Position Paper: Personal Data and IDS (Proposal)

[anilturkmayali]

Personal Data in the IDS Framework

1. Executive Summary

• Quick overview of how personal data is handled within the IDS Framework.
• Core takeaways for data space implementers.

2. Introduction

• Purpose of the Paper: Practical guidance for data space implementers working with personal data.
• The Role of Personal Data in IDS: Clarifying that IDS is applicable to both industrial and personal data.
• Scope: Addressing key questions and concerns related to personal data in data spaces.

3. Key Principles for Personal Data Handling in IDS

-What is Personal Data?: A clear definition of personal data with references to international standards (not limited to European examples).

• Data Sovereignty and Protection:

  • Maintaining control over personal data while aligning with GDPR and other privacy regulations.
  • Personal data sovereignty within data spaces.

• Trust, Interoperability, and Compliance:

  • Ensuring trust among participants through consent management, secure data exchange, and GDPR compliance.
  • Implementing semantic interoperability mechanisms to maintain the integrity of personal data across different systems and spaces.
  • Participants and Data Roles:
    • Distinction between participants as organizations vs individuals acting on behalf of organizations.
    • How organizations manage personal data belonging to individuals.
    • Roles of data rights holders in relation to the data creator, provider, and consumer.

4. Managing Personal Data in Data Spaces

• Consent, Usage Control, and Minimization:

  • Mechanisms for managing and revoking consent dynamically within IDS connectors, ensuring GDPR compliance.
  • What is Consent?: Defining consent for data processing and sharing, ensuring it aligns with individual rights.
  • Data minimization: Only collecting and processing necessary personal data.
  • Usage control policies and enforcing data sovereignty rules, including data provenance and traceability.

• Anonymization, Pseudonymization, and Cross-Border Transfers:

  • Techniques to protect personal data privacy in compliance with legal requirements.
  • Handling cross-border data transfers while addressing consent and legal frameworks.
  • Relationship to Privacy-Enhancing Technologies (PETs):
    • Incorporating privacy-preserving technologies to ensure secure data sharing and processing.
    • Examples of PETs that enhance data protection in IDS.

• Role of Data Intermediaries:

  • For certain use cases, such as B2C scenarios and Digital Product Passports (DPP), individuals may need mechanisms (e.g., data intermediaries) to participate in data spaces.
  • How organizations act as personal data intermediaries.
  • Data intermediaries as trusted agents, enabling individuals to manage their data and interact securely in data spaces while maintaining compliance with regulations.

5. Best Practices for Implementing Personal Data Handling

• Onboarding and Managing Participants:

  • Practical steps for onboarding and managing personal data providers and consumers, with a focus on certification and compliance.
  • Recognizing the role of data intermediaries in onboarding individuals into data spaces.

• Data Quality, Governance, and Lessons Learned:

  • Ensuring high data quality and governance standards in personal data management, with case studies and examples.
  • Addressing challenges like cross-border transfers and governance issues to ensure smooth data operations in data spaces.

6. Frequently Asked Questions (FAQ)

• Is IDS limited to industrial data, or does it cover personal data as well?
• How does IDS comply with GDPR for personal data?
• How is consent managed in IDS data spaces?
• How can personal data usage be monitored and audited in IDS?
• Can IDS work alongside personal data frameworks like Solid?
• What mechanisms are in place to revoke access to personal data if needed?
• How does IDS ensure secure data exchange across different jurisdictions?
• How do data intermediaries facilitate individual participation in data spaces?
• What are Privacy-Enhancing Technologies (PETs), and how are they applied in IDS?

7. Challenges and Best Practices

• Lessons Learned from Existing Implementations.

8. Conclusion

• Summary of key insights.

9. References

• (IDS-RAM, IDSA Rulebook, GDPR, and other relevant frameworks).

[anilturkmayali]

Working group meetings' participants = contributors

[anilturkmayali]

Hi all, here is the updated structure based on the feedback received from IMEC colleagues:

Personal Data and IDS Position Paper – Counter-Proposal Structure

1. Executive Summary

• Quick overview of how personal data is handled within the IDS Framework.
• Core takeaways for data space implementers.

2. Introduction

• Definition of personal data:
  Provides a clear definition of personal data, with references to global standards like ISO/IEC and GDPR, emphasizing the need for consistent definitions across jurisdictions.

• Overview of IDS framework for data spaces, including key participants:
  Introduces the IDS framework and the different participants involved in data spaces, such as organizations, individuals, and intermediaries handling personal data.

• Key role of regulatory frameworks for compliant use of personal data:
  Explains the importance of complying with regulations such as GDPR, Data Governance Act (DGA), and others when working with personal data in IDS.

