PRESERVE | Ethical and Privacy-preserving Big Data platform for Supporting criminal investigations

Summary
This proposal aims to develop an innovative and privacy-preserving decision-support system for European law enforcement authorities, leveraging advanced Big Data and AI technologies to effectively combat crimes and terrorism. The proposed system integrates Federated Learning, User and Entity Behavior Analytics (UEBA), and other Big Data and AI techniques to monitor social network data, deep and shallow web information, and police databases in a secure, collaborative, privacy-aware and ethical manner.
The primary objective is to help Law Enforcement Authorities (LEAs) fighting cybercrime and terrorism by identifying key communities and users involved in activities such as hate speech, child sexual abuse, terrorism, or drug trafficking and to use this information to better allocate police resources.
PRESERVE will leverage Federated Learning, a decentralised machine learning approach that allows model training on distributed data sources while preserving data privacy. By collaborating with multiple LEAs across Europe, PRESERVE will collectively combine social network data, deep and shallow web information, and police databases to analyse large amounts of spatial and temporal data related to criminal activities to identify patterns and correlations to provide better police-resource management on critical areas.
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More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/101168309
Start date: 01-09-2024
End date: 31-08-2027
Total budget - Public funding: 6 497 337,50 Euro - 5 338 197,00 Euro
Cordis data

Original description

This proposal aims to develop an innovative and privacy-preserving decision-support system for European law enforcement authorities, leveraging advanced Big Data and AI technologies to effectively combat crimes and terrorism. The proposed system integrates Federated Learning, User and Entity Behavior Analytics (UEBA), and other Big Data and AI techniques to monitor social network data, deep and shallow web information, and police databases in a secure, collaborative, privacy-aware and ethical manner.
The primary objective is to help Law Enforcement Authorities (LEAs) fighting cybercrime and terrorism by identifying key communities and users involved in activities such as hate speech, child sexual abuse, terrorism, or drug trafficking and to use this information to better allocate police resources.
PRESERVE will leverage Federated Learning, a decentralised machine learning approach that allows model training on distributed data sources while preserving data privacy. By collaborating with multiple LEAs across Europe, PRESERVE will collectively combine social network data, deep and shallow web information, and police databases to analyse large amounts of spatial and temporal data related to criminal activities to identify patterns and correlations to provide better police-resource management on critical areas.

Status

SIGNED

Call topic

HORIZON-CL3-2023-FCT-01-01

Update Date

23-12-2024
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Horizon Europe
HORIZON.2 Global Challenges and European Industrial Competitiveness
HORIZON.2.3 Civil Security for Society
HORIZON.2.3.2 Protection and Security
HORIZON-CL3-2023-FCT-01
HORIZON-CL3-2023-FCT-01-01