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Using Timing Attacks Against Cryptographic Algorithms
Computer algorithms that are written with the intent to keep data private are used in every day cryptography. These algorithms may exhibit execution time behaviour which is dependant on secret information that is not known to an outsider. When carefully analysed, this dependency may leak information that can be used to gain unintended access to private data, effectively nullifying the use of such algorithms. This threat poses a vital risk to the field of computer cryptography, and analysis should be done in attempt to eradicate this potential threat from any algorithms in modern day use.
In this paper, attacks are orchestrated against several algorithms that have previously been used in cryptography, resulting in the successful retrieval of secret data within a manageable time-scale.
Economía de la felicidad
En el siguiente ensayo, usted podrá encontrar encuestas que le brindaran información disponible en la pagina web del DANE, que le mostraran los diferentes estratos socioeconómicos en la ciudad de Medellín. El propósito de este ensayo es mostrar una relación entre los ingresos de los individuos y su felicidad. Dado a que la mayoría de la población de Medellín consta con bajos recursos y sufren necesidades por la falta de ciertas modalidades indispensables para sobrevivir, ellos son felices y están plenos con su estilo de vida.
Where are our Providers?: Image Clustering based on Locations of Brazilian Government Suppliers
The Observatory of Public Spending (or ODP, in Portuguese) is a special unit of Brazil's Ministry of Transparency, Monitoring and Office of the Comptroller-General (or CGU, in Portuguese) responsible for monitoring public spending and gathering managerial and audit information to support the work of CGU internal auditors. One of the most important themes monitored by this unit is Public Procurements and Government Suppliers which have won these procurement processes. Image analysis of many of these suppliers headquarters revealed suspicious landscapes, such as rural areas, isolated places or slums. These landscapes could be an indication of fake suppliers with poor capacity of delivering public goods and services. However, checking thousands of landscapes in order to find these fake suppliers would be a very expensive task. Our objective then is to discover what are the possible groups of scenes involving government suppliers, given that these images were not previously labeled, as automatically as possible. For that reason, we used Places CNN, a pretrained convolutional neural network for scene recognition presented by Zhou et al., which was trained on 205 scene categories with 2.5 million images, for scene recognition on Brazilian Government Suppliers.
Rodrigo Peres Ferreira
Data acquisition from mobile sensors
Coursework project on data analysis. Using machine learning and android sensors data to predict whether gadget is located indoors or outdoors.