity specialists and decision-makers.
(Change of dates to 2021).Top cyber talents from each participating country will meet in Vienna to network and collaborate and finally compete against each other. Contestants will be challenged in solving security related tasks from domains such as web security, mobile security, crypto puzzles, reverse engineering and forensics and in the process collect points for solving them.
Este congreso, motivado por la creciente sensibilidad de las compañías en materia de Gobierno, Riesgo y Cumplimiento, se enfoca en generar una visión global de los procesos, gestión de riesgos, fraude, control interno y cumplimiento normativo y legislativo, sin dejar de lado la metodología y ejecución de revisiones y auditorías de los mismos