Governed Federated Learning for the Social Security Sector

• Fraunhofer HHI

One of the key trends in the digital transformation of organizations is the unprecedented tendency towards data-driven insights. Like many sectors, the stakeholders in social security face the data sharing - data protection dilemma. On the one hand, they own monetizable data that could significantly improve their services or generate new revenue streams. On the other hand, they face strict data protection regulations, such as GDPR and HIPAA, which restrict data sharing and the development of data-driven applications. Steering the contradiction between data utilization and data privacy is one of the challenges faced by organizations within healthcare, insurance, pensions, and other areas of social security.

Federated Learning (FL) offers a compelling solution to address this challenge by facilitating secure and collaborative data utilization. FL enables collaborative ML model training across distributed data sources, without requiring the sharing of the data itself. Basically, unlike centralized leaning approaches, FL allows organizations to keep data within their premises while allowing Machine Learning (ML) models to be trained on them locally. Leveraging FL, only model updates are shared and aggregated centrally, thereby ensuring privacy. This approach addresses both privacy concerns and regulatory compliance while empowering organizations to benefit from variety of data-driven insights.

There are several applications of FL within the social security sector. Insurance providers can collaborate securely to develop more accurate predictive models for fraudulent claim detection or personalized policy pricing without compromising confidential client data. Pension funds can jointly optimize financial risk models, leading to better management of long-term liabilities and improved outcomes for beneficiaries. Additionally, FL offers valuable opportunities for organizations to derive insights from private customer data without compromising individual privacy, enabling enhanced personalized services and customer satisfaction.

FL also has significant implications for clinical research, especially in safeguarding intellectual property and sensitive patient data in multi-stakeholder clinical trials. It allows researchers to collaboratively build robust analytical models for faster and more accurate diagnosis, discover drugs in a more efficient way, all while adhering to strict privacy and intellectual property protection.

The practical application of FL in social security, primarily healthcare, faces additional challenges due to the lack of inherent robust governance mechanisms to ensure compliance and protect data ownership. To address these limitations, frameworks such as Eclipse Data Space Components (EDC) provide transparent, enforceable policy management that ensures adherence to clearly defined rules regarding data ownership, access, and usage. Such governance mechanisms are instrumental in fostering trust among stakeholders, maintaining data sovereignty, and ensuring compliance with regulatory standards.

Privacy-enhancing technologies, including Differential Privacy, Secure Multi-Party Computation (SMPC), and Homomorphic Encryption, further augment the security offered by FL. These technologies ensure that even the aggregated data remains secure, offering robust protection against inference attacks and ensuring the privacy of the underlying datasets.

By enabling a secure, compliant, and collaborative ecosystem, FL enhanced with EDC represents a significant step forward in enabling the social security sector to unlock its data’s full potential. It enables a new era of data-driven insights, truly realizing one of the key areas for digital transformation, where confidential and business-critical data can be effectively leveraged to drive innovation without compromising privacy or regulatory standards. FL thus offers a practical and strategic path toward sustainable digital transformation within the social security domain.