Machine Learning Model Predicts Confidential Data Values
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Solution Overview
Problem
Users are reluctant to share confidential data, such as salary information, due to privacy concerns, and existing technologies face challenges in ensuring data confidentiality and providing relevant insights from sparse data submissions.
Innovation Solution
A system utilizing a machine learning model to predict confidential data values based on user submissions and social networking profiles, combined with secure data encryption and anonymization methods to maintain user privacy and incentivize data sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users are asked to submit confidential data such as salary information, then the system can provide salary insights and statistics, but users become reluctant to share due to privacy concerns and data sparsity occurs
Solution Approach 1:
The patent introduces a machine learning model as an intermediary that predicts confidential data values based on publicly available profile information. This mediator allows the system to obtain salary insights without requiring users to directly submit sensitive information, thus resolving the contradiction between data availability and user privacy concerns
Solution Approach 2:
The system creates a copy or prediction of confidential data using machine learning algorithms that analyze user profiles, job postings, and market data. Instead of requiring actual confidential submissions, the system generates predictive copies of salary information, maintaining user privacy while providing actionable insights
2Quantity of substance
If the system collects sparse confidential data submissions, then some salary insights can be provided, but the data remains insufficient for comprehensive analysis
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing publicly available information such as user profiles, job descriptions, and market data before confidential data submission is needed. This preparatory data collection enables the machine learning model to generate accurate predictions even with limited confidential data inputs
Solution Approach 2:
The patent changes the parameters of data collection by shifting from requiring direct confidential data input to using predictive modeling based on alternative parameters such as profile attributes, job posting data, and market trends. This parameter transformation maintains analysis quality while reducing reliance on sparse confidential submissions
3Loss of information
If the system uses machine learning to predict confidential data values, then data sparsity is addressed, but the complexity of the system increases
Solution Approach 1:
The machine learning model serves multiple functions: it predicts salary values, analyzes market trends, generates insights, and adapts to different data availability scenarios. This multi-functionality justifies the increased complexity by providing comprehensive solutions to multiple data-related challenges simultaneously
Data Source
AI summary
In an example, one or more job postings, as well as corresponding confidential data values, are obtained from a social networking service. A first set of one or more features are extracted from the one or more job postings. The first set of one or more features and corresponding confidential data values are fed into a machine learning algorithm to train a confidential data value prediction model to output a predicted confidential data value for a candidate job posting. Then, the candidate job posting is obtained and a second set of one or more features are extracted from the candidate job posting. The extracted second set of one or more features is fed to the confidential data value prediction model, outputting the predicted confidential data value.