This paper introduces the Biometrics Data Space framework, which is a secure ecosystem built on Data Spaces technology and it is designed to address the challenges of suspect identification during cross-border crime investigation. Apart from Data Spaces technology, the proposed framework innovates by leveraging also Privacy Enhancing Technologies (PETs) and blockchain to enable secure, trustworthy, and sovereign data exchange between Law Enforcement Agencies (LEAs) across borders. Specifically, it utilizes advanced PETs, including Large-Scale Biometric Data Indexing based on deep hashing techniques and Homomorphic Encryption to allow for suspect identification without disclosing sensitive information of personal biometric data. Thus, it enables LEAs to securely compare and exchange encrypted sensitive biometric data, including facial images, fingerprints and voiceprints, while maintaining data privacy and data sovereignty. LEAs define the usage rules for the biometic data they own and these rules are enforced to and respected by the other LEAs participating in the Biometrics Data Space. The proposed architecture is designed to be scalable, allowing the incorporation of additional biometric modalitiies and the easy expansion and integration with new participant LEAs.

Cross-border collaboration among Law Enforcement Agencies is essential for effective and timely suspect identification, especially when the availability of biometric data varies between agencies. This paper presents a scalable and secure approach for multimodal biometric identification across multiple jurisdictions. Our approach allows Law Enforcement Agencies to combine biometric modalities -facial images, fingerprints, and voice samples- and compare them with collaborating agencies, improving the overall accuracy and effectiveness of suspect identification. By leveraging deep learning models for indexing and comparison, efficient data retrieval was achieved without compromising privacy or security. To ensure the protection of sensitive biometric data, our approach incorporates advanced encryption mechanisms, including Homomorphic Encryption for secure computations and Advanced Encryption Standard (AES encryption) for safeguarding biometric information. Its decentralised architecture allows each Law Enforcement Agency to maintain independent instances of the Deep Learning Indexer and Comparator, minimising risks associated with centralising sensitive data and supporting seamless collaboration between agencies. This approach not only improves the accuracy of suspect identification but also enhances operational efficiency by allowing Law Enforcement Agencies to query and share biometric data securely across borders.

This paper addresses the growing issue of terrorists utilizing the Internet, and particularly the Dark Web market places, with the purpose of fundraising for their illegal activities. It proposes the Visual Analytics (VA) system, an advanced AI-powered tool, in an effort to combat cross-border financing associated with terrorism. The tools focus on semantic concept detection and large-scale visual data indexing, and the ultimate goal is to familiarize end-users, practitioners, and law enforcement investigators with these technologies. This paper discusses prior works concerning the presentation and visualization of Deep Learning results to users, outlines the main objectives, and provides fundamental usage instructions for the VA system.