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Mission

Motivation

The motivations behind the PATTERN project primarily revolve around enhancing the utility of data from multiple sources while maintaining strict confidentiality. The project seeks to address the challenges faced by collaborative cybersecurity systems and distributed/federated data analytics. Traditional encryption methods limit the ability to operate over encrypted data, hindering the potential for advanced federated data sharing technologies.

PATTERN aims to overcome these challenges by developing new cryptographic mechanisms to enable efficient and scalable federated computations on sensitive data without compromising privacy or integrity. This would include innovations in homomorphic encryption and secure multi-party computation to allow operations on encrypted data with minimal overhead.

The project is motivated by the need to provide solutions that offer both privacy protection and integrity guarantees for computations on sensitive data. These motivations are grounded in the realities of current cybersecurity threats and the increasing importance of preserving privacy in data analytics. By addressing these challenges, PATTERN aims to enable more effective and secure collaborative data analytics and cybersecurity practices.

Goals

The PATTERN project aims to significantly advance the field of federated data analytics by enhancing privacy and ensuring data integrity, without sacrificing performance. The main objectives include:

  • Design and Benchmark of Privacy-preserving Technologies: Tailor and develop new cryptographic solutions for federated data analytics that ensure computations are performed without compromising the underlying data's confidentiality. This includes optimizing homomorphic encryption, multi-party computation solutions, and exploring novel cryptographic constructs like Multi-Key Fully Homomorphic Encryption and Multi-Input Functional Encryption.

  • Develop and Prototype Federated Data Analytics Solutions: Create scalable and reliable federated analytics frameworks tailored to real-world use cases. This involves deploying privacy-preserving technologies in two main scenarios: a mail analysis platform and a cybersecurity platform for more robust detection of infected files.

  • Open-source Development and Community Engagement: Share knowledge and experience gained throughout the project, fostering the adoption of privacy-enhancing technologies (PETs) by open-source communities and the broader European ecosystem.

Approach

The PATTERN project employs a multi-phased approach:

  • Concepts and Requirements Analysis: Identifying the roles and adversary models relevant to federated data management and establishing privacy, performance, and functionality requirements.

  • Cryptographic Research and Development: Designing and developing suitable privacy-enhancing technologies that integrate advanced cryptographic mechanisms to operate efficiently over encrypted data.

  • Implementation, Integration, and Validation: Applying the developed privacy technologies within two use cases - mail analytics and cybersecurity, followed by thorough validation and feedback gathering for iterative improvement.

  • Dissemination and Exploitation: Continuously promoting the project’s objectives, research, and outcomes to ensure awareness and adoption among target communities, particularly in the industrial sector.

  • Collaborative Development: The project embraces a collaborative approach, involving multiple partners across academia and industry, ensuring a wide range of expertise and perspectives.

By addressing these goals with a comprehensive and interdisciplinary approach, PATTERN aspires to provide groundbreaking solutions in federated data analytics, enhancing privacy and data integrity for a safer and more efficient digital environment​

Innovations

The PATTERN project stands out for its innovative approach to enhancing the privacy and integrity of federated data analytics. Some of the notable innovations include:

  • Advanced Cryptographic Mechanisms: PATTERN focuses on developing and implementing cutting-edge cryptographic solutions like Multi-Key Fully Homomorphic Encryption (MK-FHE), Multi-Input Functional Encryption (MIFE), and Zero-Knowledge Proofs (ZK & zkSNARKs). These technologies enable secure computations over encrypted data, thus ensuring privacy while maintaining data utility.

  • Tailored Privacy-preserving Techniques: The project aims to adapt and optimize various privacy-preserving techniques specifically for federated analytics applications. This involves customizing homomorphic encryption, secure multi-party computation (MPC), and anonymization methods to fit the unique requirements of analytical operations, thereby improving efficiency and scalability.

  • Integrity Tools: PATTERN is developing tools to ensure the integrity of federated computations. These tools are designed to verify that the operations performed on encrypted data are correct and untampered, providing additional security assurances beyond privacy.

  • Use Case Development and Validation: The project is grounded in real-world applications, with specific use cases in mail analysis and cybersecurity. These scenarios help to demonstrate the practicality and effectiveness of PATTERN's privacy-preserving technologies in actual industrial contexts.

Perspectives

  • Enhanced Data Privacy and Security: By successfully implementing its cryptographic and privacy-preserving innovations, PATTERN aims to set new standards in data privacy and security, particularly in the context of federated analytics. This could lead to widespread adoption in various sectors, including healthcare, finance, and telecommunications.

  • Open-source Development and Community Engagement: PATTERN is committed to open-source development and aims to foster a vibrant ecosystem around privacy-enhancing technologies. By sharing knowledge, tools, and best practices, the project seeks to encourage widespread adoption and continuous improvement of these technologies.

  • Impact on Industry and Academia: The solutions developed in PATTERN are expected to have significant impacts on both industry and academia. For industry, it offers a way to leverage data analytics while ensuring customer privacy, which can enhance trust and compliance with regulations like GDPR. For academia, the project provides new challenges and datasets for research, advancing the field of privacy-preserving technologies.

  • Standardization and Policy Influence: By demonstrating the feasibility and benefits of its innovations, PATTERN aims to influence standards and policies related to privacy and data protection. This could lead to more robust privacy guidelines and better integration of privacy-enhancing technologies in standard data processing practices.

  • Long-term Vision and Scalability: The project has a long-term vision that transcends immediate solutions. By addressing fundamental challenges in privacy-preserving federated analytics, PATTERN lays the groundwork for future innovations and applications, ensuring that the technologies developed are scalable and adaptable to evolving data landscapes and regulations​