Cryptography & Graphs

• Computational aspects of elliptic and hyperelliptic curves.


• Cryptography in computationally restricted devices.


• Secure electronic voting.


• Privacy-preserving social network data analysis.


• Extremal (di) graphs and eccentric (di) graphs.

   Participants in a social network establish relationships among them motivated by their common preferences. In this way, a graph structure is constructed so that each participant in the network is assigned to a node and relationships are represented by means of edges. Analysis of social network graph data and their evolution, is a source of valuable information for several research areas such as sociology, psychology, economics and epidemiology, among others.

   Social networks contain sensitive personal data whose publication or transfer would compromise network participants privacy. This means that providing access to such data for their study and scientific analysis could jeopardize individuals privacy. Our research in this area focuses on the development of new privacy models for protection of social network data.