Flora

FLORA is an innovative health application focusing on ovulation tracking while prioritizing user privacy and data security. Utilizing advanced technologies such as federated learning and blockchain, FLORA ensures user data remains private and secure on their devices. The project aligns with TRUSTCHAIN objectives and leverages cutting-edge machine learning and cryptographic techniques to enhance user experience and data protection.
- Motivation for the project: FLORA addresses the significant privacy concerns prevalent in health applications. Ovulation Tracker apps have become indispensable tools for women. However, recent studies show that many period trackers lack privacy safeguards and share data without user consent, this putting user rights at risk.
- Generic use case description: FLORA provides an ovulation tracking solution that prioritizes user privacy and data security. Users can track their health metrics through a user-friendly mobile application. Federated Learning, PETs and Blockchain are used to minimize data beaches.
- Essential functionalities: A user-friendly health app leveraging Federated Learning PETs: Fully Homomorphic Encryption, Differential Privacy, and Proxy Re-Encryption Digital Identity; Management/Consent System: Manages identities and consent Model Market and Reward System: Users share anonymized data models and receive rewards
- How these functionalities can be integrated within the software ecosystem: The project follows a microservices approach, where each of the suggested functionalities will be implemented using a submodule. This design strategy allows for greater flexibility, as each submodule can be developed and deployed independently. By modularizing functionalities, the project ensures interoperability.
- Gap being addressed: FLORA addresses critical gaps in health tracking applications by significantly enhancing user privacy. Traditional apps often store sensitive health data on centralized servers, making them vulnerable to unauthorized access. FLORA overcomes this in a decentralized approach that aligns with GDPR.
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Expected benefits achieved with the novel technology building blocks:
Enhanced Privacy: Protects user data through advanced encryption and federated learning
User Empowerment: Gives users control over their data
Accurate Ovulation Tracking: Provides reliable health metrics by leveraging ML
Ethical AI Usage: Demonstrates responsible AI deployment in health applications - Potential demonstration scenario: FLORA will be piloted with real end-users to gather feedback and validate its functionalities, ensuring it meets user needs and regulatory requirements.

Team

Pavlos Efraimidis
Professor, Democritus University of Thrace, Affiliated Member, Athena RC

Eli Katsiri
Assistant Professor, Democritus University of Thrace, Affiliated Member, Athena RC

George Drosatos
Researcher, ILSP, ATHENA RC

Vasilis Perifanis
Adjunct Researcher, ILSP, ATHENA RC

Andreas Sendros
PhD Cand. in Blockchain, DUTh

Christos Karasoulas
Android Developer, PhD Cand. DUTh

Periklis Kostamis
PhD Cand. in Blockchain, DUTh

Theodoros Tsiolakis
PhD Cand. in ML, DUTh

Eleni Briola
PhD Cand. in ML, DUTh

Christos Nikolaidis
PhD Cand. in ML, DUTh

Athanasios Vrachinopoulos
OPSIS Director, BSc Computer Science

Romylos Karatzas
ICT Consultant, Computer Scientist
Entities

ATHENA Research Center
ATHENA RC has a great portfolio of research projects spanning a great range of topics, including healthcare and IoT applications.
Website: https://www.athenarc.gr/

OPSIS-Research SRL
OPSIS-Research specializes in providing end-to-end software solutions and consulting services in the domain of Internet of Things, including, but not limited to, services related to Blockchain, IoT, Analytics, Artificial Intelligence and Cognitive Technologies, Machine Learning and Augmented Reality.
Website: https://opsys.ro/