• Practical guidance for data space implementers working with personal data:
  Offers practical advice to data space implementers on how to handle personal data, ensure compliance, and protect individual rights within data spaces.

3. Key Principles for Personal Data Handling in IDS

• Trust:
  Trust is a fundamental principle in personal data exchanges. It involves ensuring that all parties in the data space can rely on responsible data flows and maintaining a balance between confidentiality, integrity, and availability of personal data.

  • Definition of trust:
    Trust in the IDS framework is about ensuring secure and transparent data sharing that benefits all participants.

  • Responsible data flows based on mutually beneficial exchanges:
    Data exchanges should be built on trust, ensuring all parties benefit from the exchange while respecting data rights and sovereignty.

• Personal Data Protection:
  Compliance with data protection regulations such as GDPR ensures that personal data is processed lawfully and that individuals' rights are upheld within data spaces.

  • EU GDPR compliance:
    Clarifies the legal roles, responsibilities, and obligations of organizations under GDPR, ensuring data protection and individual rights. (+eIDAS regulation)

  • EU strategy for data (DGA):
    Explains how the Data Governance Act (DGA) supports personal data sovereignty and the role of intermediation service providers and altruism organizations.

  • Compliance with other jurisdictions:
    Highlights the challenges of handling cross-border data transfers and ensuring compliance with various international regulations.

• Interoperability:
  For personal data to flow seamlessly across different systems, there must be semantic, legal, technical, and organizational interoperability.

  • Semantic interoperability:
    Ensures that data meaning is preserved across different systems, enabling efficient and secure exchanges.

  • Legal interoperability:
    Explores how different legal frameworks interact to enable cross-border and cross-jurisdictional data sharing.

  • Technical interoperability:
    Focuses on aligning technologies and standards for secure data exchanges.

  • Organizational interoperability:
    Addresses how business-like roles (e.g., data consumers or providers) align with legal roles and societal functions.

4. Managing Personal Data in Data Spaces

• Mechanisms for building trust:
  Explores the processes that establish and maintain trust within personal data spaces, from legal agreements to technology solutions.

  • Negotiation on legal ground and purpose:
    Focuses on ensuring that all data exchanges are legally grounded and consent is properly managed.

  • Data minimisation:
    Ensures that only the necessary personal data is collected and processed, reducing exposure to risk.

  • Usage policies and secondary reuse:
    Policies that define how data can be used and reused while ensuring compliance with data sovereignty principles.

• Data subject’s rights exercising:
  Outlines how individuals can exercise their rights under data protection laws, such as access, correction, and erasure of their data.

• Auditing & compliance:
  Describes how auditing mechanisms are put in place to ensure compliance with data protection regulations and governance policies.

• Privacy-preserving technologies:
  These technologies, such as anonymization and pseudonymization, help protect personal data while allowing for secure and compliant data sharing.

  • Access/usage control:

  • Anonymization, Pseudonymization:

• Data intermediaries as trusted agents:
  Data intermediaries help manage personal data exchanges by ensuring that the data is handled securely and in compliance with regulations.

5. Best Practices for Implementing Personal Data Handling

• Practical steps for onboarding and managing personal data providers and consumers:
  Outlines clear steps for onboarding participants into data spaces, ensuring they comply with certification and governance requirements.

• Practical steps for data publishing and usage:
  Provides guidance on how to publish and use personal data within a data space, including managing metadata like data provenance and usage policies.

• Practical steps for auditing:
  Explains how to audit personal data practices to ensure ongoing compliance with privacy regulations and best practices.

6. Existing Implementations

Presents case studies or examples of how personal data is currently managed within IDS-aligned data spaces. It highlights best practices, challenges, and lessons learned from existing implementations, providing practical insights for future projects.

7. Conclusion

Summarizes the key insights gained from exploring personal data handling in IDS. It reiterates the importance of trust, sovereignty, compliance, and privacy-preserving technologies for building secure and user-centric personal data spaces.

8. References

Includes all relevant frameworks, legal documents, and technical standards referenced in the position paper, such as GDPR, the Data Governance Act (DGA), IDS-RAM, and other regulations applicable to personal data handling in data spaces.

[AvdWB]

I like the structure, but I miss the "Why". Let's add in the introduction a paragraph why the handeling of personal data in IDS is necessary and relate this to the eIDAS implementation. We could distinguish a pull factor (Why does IDS needs to process personal data) and a push factor: (Why does eIDAS have shortcomings in sharing personal data, and How IDS could help out)

Furthermore perhaps it would be a good idea to plot IDS to other data sharing solutions. (Solid, Decentralized Web Nodes, ...)