The SpeechXRays project will develop and test in real-life environments a user recognition platform based on voice acoustics analysis and audio-visual identity verification. SpeechXRays will outperform state-of-the-art solutions in the following areas: · Security: high accuracy solution (cross over accuracy of 1/100 i.e. twice the commercial voice/face solutions) · Privacy: biometric data stored in the device (or in a private cloud under the responsibility of the data subject) · Usability: text-independent speaker identification (no pass phrase), low sensitivity to surrounding noise · Cost-efficiency: use of standard embedded microphone and cameras (smartphones, laptops). The project will combine and pilot two proven techniques: acoustic driven voice recognition (using acoustic rather than statistical only models) and multi-channel biometrics incorporating dynamic face recognition (machine vision analysis of speech, lip movement and face). The vision of the SpeechXRays project is to provide a solution combining the convenience and cost-effectiveness of voice biometrics, achieving better accuracies by combining it with video, and bringing superior anti-spoofing capabilities. The technology will be deployed on 2000 users in 3 pilots: a workforce use case, an eHealth use case and a consumer use case. The project lasts 36 months and is coordinated by world leader in digital security solutions for the mobility space.

SMILE proposes a novel mobility concept that addresses the aforementioned challenges by designing, implementing and evaluating in relevant environments (TRL6) prototype management architecture, for the accurate verification, automated control, monitoring and optimization of people’ flows at Land Border Infrastructures. It leverages the capabilities of the smart mobile devices in biometric control for secure and trusted authentication, and elaborates on their exploitation as part of a multimodal biometric verification process that supplements / complements existing approaches. Furthermore, SMILE’s mobility concept builds upon Private Cloud Infrastructure technologies which communicate with remote SMILE handhelds through a secure gateway. SMILE ecosystem will target EU land borders which will be the beneficiaries of the proposed solutions. In fact, the proposed technology and business framework developed in SMILE will be validated through pan-European demonstrations in 3 BCPs. The operational properties of the technologies and overall solution will be validated and evaluated against cost, performance, effectiveness and usability indicators. Use cases will be supported by different architectural designs, which will be classified according to the operation mode. BCPs participating in the project’s pilots will deploy and evaluate the solution at business as usual and emergency situations across various status operations. SMILE aims to (1) minimise the exposure of BCPs to security risks and threats, and (2) help them successfully respond to security incidents, while relieving them from all unnecessary and costly efforts of identifying, acquiring and using the appropriate technology. To this CNBP, HBP & RBP BCP partners will deploy and validate the proposed secure & reliable ecosystem in two use cases (Romania Bulgaria), in which the adaptation of SMILE framework to focused applications will be performed.

Huge increase of collected data, storage capacity and computing power promote the field of Artificial Intelligence (AI) to the status of panacea to all problems. Indeed, neural networks improved the results in the fields challenging for the handcrafted algorithms previously. However, there is always a price to pay: number of its drawbacks remain unaddressed. In the real world, a decision system with AI can receive an input that is unlike anything it has seen during training. That can lead to the unpredictable behavior. Can we trust the output of such system for a particular input? In LIMPID project, we address this issue of confidence of AI output in the context of face recognition and face quality estimation in images. LIMPID concentrates on a challenge how to estimate the confidence to any response of AI algorithm. This approach can be used in a wide range of applications. LIMPID also proposes the analyses of the image features that highly contribute to the AI algorithm’s decision.

Implementing cryptography on embedded devices is an ongoing challenge: every year new implementation flaws are discovered and new attack paths are being used by real life adversaries. Whilst cryptography can guarantee many security properties, it crucially depends on the ability to keep the used keys secret even in face of determined adversaries. Over the last two decades a new type of adversary has emerged, able to obtain, from the cryptographic implementation, side channel leakage such as recording of response times, power or EM signals, etc. To account for such adversaries, sophisticated security certification and evaluation methods (Common Criteria, EMVCo, FIPS…) have been established to give users assurance that security claims have withstood independent evaluation and testing. Recently the reliability of these evaluations has come into the spotlight: the Taiwanese citizen card proved to be insecure, and Snowden’s revelations about NSA's tampering with FIPS standards eroded public confidence. REASSURE will (1) improve the efficiency and quality of all aspects of certification using a novel, structured detect-map-exploit approach that will also improve the comparability of independently conducted evaluations, (2) cater for emerging areas such as the IoT by automating leakage assessment practices in order to allow resistance assessment without immediate access to a testing lab, (3) deliver tools to stakeholders, such as reference data sets and an open-source leakage simulator based on instruction-level profiles for a processor relevant for the IoT, (4) improve existing standards by actively pushing the novel results to standardization bodies. REASSURE's consortium is ideal to tackle such ambitious tasks. It features two major circuits manufacturers (NXP, IDEMIA), a highly respected side channel testing lab (Riscure), an engaged governmental representative (ANSSI), and two of the most prominent research institutions in this field (UCL, University of Bristol).

The i3-MARKET project addresses the growing demand for a single European Data Market Economy by innovating marketplace platforms, demonstrating with industrial implementations that the data economy growth is possible. i3-MARKET proposal provides technologies for trustworthy (secure and reliable), data-driven collaboration and federation of existing and new future marketplace platforms, special attention on industrial data and particularly on sensitive commercial data assets from both SMEs to large industrial corporations is taken.It is well known that despite various research and innovation attempts working on Big Data Management, of Personal and or Industrial Data Integration and Security, there is no broadly accepted trusted and secure data marketplace. i3-MARKET will address this by developing lacking technologies and solutions for a trusted (secure, self-governing, consensus-based and auditable), interoperable (semantic-driven) and decentralised (scalability) infrastructure, called i3-MARKET Software Framework also named i3-MARKET Backplane, that enables federation via interoperability of the existing and future emerging data spaces and marketplaces. The i3-MARKET backplane introduces data monetisation in the form of Intelligent Data Economy services to formerly closed systems to offer and share data and lowering the market entry barriers for stakeholders – especially SMEs – to trade their data assets with the aim to ignite a flourishing data economy that fosters innovation and business in Europe. i3-MARKET focuses on the desired levels of privacy and confidentiality that support both legal and user-desired control and transparency for sharing data among relevant systems and services. The i3-MARKET backplane pays special attention in regulatory aspects around sensitive data assets developing the required security and access control measures that enable secure trading of data including support for automated contracting following European regulation (GDPR).