Confidential Data Frontend Backend Architecture for Secure Skill Valuation
Find Innovative SolutionsGenerate Solutions
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 the data remains confidential and is only used for specific purposes, making it difficult to provide accurate recommendations while maintaining user privacy.
Innovation Solution
A system that securely collects, tracks, and utilizes confidential data by using a confidential data frontend and backend architecture, ensuring encryption of user identification and data, and employing machine learning techniques to assign monetary values to skills based on submitted data, allowing for secure and anonymized insights and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If confidential data is collected and utilized for recommendations, then recommendation accuracy is improved, but user privacy and data security are compromised
Solution Approach 1:
The patent introduces multiple intermediaries between the user and the recommendation system: (1) encryption algorithms that transform confidential data into protected formats, (2) anonymization processes that remove identifying information while preserving statistical properties, and (3) secure computation protocols that allow analysis without direct data access. These intermediaries enable the system to utilize confidential data for accurate recommendations while maintaining user privacy through layered protection mechanisms.
2Reliability
If confidential data is encrypted and protected, then user trust is improved, but data usability for machine learning is reduced
Solution Approach 1:
The patent applies parameter changes by transforming the state of confidential data through controlled encryption levels and selective anonymization. Instead of fully encrypting all data (which would render it unusable), the system adjusts encryption parameters to protect only sensitive portions while leaving other data in usable formats. This allows machine learning models to access and process data effectively while maintaining appropriate security protections.
3Measurement precision
If more confidential data is collected, then statistical insights accuracy is improved, but user reluctance increases
Solution Approach 1:
The patent implements preliminary action by establishing strong encryption and anonymization protocols before data collection begins. By pre-configuring robust security measures and clearly communicating these protections to users, the system builds trust upfront that encourages participation. Users are more willing to share confidential data when they know protective actions have already been taken, thereby enabling the collection of sufficient data for accurate statistical insights.
Data Source
AI summary
In an example, each of a plurality of members of social networking service is mapped to a weighted skill vector, each weighted skill vector including a list of skills for the member with an associated weight indicating strength of the skill. Members of the social networking service that belong to an industry are aggregated to obtain a weighted matrix of members and skills along with compensation vectors indicating compensation for each of the members in the matrix. The weighted matrix of users and skills and corresponding compensation vectors is used to train a machine learning skill monetary value prediction model to output a predicted monetary value for one or more skills contained in a candidate vector fed to the machine learning skill monetary value prediction model.