Clean Room Machine Learning for Privacy-Compliant Data Sharing
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Solution Overview
Problem
Data privacy laws restrict the sharing of data between adversarial parties, hindering machine learning processes such as targeted advertising and customer assessment, as existing technologies fail to provide a privacy-compliant environment for data sharing.
Innovation Solution
A clean-room based machine learning system that creates a privacy-safe environment for data sharing, using data entities and tools like SQL engines and machine learning capabilities, enforcing privacy compliance by denying access to individual user data and safeguarding against privacy attacks, allowing for differential privacy synthetic data generation and secure machine learning model training and inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is shared between adversarial parties for machine learning, then machine learning accuracy and model performance improve, but user privacy and data security are compromised
Solution Approach 1:
The patent implements a clean room environment that acts as an intermediary between adversarial parties. This secure enclave allows machine learning models to access and process data from multiple sources without the parties directly sharing data outside the controlled environment. The clean room mediates data access by providing controlled, privacy-preserving interfaces that enable ML operations while preventing unauthorized data exposure, thus resolving the contradiction between ML accuracy and privacy protection.
Solution Approach 2:
The patent creates an inert, secure environment (clean room) that isolates sensitive data from external access. Within this controlled atmosphere, machine learning operations can proceed with access to necessary data without the risks associated with traditional data sharing. The clean room acts as a protective barrier that maintains data security while enabling the computational processes needed for accurate machine learning, thereby eliminating the trade-off between privacy and model performance.
2Object-affected harmful factors
If data is restricted for privacy compliance, then user privacy is protected, but machine learning processes are hindered
Solution Approach 1:
The patent segments the data access process into controlled components within the clean room environment. Instead of restricting all data access entirely, the system divides data access into authorized, monitored operations that occur within the secure enclave. This segmentation allows privacy compliance to be maintained while enabling specific machine learning tasks to proceed with the necessary data, thus resolving the contradiction between privacy protection and ML efficiency.
Solution Approach 2:
The clean room environment provides a universal platform that serves multiple functions: it protects user privacy through controlled access mechanisms while simultaneously enabling various machine learning operations. The system is designed to handle diverse ML workloads (training, inference, evaluation) within the same privacy-preserving framework, making the restriction mechanism itself multi-functional rather than purely limiting, thereby maintaining both privacy and productivity.
3Loss of time
If direct data sharing is implemented, then machine learning model training is accelerated, but privacy compliance is violated
Solution Approach 1:
The patent introduces a clean room intermediary that enables rapid machine learning model training without direct data sharing between adversarial parties. The clean room provides a secure environment where data can be efficiently accessed and processed for training purposes while maintaining privacy compliance through controlled access protocols. This intermediary approach eliminates the time loss associated with traditional secure data transfer methods while ensuring regulatory compliance is maintained throughout the training process.
Data Source
AI summary
Devices, systems, and methods are provided for encapsulating machine learning in a clean room to generate a goal-based output. A method may include identifying, by a device operating within a clean room, an agreement between multiple parties to share data for use in machine learning to generate a goal-based output; retrieving the data; selecting the machine learning model based on a goal indicated by the agreement; generating, using the data as inputs to the selected machine learning model, a first set of probabilities indicative that a respective user may perform an action; generating, using the selected machine learning model and the first set of probabilities, a second set of probabilities indicative that a respective user may perform the action; generating the goal-based output based on the second set of probabilities; and sending the goal-based output from the clean room to a destination location.