Confidential Data Quality Scoring and Privacy Protection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
There is a challenge in ensuring the confidentiality and accuracy of sensitive data, such as salary information, in computer systems, as users are hesitant to share due to privacy concerns and the risk of misuse or incorrect usage, leading to difficulties in collecting reliable data.
Innovation Solution
A system is implemented that uses a confidential data frontend to collect and track sensitive information, encrypting it separately from user identifiers, and employing machine learning to determine user eligibility for insights and data submission, while protecting against inference attacks through timestamp modification and k-anonymity, ensuring secure and accurate data usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If confidential data is collected and stored in a computer system, then data accuracy and reliability are improved, but user privacy security deteriorates due to potential misuse or incorrect usage
Solution Approach 1:
The patent segments confidential data into multiple independent components using secret sharing schemes, where no single component reveals information about the original data. This allows the system to store and process data accurately while maintaining privacy security, as the data remains encrypted until reassembled with sufficient shares.
Solution Approach 2:
The patent introduces trusted intermediaries or secure computation protocols that enable data processing without direct access to the raw confidential information. These intermediaries facilitate accurate data collection and analysis while preserving user privacy by preventing any single entity from viewing the complete confidential data.
2Quantity of substance
If confidential data is collected from users, then data quantity is improved, but user trust deteriorates due to privacy concerns and risk of misuse
Solution Approach 1:
The patent implements feedback mechanisms where users can verify how their confidential data is being used and ensure it aligns with their intended purposes. This builds user trust by providing transparency and control, encouraging more users to contribute data while maintaining privacy protections.
Solution Approach 2:
The patent enables users to maintain control over their own confidential data through self-service mechanisms, allowing them to manage their data shares, control access permissions, and revoke consent when needed. This empowers users and builds trust by demonstrating that the system respects user autonomy.
3Productivity
If data submission is allowed without restrictions, then data collection efficiency is improved, but data accuracy deteriorates due to fraud and incorrect entries
Solution Approach 1:
The patent performs preliminary validation and verification of data submissions before they are fully processed and stored. This includes checking data consistency, verifying user credentials, and detecting potential fraud patterns early in the submission process, thereby maintaining high data accuracy without significantly impacting collection efficiency.
Solution Approach 2:
The patent replaces manual data verification processes with automated computational methods, including machine learning models and cryptographic verification protocols. This substitution maintains data accuracy by detecting fraudulent or incorrect entries while preserving data collection efficiency through automated rather than manual review.
Data Source
AI summary
In an example embodiment, a method for protecting against incorrect confidential data values in a computer system is provided. A machine learning algorithm is used to train a confidential data value quality score based on metrics extracted from member profile and member usage information in a social networking service. The confidential data value quality score model is then used to output an estimated quality score for submitted confidential data values.