Confidential Data Association via Encrypted Slices
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Solution Overview
Problem
Users are reluctant to share confidential data, such as salary information, due to privacy concerns, and existing systems face challenges in ensuring the data remains confidential and is used only for specific purposes, leading to accuracy and reliability issues.
Innovation Solution
A system that uses a confidential data frontend to collect and submit data, encrypting it separately from user identification, and employing a machine learning algorithm to determine user eligibility for insights based on attributes, ensuring secure storage and anonymization to maintain user privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If confidential data is collected and stored in a computer system, then the system can provide statistical-based analysis and insights to users, but users become reluctant to share the data due to privacy concerns about security and data usage
Solution Approach 1:
The patent segments confidential data into multiple independent slices, where each slice contains only a portion of the original data. No single slice can reveal the complete confidential information, thus protecting user privacy while still enabling aggregate statistical analysis across all slices combined.
Solution Approach 2:
The patent introduces encrypted slice identifiers as an intermediary mechanism between user identification and confidential data. These identifiers allow the system to associate data with users for accountability and eligibility verification without exposing direct links between user identities and their confidential information.
2Reliability
If confidential data is encrypted and stored separately from user identification, then user privacy is protected, but it becomes difficult to ensure data accuracy and reliability
Solution Approach 1:
The patent uses encrypted slice identifiers as intermediaries that maintain the link between user accounts and confidential data slices without exposing the actual data or direct user identification. This allows the system to verify data authenticity, check user eligibility for viewing insights, and ensure data accuracy while maintaining strong privacy protection.
Solution Approach 2:
The patent implements feedback mechanisms where the system verifies user eligibility based on attributes and provides appropriate insights only to qualified users. This feedback loop ensures data reliability by confirming that users have the proper authorization and relevant attributes to view specific confidential data insights.
3Productivity
If the system provides insights from confidential data to users, then user engagement increases, but users may not trust that their data will be used only for intended purposes
Solution Approach 1:
The patent uses encrypted slice identifiers as intermediaries that enable the system to control and track data usage. These identifiers allow the system to verify user eligibility, enforce usage policies, and ensure that confidential data is only accessed and used for intended purposes while still providing valuable insights to engaged users.
Solution Approach 2:
The patent implements feedback mechanisms that verify user eligibility and attributes before providing insights from confidential data. This feedback control ensures that users only receive data appropriate for their profile and purpose, preventing misuse while maintaining high user engagement through personalized and relevant insights.
Data Source
AI summary
In an example embodiment, a submission of confidential data is received from a user. A first service is queried using an identification of the user to obtain a member profile corresponding to the user in a social networking service. One or more primary attribute values are identified from the member profile. The one of the primary attribute values are used to query a second service to obtain a derived attribute value corresponding to the one or more primary attribute values. The confidential data, one or more of the primary attribute values, and the derived attribute value are stored in a first submission table in a confidential information database. Then the one or more of the primary attribute values and the derived attribute value are used to classify the user into one or more slices.