Constraint Manager for Collaborative Intelligence
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Solution Overview
Problem
Existing data sharing techniques face challenges in ensuring data privacy, controlling access, and facilitating collaborative intelligence while managing concerns around data completeness, privacy, and competitive advantage.
Innovation Solution
The implementation of a data trustee environment that utilizes constraint computing and constraint querying to derive collaborative intelligence from shared data, ensuring that underlying raw data remains private and that access is controlled through configurable constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is shared to derive collaborative intelligence, then the completeness and value of datasets improve, but data privacy and security concerns 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 data, allowing collaborative intelligence to be derived without direct access to raw data by consumers. This mediator structure enables data sharing benefits while maintaining privacy through controlled access mechanisms and constraint-based querying that prevents exposure of sensitive information.
Solution Approach 2:
The data access control mechanism is segmented into multiple layers: data ownership rights, trustee management rights, and consumer query rights. Each segment has specific permissions and constraints. This segmentation allows different parties to access different aspects of the data according to their needs and authorization levels, enabling collaborative intelligence while maintaining privacy through granular access control.
2Reliability
If access control mechanisms are implemented to protect data privacy, then data security improves, but ease of data sharing and collaborative access worsens
Solution Approach 1:
Access control policies and constraints are established in advance when data is entrusted to the trustee environment. Data owners define their privacy requirements, access rules, and computational constraints before data sharing occurs. This preliminary configuration enables seamless collaborative access during runtime without requiring complex real-time authorization checks, thus maintaining both security and ease of operation.
Solution Approach 2:
The data trustee environment automatically enforces access control policies and constraint validation without requiring manual intervention from data owners or consumers. The system self-manages authorization checks, query constraint validation, and access decision-making based on pre-configured policies. This automation maintains strong security controls while keeping data sharing operations simple and straightforward for users.
3Object-affected harmful factors
If constraint computing is applied to control data access, then data privacy protection improves, but computational complexity and processing time worsen
Solution Approach 1:
Computational constraints and privacy rules are compiled and validated in advance during data ingestion and trust establishment. The trustee environment pre-processes and stores constraint specifications in optimized formats that enable efficient runtime evaluation. This preliminary preparation reduces the computational burden during actual data access operations, maintaining strong privacy protection while minimizing processing overhead.
Solution Approach 2:
The system transforms complex privacy constraints into simplified parameter-based validation rules that can be efficiently checked during query execution. By converting detailed privacy policies into structured constraint parameters (such as aggregation requirements, sensitivity thresholds, and access conditions), the system maintains rigorous privacy protection while enabling fast computational evaluation through parameter comparison rather than complex logical reasoning.
4Reliability
If underlying raw data is kept private to maintain competitive advantage, then business security improves, but ability to derive collaborative intelligence worsens
Solution Approach 1:
Instead of sharing actual raw data, the trustee environment creates and manages copies or representations of the data for collaborative processing. Data owners retain exclusive access to the original data while authorized consumers access controlled copies through the trustee. This copying mechanism enables collaborative intelligence derivation from data representations while the original competitive advantage-protecting data remains private and under full owner control.
Solution Approach 2:
The trustee environment acts as an intermediary that enables collaborative intelligence derivation without requiring direct access to raw data by external parties. The trustee holds the data, performs authorized computations, and returns results that preserve competitive advantage. This mediator structure allows businesses to benefit from collaborative intelligence while maintaining control over their proprietary data assets.
Data Source
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Figure 2
Figure 3A~3B
AI summary
Embodiments of the present disclosure are directed to techniques for monitoring and orchestrating the use and generation of collaborative data in a trustee environment subject to configurable constraints. A user interface can be provided to enable tenants to specify desired computations and constraints on the use and access to their data. A constraint manager can communicate with various components in the trustee environment to implement the constraints. For example, requests to execute an executable unit of logic such as a command or function call may be issued to the constraint manager, which can grant or deny permission. Permission may be granted subject to one or more conditions that implement the constraints, such as requiring the replacement of a particular executable unit of logic with a constrained executable unit of logic. As constraints are applied, any combination of schema, constraints, and/or attribution metadata can be associated with the data.