Telemetry Data Anonymization via Field Segmentation
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Solution Overview
Problem
Modern software systems face challenges in anonymizing sensitive information within telemetry data to prevent exposure to third parties, such as IP addresses, machine information, and user details, while still allowing analysis for performance insights.
Innovation Solution
A telemetry data set is processed to identify fields that require anonymization based on predefined rules, with masking techniques like encryption and hashing applied to sensitive fields, generating an anonymized data set that includes both masked and unmasked information as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If telemetry data is collected for analysis, then performance insights are improved, but sensitive information exposure risk increases
Solution Approach 1:
The telemetry data is segmented into different fields with different sensitivity levels. The system identifies and separates sensitive fields (containing PII, network information, machine information) from non-sensitive fields, applying different handling rules to each segment. This allows performance analysis on non-sensitive data while protecting sensitive information through targeted anonymization.
Solution Approach 2:
An anonymization service acts as an intermediary between the telemetry data collection system and third-party analysis systems. This service receives telemetry data, applies anonymization rules to mask sensitive information, and outputs anonymized data that can be safely shared with third parties for performance analysis without exposing sensitive information.
2Reliability
If anonymization is applied to sensitive fields, then data privacy is improved, but data usability for analysis may deteriorate
Solution Approach 1:
Different quality levels of anonymization are applied to different data fields based on their sensitivity and analytical value. Critical analytical fields receive minimal or no anonymization to preserve usability, while sensitive fields receive appropriate anonymization. The system allows configurable anonymization strategies per field to balance privacy and usability requirements.
Solution Approach 2:
The system changes the parameters of sensitive data fields through anonymization techniques such as masking, hashing, or generalization. These parameter changes transform identifiable information into anonymized forms that protect privacy while maintaining the structural integrity and analytical utility of the telemetry data for performance analysis.
3Measurement precision
If comprehensive telemetry data is collected, then analysis accuracy is improved, but complexity of data handling increases
Solution Approach 1:
Anonymization rules and schemas are defined in advance before telemetry data collection. The system pre-configures which fields require anonymization and applies these rules automatically during data collection or processing. This preliminary setup reduces the complexity of handling comprehensive telemetry data by establishing clear, automated anonymization workflows rather than requiring manual processing of each data field.
Data Source
AI summary
According to one aspect of the present disclosure, a telemetry data set, which includes a plurality of data fields associated with application operations, is collected for an application. A request for at least a portion of the telemetry data set is received and processed. Data fields in the telemetry data set that are to be anonymized are identified based on the request, with information in the identified data fields is to be anonymized according to corresponding anonymization rules. The information in each of the identified data fields is masked based on the corresponding anonymization rule for the data field, and an anonymized telemetry data set is generated. The anonymized telemetry data set includes the masked information for the identified data fields and unmasked information for other data fields indicated in the request.