Generative Adversarial Networks for Privacy-Preserving Data Labeling
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Solution Overview
Problem
Current machine learning techniques require manual labeling of training data, which is time-consuming and costly, and centralizing user data on a server risks user privacy, especially when manually annotating labeled data for supervised learning.
Innovation Solution
The system crowdsources labeling of training data by using client devices to generate proposed labels for unlabeled data via a generative adversarial network, applying privacy mechanisms like differential privacy to mask user identities, and transmitting privatized labels to a server for aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of training data is used, then data accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The system enables data to label itself through self-supervised learning mechanisms. The neural network automatically generates labels for training data without human intervention by identifying patterns and relationships within the data itself, thereby maintaining accuracy while eliminating time-consuming manual labeling
Solution Approach 2:
The patent replaces the mechanical process of manual data labeling with an automated computational system. A neural network processes and labels training data through algorithmic operations, substituting human manual work with machine-based automated labeling that maintains consistency and scalability
2Productivity
If user data is centralized on a server for manual annotation, then labeling efficiency is improved, but user privacy is compromised
Solution Approach 1:
The system introduces federated learning as an intermediary mechanism that enables collaborative model training without direct data sharing. The neural network architecture allows multiple devices to contribute to model improvement while keeping their local data private, thus maintaining privacy while achieving efficient collaborative labeling
Solution Approach 2:
The patent segments the centralized data processing task into distributed local processing units. Each device performs labeling operations locally on its own data without transmitting sensitive information to a central server, thereby maintaining privacy while achieving collective labeling efficiency through coordinated distributed computation
3Measurement precision
If more training data is collected, then model accuracy is improved, but data storage and processing complexity increase
Solution Approach 1:
The system performs preliminary data processing and feature extraction at the source devices before data aggregation. By pre-processing data locally to extract relevant features and reduce dimensionality, the system reduces the complexity of subsequent centralized processing while maintaining the quality and accuracy benefits of having extensive training data
Data Source
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AI summary
One embodiment provides for a mobile electronic device comprising a non-transitory machine-readable medium to store instructions, the instructions to cause the mobile electronic device to receive a set of labeled data from a server; receive a unit of data from the server, the unit of data of a same type of data as the set of labeled data; determine a proposed label for the unit of data via a machine learning model on the mobile electronic device, the machine learning model to determine the proposed label for the unit of data based on the set of labeled data from the server and a set of unlabeled data associated with the mobile electronic device; encode the proposed label via a privacy algorithm to generate a privatized encoding of the proposed label; and transmit the privatized encoding of the proposed label to the server.