publications

2026

1. Practical Semi-Open Chat Groups for Secure Messaging Applications

   Alex Davidson , Luiza Soezima, and Fernando Virdia

   Proceedings on Privacy Enhancing Technologies, Jul 2026
2. Fuzzy Private Set Union via Oblivious Key Homomorphic Encryption Retrieval

   Jean-Guillaume Dumas , Aude Maignan , and Luiza Soezima

   Jul 2026

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3. Track me if you can: Ephemeral coin tracing

   Ignacio Amores-Sesar , Christian Cachin , Rohit Chatterjee , and 3 more authors

   Jul 2026

2022

1. UlyssesNER-Br: A Corpus of Brazilian Legislative Documents for Named Entity Recognition

   Hidelberg O. Albuquerque , Rosimeire Costa , Gabriel Silvestre , and 16 more authors

   In Computational Processing of the Portuguese Language: 15th International Conference, PROPOR 2022, Fortaleza, Brazil, March 21–23, 2022, Proceedings , Jul 2022

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   The amount of legislative documents produced within the past decade has risen dramatically, making it difficult for law practitioners to consult and update legislation. Named Entity Recognition (NER) systems have the untapped potential to extract information from legal documents, which can improve information retrieval and decision-making processes. We introduce the UlyssesNER-Br, a corpus of Brazilian Legislative Documents for NER with quality baselines. The presented corpus consists of bills and legislative consultations from Brazilian Chamber of Deputies. We implemented Conditional Random Field (CRF) and Hidden Markov Model (HMM) models, and the promising F1-score of 80.8% in the analysis by categories and 81.04% in the analysis by types, was achieved with the CRF model. The entities with the best average F1-score results were “FUNDlei” and “DATA”, and the ones with the worst results were “EVENTO” and “PESSOAgrupoind”. The corpus was also evaluated using a BiLSTM-CRF and Glove architectures provided by the pioneering state-of-the-art paper, achieving F1-score of 76.89% in the analysis by categories and 59.67% in the analysis by types.