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Project NumberProposal Name
Lead Partner (name)PartnersKeywordsAbstract
3,108MW4ALL 2.0Least Authority 
identity free file transfer
3,110DAppNodeDAppNode Association
personal data management
3.10GeoWalletBlocs et Compagnie
gelocation, mobility data, blockchain
3.12Keyn 2.0Keyn B.V.Content Powerwebauthn, authentication
3.21TOTEMFeron Technologies P.C. (FERON)ntopIOT, trusted connected home
3.27PY - 2.0Panga
Home Network Operating Server, IoT, home working
3.38PRIMACognitive Innovations 
AI, FOG, machine learning
3.40PaE Consent GatewayTrinity College DublinOpen Consent Network, Birmingham City Universitypersonal data management, consent management
3.53TruVeLedgerRISE Research Institutes of Sweden AB
vehicle safety data
automated identity management, MidPoint

With the introduction of the Regulation (EU) 2016/679 and the need for companies to comply with ISO/IEC 27001 requirements, privacy enhancing technologies are becoming crucial for several types of enterprises. There is therefore an increasing demand for new and effective anonymizing techniques and their application in different domains with specific requirements.

Our main objective is to provide a service that allows the automatic anonymization and protection of user personal data contained in texts and voice transcriptions in compliance with the applicable legal framework.

With this aim, we intend to work on a Type 2 project for the technological development of an automated anonymizer prototype for Italian and English, to be firstly applied to two relevant use cases, and then extended to other scenarios (domain adaptation).

The use cases will involve the anonymization of 1) free text sections from customer surveys and internal reports analyzed for the evaluation of customer and employee experience; 2) linguistic resources (both written texts and audio recordings) created for companies that develop voice technologies such as STT and ASR.

The anonymization process will be carried out by means of both Deep Learning and rule-based Natural Language Processing technologies and will include common data (i.e. proper names, locations, ID numbers, phone numbers and e-mail addresses) and so-called “special categories of personal data”. This combination of technologies will allow for a more precise configuration, the immediate application of user requirements, system scalability to new relevant PII, and service improvement with the gradual collection of new documents.

The project will be implemented by CELI, an Italian company with experience in Language Technologies and AI, and ICT Legal Consulting, an international law firm specialized in the fields of ICT, Privacy, Data Protection/Security and Intellectual Property Law.

3.57AnonymAICELIICT Legal Consultingfree text anonyimisation, natural language processing
3.58CASPER 2.0University of Belgrade – School of electrical engineeringO Mundo da Carolina – Associaçao de Apoio a Crianças e Jovensonline child protectionOur consortium has received a Type 1 grant from NGI_Trust for the CASPER project in 1st open call. The main goal of the project was to identify and apply the potentials of using artificial intelligence to protect children on the Internet. The current events, related to COVID-19 pandemics, show that this kind of protection is more relevant than ever since children are spending much more time online.
Different types of content have been analysed, including text, images, video, and audio, as well as different types of online threats. We have also analysed several software architectures to potentially apply in order to develop a high-quality solution with taking care of privacy protection.
As a result of numerous developments, analysis, and testing activities, we have defined CASPER software agent architecture and identified optimal algorithms regarding the criteria mentioned previously. We proved the initial concept, that AI can be applied at the Human-Computer Interaction level to protect children on the Internet. The proposed approach was innovative in terms that there are no other solutions working on that level, capable of analysing all major types of content (visual, audio, and textual), able to respond to different types of threats (porn and nudity, cyberbullying, indoctrination, etc.), and capable of overcoming problems related to content encryption.
Based on the results achieved in this grant period, we created a CASPER pilot demo that represents the selected algorithms effectiveness and the intended way the solution will work:
However, despite the results achieved, we identified few major ways that the solution needs to be improved in:
1. Achieving real-time performance;
2. Exploring different deployment models;3. Improving the algorithms effectiveness;
4. Expanding project scope to elderly population;
5. Supporting languages other than English.
Therefore, we are proposing the extension of the project and support from the NGI.
3.65IoTrustOdin Solutions SLDigital Worx GmbhIOT bootstrapping
web decentralisation, personal data management, solid
3.75DeepFakeSidekik OU
fake news / information analysis
3.82FAIR-AI 2.0The University of Cambridge
improved AI algorithms
3.85CassiopeiaIT-Av - Instituto de Telecomunicações - Aveiro (affiliated with University of Aveiro)GR - Gilad Rosner, Birmingham City Universitypersonal data management
3.90MedIAMFabien Imbault 
secure medical IOT devices
3.94IRISResonate Co-operative
SSI, ethical music
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