Confidential Data Engagement Model Using 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 confidential data for statistical analysis.
Innovation Solution
A system that uses a confidential data frontend to collect and encrypt user-submitted data, storing it in a backend database with separate encryption keys for identifying and confidential data, ensuring security and anonymity, and employs a machine learning algorithm to determine user eligibility for insights based on engagement scores and thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If confidential data is collected and stored for statistical analysis, then data accuracy and reliability are improved, but user privacy security deteriorates
Solution Approach 1:
The patent segments confidential data into two separate encrypted columns: one containing user identifying information encrypted with a first key, and another containing confidential data encrypted with a second key. This segmentation ensures that even if one column is compromised, the other remains protected, thereby maintaining privacy security while enabling statistical analysis through decrypted data segments.
Solution Approach 2:
The patent introduces an encrypted transaction number as an intermediary that links user identifying information with confidential data without exposing either directly. This intermediary enables the system to associate data for statistical analysis while maintaining security through encryption, resolving the contradiction between data reliability and privacy security.
2Loss of information
If confidential data is collected from users, then statistical analysis capability is improved, but user trust deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where users who contribute confidential data receive prioritized access to insights and statistical analysis results. The system tracks user submissions and uses this feedback to incentivize continued participation, thereby maintaining user trust while enhancing statistical analysis capability through ongoing data collection.
Solution Approach 2:
The patent enables users to voluntarily contribute confidential data through self-service mechanisms, where users can submit their own information and directly benefit from the statistical insights generated. This self-service approach empowers users to control their data contribution while ensuring data accuracy and maintaining trust through transparent benefits.
3Object-affected harmful factors
If encryption is applied to protect confidential data, then privacy security is improved, but data accessibility deteriorates
Solution Approach 1:
The patent applies local quality by encrypting only the specific columns containing sensitive information (user identifying information and confidential data) while leaving other operational data accessible. The encryption keys are managed locally within the system, allowing selective decryption for authorized statistical analysis operations, thereby maintaining both security and accessibility.
Solution Approach 2:
The patent changes the encryption parameter state dynamically: data is encrypted at rest for security, but can be decrypted to plaintext state when authorized for statistical analysis. The system manages key parameters to control this transformation, ensuring that privacy security is maintained during storage while data accessibility is restored during authorized operations.
Data Source
AI summary
In an example embodiment, a machine learning algorithm is used to train an engagement score model to calculate an engagement score for a particular member indicating a probability that the particular member would increase engagement with the social networking service if provided with statistical information about confidential data submitted by other members. Member usage information is obtained corresponding to a first member of a social networking service. Then a plurality of features are extracted from the member usage information corresponding to the first member. This plurality of features is inputted into the engagement model to obtain an engagement score for the first member. It is then determined whether or not to provide statistical information to the first member about confidential data submitted by other members based on the engagement score for the first member.