Carlos Dafonte

David Soler, Carlos Dafonte, Manuel Fernández-Veiga et al.

The Messaging Layer security (MLS) and its underlying Continuous Group Key Agreement (CGKA) protocol allows a group of users to share a cryptographic secret in a dynamic manner, such that the secret is modified in member insertions and deletions. Although this flexibility makes MLS ideal for implementations in distributed environments, a number of issues need to be overcome. Particularly, the use of digital certificates for authentication in a group goes against the group members' privacy. In this work we provide an alternative method of authentication in which the solicitors, instead of revealing their identity, only need to prove possession of certain attributes, dynamically defined by the group, to become a member. Instead of digital certificates, we employ Attribute-Based Credentials accompanied with Selective Disclosure in order to reveal the minimum required amount of information and to prevent attackers from linking the activity of a user through multiple groups. We formally define a CGKA variant named Attribute-Authenticated Continuous Group Key Agreement (AA-CGKA) and provide security proofs for its properties of Requirement Integrity, Unforgeability and Unlinkability. We also provide an implementation of our AA-CGKA scheme and show that it achieves performance similar to a trivial certificate-based solution.

David Soler, Carlos Dafonte, Manuel Fernández-Veiga et al.

Instant messaging has become one of the most used methods of communication online, which has attracted significant attention to its underlying cryptographic protocols and security guarantees. Techniques to increase privacy such as End-to-End Encryption and pseudonyms have been introduced. However, online spaces such as messaging groups still require moderation to prevent misbehaving users from participating in them, particularly in anonymous contexts.. In Anonymous Blocklisting (AB) schemes, users must prove during authentication that none of their previous pseudonyms has been blocked, preventing misbehaving users from creating new pseudonyms. In this work we propose an alternative Federated Anonymous Blocklisting (FAB) in which the centralised Service Provider is replaced by small distributed Realms, each with its own blocklist. Realms can establish trust relationships between each other, such that when users authenticate to a realm, they must prove that they are not blocked in any of its trusted realms. We provide an implementation of our proposed scheme; unlike existing AB constructions, the performance of ours does not depend on the current size of the blocklist nor requires processing new additions to the blocklist. We also demonstrate its applicability to real-world messaging groups by integrating our FAB scheme into the Messaging Layer Security protocol.

Minia Manteiga, Raúl Santoveña, Marco A. Álvarez et al.

A method based on Generative Adversaria! Networks (GANs) is developed for disentangling the physical (effective temperature and gravity) and chemical (metallicity, overabundance of a-elements with respect to iron) atmospheric properties in astronomical spectra. Using a projection of the stellar spectra, commonly called latent space, in which the contribution dueto one or several main stellar physicochemical properties is minimised while others are enhanced, it was possible to maximise the information related to certain properties, which can then be extracted using artificial neural networks (ANN) as regressors with higher accuracy than a reference method based on the use of ANN trained with the original spectra. Methods. Our model utilises autoencoders, comprising two artificial neural networks: an encoder anda decoder which transform input data into a low-dimensional representation known as latent space. It also uses discriminators, which are additional neural networks aimed at transforming the traditional autoencoder training into an adversaria! approach, to disentangle or reinforce the astrophysical parameters from the latent space. The GANDALF tool is described. It was developed to define, train, and test our GAN model with a web framework to show how the disentangling algorithm works visually. It is open to the community in Github. Results. The performance of our approach for retrieving atmospheric stellar properties from spectra is demonstrated using Gaia Radial Velocity Spectrograph (RVS) data from DR3. We use a data-driven perspective and obtain very competitive values, ali within the literature errors, and with the advantage of an important dimensionality reduction of the data to be processed.

Raúl Santoveña, Carlos Dafonte, Minia Manteiga

Data compression techniques focused on information preservation have become essential in the modern era of big data. In this work, an encoder-decoder architecture has been designed, where adversarial training, a modification of the traditional autoencoder, is used in the context of astrophysical spectral analysis. The goal of this proposal is to obtain an intermediate representation of the astronomical stellar spectra, in which the contribution to the flux of a star due to the most influential physical properties (its surface temperature and gravity) disappears and the variance reflects only the effect of the chemical composition over the spectrum. A scheme of deep learning is used with the aim of unraveling in the latent space the desired parameters of the rest of the information contained in the data. This work proposes a version of adversarial training that makes use of a discriminator per parameter to be disentangled, thus avoiding the exponential combination that occurs in the use of a single discriminator, as a result of the discretization of the values to be untangled. To test the effectiveness of the method, synthetic astronomical data are used from the APOGEE and Gaia surveys. In conjunction with the work presented, we also provide a disentangling framework (GANDALF) available to the community, which allows the replication, visualization, and extension of the method to domains of any nature.