ExpectedOutcome:
Projects’ results are expected to contribute to all of the following outcomes:
- Improved capabilities of European Police Authorities and other relevant security practitioners for a fast and flexible analysis of huge amounts of heterogeneous data through the application of robust and advanced tools, allowing them to efficiently fight criminals and terrorists who use novel technologies;
- Enhanced and modern analysis of heterogeneous data as well as training curricula that take into account legal and ethical rules of operation, cost-benefit considerations, as well as fundamental rights such as privacy and protection of personal data, providing reports that can be used in court;
- The work of European Police Authorities in the area of fighting crime and terrorism is supported by big data analysis that is in accordance with data minimisation principles and high privacy standards, with clearly identified challenges, adequate models and scientifically validated technical options for tackling the challenge proposed and solutions developed that meet the challenge.
Scope:
With the constant increase of technological developments, the processing of large datasets is inevitable for police work in today’s digital world. As a wide range of products and services become digitalised and interconnected, Police Authorities need adequate technologies to properly detect and counter emerging threats. Big data analysis also provides invaluable opportunities to carry out investigations, identify suspects, reveal or anticipate crime patterns or links between previously unconnected events or actors. In particular, there is a continuous need for handling large, complex and unstructured datasets, in order to gather, normalise, process, connect, prioritise, visualise the data (including text, image, audio and video) in ways that facilitate the extraction of actionable intelligence, while ensuring interoperability between existing systems and standards in different Member States. Solutions to perform temporal and geospatial analyses are needed too. The successful proposal should have a clear strategy related to quality data sets to be used for training and testing. The innovation efforts should provide support to web-based data analysis that can facilitate e.g. the fight against hate speech, human trafficking, terrorism or child sexual exploitation in an online environment. The work should include surface, deep and dark web.
Examples of relevant techniques include: examination of digitally captured signatures, identification of voice cloning and of deepfakes; detection and recognition of persons/objects/logos; speaker diarisation and identification; speech recognition and transcription into text; automatic classification of text based on risk factors; optical character recognition; named entity recognition; concept extraction, extraction of entities and relations between them in unstructured text; multimodal analytics, in order to discover insights and patterns in large volumes of data through clustering, as well as the identification of user communities and key actors in the social networks being formed online; automatic correlations among all available sources, as well as cross-checking, cross-matching and mapping information between different cases, i.e. cross-reference with existing records in databases of Police Authorities. Identification of perpetrators can also be enhanced by detecting their online behaviour and habits, e.g. which days/hours they are used to login/logout.
Taking advantage of these modern technologies will require Police Authorities to move away from business models based on data input to data evaluation. It will require robust and reliable information management structures that encompass all aspects from data collection to handling, evaluation, exploitation and data security. In particular, key principles such as lawfulness of processing and data minimisation should apply to ensure that Police Authorities conduct data analysis in full compliance with fundamental rights and EU privacy and personal data protection legal framework. For example, it may be necessary to filter and reduce large datasets to what is relevant for operational support activities and in investigations, and/or apply methods such as differential privacy. Hence, all these efforts should also reconcile big data analysis and data protection, i.e.: explore challenges to conduct big data analysis in accordance with data minimisation principles and data protection by default standards, propose possible models and scientific options to tackle the challenge, and develop solutions (digital tools) that meet the challenge, focusing on triage and clustering functions. Possibilities of assessing and preventing bias and discrimination as a result of big data analysis should be analysed too. The successful proposal should thus help framing the issue of big data analysis for Police Authorities, providing guidelines as well as operational tools to comply with EU data protection law.
The successful proposal should build on the publicly available achievements and findings of related previous national or EU-funded projects as well as create synergies with similar on-going security research projects from the Calls 2021-2022 on Fighting Crime and Terrorism in the area of modern information analysis, in order to avoid duplication and to exploit complementarities as well as opportunities for increased impact.
In this topic the integration of the gender dimension (sex and gender analysis) in research and innovation content should be addressed only if relevant in relation to the objectives of the research effort.
Proposals funded under this topic are expected to engage with the Europol Innovation Lab during the lifetime of the project, including validating the outcomes, with the aim of facilitating future uptake of innovations for the law enforcement community.
Possibilities of coordination with related activities funded through the Internal Security Fund (such as the European Anti-Cybercrime Technology Development Association) and the Digital Europe Programme should be analysed too.
Specific Topic Conditions:
Activities are expected to achieve TRL 7-8 by the end of the project – see General Annex B.
