Structured Data Masking for Privacy-Safe Third-Party Sharing
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Solution Overview
Problem
Existing systems share structured data records with third-party applications, exposing personal user information and consuming excessive resources due to the inclusion of entity names, locations, and dates, thereby compromising privacy and resource efficiency.
Innovation Solution
A server applies masks to structured data records before transmission, replacing sensitive information with categories or removing identifiers, and generates pseudo-random identifiers for masked entries, reducing data size and conserving resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If structured data records with entity names, locations, and dates are shared with third-party applications, then the completeness and utility of the data is improved, but user privacy is compromised and resource consumption increases
Solution Approach 1:
The patent extracts and removes sensitive information (entity names, locations, dates) from structured data records before sharing with third-party applications. This extraction process retains the essential transactional data while eliminating the harmful elements that compromise user privacy, directly resolving the contradiction between data completeness and privacy protection.
Solution Approach 2:
The patent applies different quality treatments to different parts of the data structure. Sensitive fields such as entity names, locations, and dates are masked or removed, while non-sensitive transactional data is preserved in full detail. This local differentiation allows the system to maintain data utility where needed while protecting privacy where required.
2Loss of information
If complete structured data records are transmitted to third-party applications, then the data utility is improved, but power and processing resources are excessively consumed
Solution Approach 1:
The patent extracts and removes unnecessary sensitive information from structured data records before transmission. By taking out entity names, locations, and dates that are not essential for the third-party application's core functionality, the system reduces the overall data size and consequently lowers the power and processing resources required for transmission and handling.
3Loss of information
If sensitive information is included in shared data records, then the information value for third-party applications is improved, but the security and privacy protection is weakened
Solution Approach 1:
The patent systematically extracts sensitive information (entity names, locations, dates) from structured data records before sharing with third-party applications. This extraction maintains the essential transactional value of the data while eliminating the privacy-sensitive elements, thereby preserving information value without compromising security.
Solution Approach 2:
The patent applies selective masking to specific sensitive fields within the data structure while preserving non-sensitive transactional data. This local quality approach ensures that each data element is treated according to its sensitivity level, maintaining information value where appropriate and protecting privacy where required.
Data Source
AI summary
In some implementations, a server may receive, from a user device, an indication of a privacy setting associated with an account of a user. The server may further receive, from a third-party application, a request for information associated with the account of the user. The server may apply one or more masks to entries in a structured data record that is associated with the account of the user, to generate masked entries. The server may further generate pseudo-random identifiers for the masked entries. The server may transmit, to the third-party application, the masked entries in association with the pseudo-random identifiers.