Confidential Data Tracking via Segmented Encryption
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Solution Overview
Problem
Users are reluctant to share confidential data, such as salary information, due to privacy concerns about data security and usage, making it challenging to collect and maintain accurate and reliable data for statistical analysis.
Innovation Solution
A system that securely collects and tracks confidential data by using a frontend and backend architecture, encrypting user identification and data separately, and employing a machine learning algorithm to determine user eligibility for insights based on engagement scores, ensuring data security and accuracy while providing insights only to users who contribute their own data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If confidential data is collected and stored in a computer system, then statistical analysis and insights can be provided to users, but user privacy concerns and data security risks increase
Solution Approach 1:
The patent segments confidential data into multiple encrypted columns (e.g., first column with encrypted data using first key, second column with encrypted user ID using second key). This segmentation prevents any single key or column from revealing both the user identity and their confidential information, thereby reducing privacy concerns while maintaining data utility for statistical analysis.
Solution Approach 2:
The patent introduces encryption keys as intermediaries between the stored data and the users. The first key encrypts confidential data and the second key encrypts user IDs, creating a layered security structure. This intermediary encryption mechanism allows the system to store and analyze confidential data while protecting user privacy, as neither key alone can compromise the other's protected information.
2Reliability
If user identification and confidential data are stored together, then data accuracy and reliability are improved, but data security and confidentiality are compromised
Solution Approach 1:
The patent divides the storage structure into separate encrypted columns: one column stores confidential data encrypted with a first key, while another column stores user identification encrypted with a second key. This segmentation maintains data accuracy by preserving the association between users and their data (for reliability) while enhancing confidentiality through separate encryption schemes that prevent unauthorized correlation.
Solution Approach 2:
The patent applies different encryption qualities to different parts of the data structure. Confidential data receives encryption with a first key, while user identification receives encryption with a second key. This local differentiation of encryption strategies allows the system to maintain overall data accuracy while providing targeted confidentiality protection for sensitive user information.
3Productivity
If all users are provided with insights from confidential data, then user engagement and data contribution are increased, but system complexity and resource consumption increase
Solution Approach 1:
The patent implements a feedback mechanism where users who contribute confidential data receive insights generated from that data. The system processes encrypted data to generate statistical insights and selectively provides them back to contributing users. This feedback loop encourages continued data contribution while managing system complexity through efficient processing of encrypted data without requiring full decryption.
4Reliability
If confidential data is encrypted using multiple keys, then data security is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies encryption as a preliminary action during data ingestion and storage, rather than during query processing. By pre-encrypting confidential data and user IDs with their respective keys before storage, the system establishes strong security without adding computational overhead during insight generation and user interactions, thus reducing processing time delays.
Data Source
AI summary
In an example embodiment, a submission of confidential data is received from a user via a first computerized user interface. An identification of the user is obtained, and details regarding the submission are stored in a submission table. Then a request to display, to the user, statistical information derived from confidential data from users other than the user, is received from a second computerized user interface. Based on information stored in the submission table, a determination is made that the request to display, to the user, statistical information derived from confidential data from users other than the user should be granted. In response to the determining, the statistical information derived from confidential data from users other than the user is displayed via the second computerized user interface.