Masking Engine for Privacy-Preserving Application Monitoring
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Solution Overview
Problem
Existing software monitoring tools face challenges in protecting user privacy by inadvertently exposing personal information in reports generated from user interactions with applications, as they often send sensitive data over networks without adequate masking.
Innovation Solution
A monitoring system with a report analyzer that determines whether values in reports are personal or non-personal by using a masking engine to mask potentially personal information and unmask it only when it is deemed non-personal based on statistical analysis across multiple reports, ensuring privacy preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If monitoring tools send all reported information over the network for analysis, then developers can obtain comprehensive application performance data, but user privacy is compromised due to exposure of personal information
Solution Approach 1:
The system performs preliminary masking of potentially personal information before transmitting reports over the network. The masking engine proactively identifies and masks sensitive data elements in advance, ensuring that personal information is protected while still allowing application performance data to be collected and analyzed.
Solution Approach 2:
The masking engine acts as an intermediary between the report generation process and network transmission. It processes the reported information, selectively masking personal data while preserving application performance metrics, thereby mediating between the need for data collection and privacy protection.
2Object-affected harmful factors
If the system masks all potentially personal information in reports, then user privacy is protected, but useful application performance information may be lost or obscured
Solution Approach 1:
The masking engine applies masking selectively to specific data elements within reports rather than uniformly masking all information. It identifies which fields contain potentially personal information and masks only those specific elements, while leaving application performance metrics and other non-personal data unmasked and accessible for analysis.
Solution Approach 2:
The system changes the state of data elements dynamically - applying masking transformations only to identified personal information fields while maintaining the original state of non-personal application performance data. This parameter-based selective masking preserves useful information while protecting privacy.
3Object-affected harmful factors
If the system transmits minimal masked information over the network, then user privacy is maintained, but developers receive insufficient data for effective application analysis
Solution Approach 1:
The masking engine applies consistent masking rules uniformly across all reports, creating a homogeneous treatment of personal information while preserving the structure and completeness of application performance data. This ensures that all sensitive fields are protected consistently while maintaining data uniformity for analysis purposes.
Data Source
AI summary
Examples disclosed herein relate, among other things, to an apparatus that includes a processor and a non-transitory machine-readable medium on which is stored instructions that, when executed by the processor, cause the processor to generate a report, the report including a value, access a memory, the memory storing indications of whether values are to be masked, determine that the value is to be masked based on the accessed memory, and substitute the value in the report with a masked value.