Data Privacy Pipeline Configuration for Collaborative Dataset Access
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Solution Overview
Problem
Existing data sharing techniques face challenges in facilitating collaborative intelligence while ensuring data privacy and controlling access, often hindered by legal restrictions, data confidentiality concerns, and the high cost and labor intensity of data observation and analysis.
Innovation Solution
A graphical user interface is used to construct and configure a data privacy pipeline in a data trustee environment, allowing multiple parties to specify parameters for data sharing and access, using placeholder elements and parameterized access control to enable multi-party contributions and contractual agreements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is shared to improve dataset completeness, then collaborative intelligence can be developed, but data privacy and security concerns arise
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 computations and returning results without allowing direct access to the raw data. This mediator structure enables collaborative intelligence development while maintaining data privacy, as neither the data owner nor the consumer can directly access the other's data.
2Reliability
If data access control is implemented to protect privacy, then data security is improved, but data sharing and collaboration are hindered
Solution Approach 1:
The system implements dynamic access control where permissions and data availability are adjusted based on the computational context and trust relationships. The data trustee can dynamically grant or revoke access rights, adjust data visibility, and modify computation permissions based on the specific collaborative scenario, enabling both security and flexibility.
Solution Approach 2:
The system changes parameters of data access such as visibility, modifiability, and query permissions based on the trust level and contractual agreements between parties. Different parameter settings are applied to different data elements or computational steps, allowing fine-grained control that balances security requirements with collaboration needs.
3Productivity
If placeholder elements are used to facilitate multi-party contributions, then pipeline development is accelerated, but system complexity increases
Solution Approach 1:
Placeholder elements are pre-defined templates that represent future data sources, computations, or transformations in the pipeline. These placeholders allow the pipeline structure to be designed and validated in advance without requiring all actual data and computations to be ready, accelerating the collaborative development process while the trustee system manages the complexity of resolving and connecting these placeholders.
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.