Data Privacy Pipeline Interfaces With Placeholder-Based Access Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data sharing practices face challenges in facilitating collaborative intelligence development while ensuring data privacy and controlling access, due to concerns over data confidentiality, regulatory restrictions, and the high cost and labor intensity of data observation and analysis.
Innovation Solution
A graphical user interface is provided to enable tenants to specify parameters for a contractual agreement to share and access data, allowing for the construction and configuration of a data privacy pipeline with placeholder elements and parameterized access control, enabling multi-party contributions and deployment in a data trustee environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data sharing is implemented to bridge gaps in datasets, then the quality and completeness of collaborative intelligence improves, but data privacy concerns and access control difficulties worsen
Solution Approach 1:
A data trustee environment is introduced as an intermediary between data owners and data consumers. The trustee holds and manages the shared data, executing computational steps and returning results without exposing the actual data to either party. This mediator enables collaborative intelligence development while maintaining data privacy, as neither the data owner nor the consumer directly accesses the raw data.
2Ease of operation
If traditional data sharing methods are used, then data access and collaboration are simplified, but control over data usage and access rights are lost
Solution Approach 1:
The system implements dynamic access control through configurable computational steps and parameterized queries. Data consumers can specify what computations to perform on the shared data without needing direct access to the data itself. The data trustee dynamically executes these steps and returns only the requested results, providing flexible control over data usage while maintaining ease of operation for consumers.
3Loss of information
If data observation and analysis are performed manually, then data insights can be obtained, but the process becomes costly and labor intensive
Solution Approach 1:
Manual data observation and analysis activities are replaced with automated computational steps executed by the data trustee environment. The system accepts defined computational operations (such as aggregations, filtering, or statistical analyses) and automatically processes the shared data to generate insights. This substitution eliminates manual labor and associated costs while maintaining or improving the quality of data insights through systematic, repeatable computational processes.
Data Source
AI summary
Embodiments of the present disclosure are directed to techniques for constructing and configuring a data privacy pipeline to generate collaborative data in a data trustee environment. An interface of the trustee environment can serve as a sandbox for parties to generate, contribute to, or otherwise configure a data privacy pipeline by selecting, composing, and arranging any number of input datasets, computational steps, and contract outputs. (e.g., output datasets, permissible named queries on collaborative data). The interface may allow a contributing party to use one or more unspecified “placeholder” elements, such as placeholder datasets or placeholder computations, as building blocks in a pipeline under development. Parameterized access control may authorize designated participants to access, view, and/or contribute to designated portions of a contact or pipeline. Authorized participants may indicate their approval, and the pipeline may be deployed in the data trustee environment pursuant to the agreed upon parameters.