I + D + I

Research and develop of App privacy analysis project under the mentorship of Prof. José María del Álamo Ramiro .

This included dynamic and static app analysis besides Machine Learning techniques and natural language processing in order to understand each app privacy policy and verify if the information declared in it matches with real app behaviour. It was created with docker containers, RabbitMQ and MySQL databases.

Please visit the project web here.

Article that explains a Privacy Assistant Android App I created here.

Short article that explains the new way of creating and using Machine Learning proyects, Federated Machine Learning here.

Privacy assistant for Android mobiles develop

We live in a time where there is a big lack of knowledge from users about what is happening with their personal data. It is not easy for an average user to become aware of what are his daily phone applications doing and whether they are filtering personal data and in that case, what kind of data are we talking about.

In order to solve this problem, this project will aim to create an Android application that helps users to protect their privacy and let them know what is really going on with their personal data and their phones.

Thus, in this project it will be used an already existing Android application called AntMonitor, that passively monitors all packets in and out of the network interface that will be combined with FML (Federated Machine Learning) technologies to develop a new preferences recommendation model that can be adapted to fit to each users risk profile.

App video: here.

Presentation defense video: here

Article: here.