Reusable Data Privacy Pipeline Templates for Secure Collaboration

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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 preventing data sharing due to concerns over competitive advantage, regulatory restrictions, and the labor-intensive nature of data processing.

Innovation Solution

A data collaboration tool that bundles data pipelines and contracts into a reusable template app, allowing developers to create placeholder elements, enabling participants to fill in placeholders without exposing underlying data, and supports debugging with sample data or virtual assets to protect privacy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If data sharing is implemented to bridge gaps in datasets, then valuable intelligence and insights can be derived, but data privacy concerns and regulatory restrictions prevent sharing

Engineering Contradiction:

Improvecompleteness of datasetVSAvoiddata privacy risk

Core Design Contradiction:

Loss of informationVSObject-affected harmful factors

Solution Approach 1:

A trusted third-party environment (TEE) is introduced as an intermediary between data providers and data consumers. The TEE securely holds and processes data without exposing it to external parties, enabling data sharing while maintaining privacy through cryptographic proofs and controlled access mechanisms.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The data sharing system is segmented into distinct functional components: data ingestion modules, processing modules within TEE, output generation modules, and contractual governance layers. This segmentation allows each component to operate with appropriate security measures and privacy protections tailored to its specific function.

Inventive Principle:

Principle #1Segmentation

2Reliability

If custom data pipelines are built for each collaboration, then data privacy can be controlled, but the process becomes labor-intensive and complex

Engineering Contradiction:

Improvedata privacy controlVSAvoidpipeline configuration complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

Pre-configured pipeline templates with embedded privacy controls and best practices are prepared in advance. These templates include predefined data ingestion, processing, and output modules that can be rapidly deployed and customized for specific collaborations, eliminating the need to build pipelines from scratch each time.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The pipeline templates are designed to be universal and multi-functional, capable of handling various data types and collaboration scenarios through configuration rather than structural changes. A single template framework serves multiple purposes across different use cases, reducing overall system complexity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Productivity

If reusable templates are used to simplify pipeline creation, then development efficiency improves, but customization for specific privacy requirements becomes difficult

Engineering Contradiction:

Improvepipeline development speedVSAvoidprivacy requirement customization

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The pipeline templates incorporate dynamic configuration capabilities that allow parameters, data sources, and processing logic to be adjusted based on specific collaboration requirements. This dynamic nature enables the same template to adapt to different privacy needs without sacrificing the efficiency gains from reusability.

Inventive Principle:

Principle #15Dynamics

4Reliability

If debugging is performed with production data, then real exceptions can be detected, but data privacy is compromised through diagnostic logs

Engineering Contradiction:

Improveexception detection accuracyVSAvoiddata leakage through logs

Core Design Contradiction:

ReliabilityVSObject-generated harmful factors

Solution Approach 1:

Synthetic test data that mirrors the structure and characteristics of production data is created as a safe copy for debugging purposes. This synthetic data allows comprehensive exception detection and pipeline validation without exposing sensitive production information, maintaining privacy while enabling thorough testing.

Inventive Principle:

Principle #26Copying

Data Source

PatentUS12572336B2Initiating data privacy pipelines using reusable templates

Publication Date: 2026.03.10 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Implementations are directed to developing and facilitating a data collaboration using a data collaboration tool that bundles data pipelines and governing contracts into a data collaboration app. The data collaboration tool may include an authoring mode and may include an electronic canvas that visually represents all contracts and pipelines of the data collaboration app on a single canvas and visually represents traceability from the contracts to the pipeline elements they enable. A developer may use authoring mode to develop a template app that includes placeholder elements, including a reference to an anonymous placeholder participant. The template app may be shared, and a recipient may invite data collaborators to fill in the placeholder elements and deploy the app, enabling the data collaborators to trigger the data pipelines to execute in a data trustee environment to generate insights from each other's assets without exposing the assets to the collaborators or the developer.