Filtering Mapped Datasets to Resolve Data Accuracy Trade-offs
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Solution Overview
Problem
There are challenges in ensuring the confidentiality and accuracy of sensitive data, such as salary information, shared within online social networking services, due to privacy concerns and the difficulty in maintaining data security and reliability.
Innovation Solution
A system is implemented that gathers confidential information from users in cohorts, maps external data to these cohorts, and calculates bias metrics; if a cohort's bias exceeds a tolerable level, the system prevents the display of associated data, ensuring secure, accurate, and reliable handling of sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If public data is integrated into the online social networking service to accentuate information about members, then the information completeness is improved, but the data accuracy deteriorates because it is not readily known which entities in the public data map to members
Solution Approach 1:
The patent introduces a mapping mechanism that acts as an intermediary between public data entities and private network members. This mapping layer enables the system to integrate public data while maintaining accurate identification of which public entities correspond to actual members, thus resolving the contradiction between information completeness and data accuracy
2Quantity of substance
If members provide confidential information such as salary data, then the data quantity is improved, but the data reliability deteriorates due to privacy concerns and security challenges
Solution Approach 1:
The patent segments the data processing into distinct phases: data collection, bias detection, and conditional display. By separating these functions and introducing bias computation as an independent verification layer, the system enables members to provide confidential data while maintaining reliability through automated bias checking before data is displayed to others
Solution Approach 2:
The system implements feedback through bias computation that continuously monitors the submitted data for patterns indicating dishonesty or error. This feedback mechanism allows the system to verify data reliability after collection, maintaining member trust while ensuring data quality before it enters the public view
3Reliability
If the system computes bias metrics for mapped data, then the data reliability is improved, but the device complexity increases
Solution Approach 1:
The patent applies preliminary action by computing bias metrics in advance, before the mapped data is displayed to users. This pre-computation approach allows the complex bias detection logic to be executed once during data processing, and the results stored for later reference, thereby improving reliability without proportionally increasing operational complexity
Data Source
AI summary
In an example, a method includes generating a targeted communication to respective computing devices of one or more members of an online social networking service, electronically collecting the responses to the targeted communication, mapping the responses to a cohort by updating a record in a database of members, the record identifying the cohort for the respective member, filtering information corresponding to a cohort to provide filtered cohort, and suppressing displaying of information corresponding to the cohort in response to the filtered information for the cohort indicating one or more of the biases being above a bias threshold value.