Ensuring Purpose Limitation in Large-Scale Infrastructures with Provenance-Enabled Access Control.

Area of Science:

• Computer Science
• Information Science
• Data Privacy

Background:

• Modern data infrastructures generate vast amounts of personal information for personalized services.
• Legal and regulatory frameworks mandate transparent data use aligned with collection purposes to preserve privacy.
• Data transformation and aggregation in large-scale, distributed systems often lead to loss or misinterpretation of the original data collection purpose.

Purpose of the Study:

• To propose a method for preserving the authorized collection purposes of personal data in large-scale infrastructures.
• To enhance transparency and accountability in data usage for individuals and auditing authorities.

Main Methods:

• Integrating data collection purpose as immutable, append-only metadata (provenance) within data resources.
• Utilizing an access control mechanism to retrieve provenance information for data-usage verification.

Main Results:

• Ensured purpose limitation for personal data in large-scale infrastructures.
• Provided a transparent audit trail for personal information usage.
• Enabled data-usage verification through an access control mechanism querying immutable provenance.

Conclusions:

• Immutable provenance metadata effectively preserves data collection purposes in complex infrastructures.
• This approach enhances privacy protection by ensuring purpose limitation and providing transparency for data usage verification.