Dialog System PII Protection via Audio Classification and Selective Encryption
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Solution Overview
Problem
Existing systems face challenges in protecting personally identifying information (PII) during customer interactions in dialog systems, as they need to balance data security with the requirement to log interactions for quality improvement and dispute resolution without exposing sensitive data to third-party hosts.
Innovation Solution
A dialog system that classifies audio data turns and takes security actions such as suppression or encryption based on classification, using a classification module and security action module to protect PII by storing only non-identifying information or encrypting sensitive data, ensuring that PII is not logged or is logged in a protected format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If audio data is logged for quality improvement and dispute resolution, then data security is compromised, but if audio data is not logged, then quality improvement and dispute resolution capabilities are lost
Solution Approach 1:
The system extracts and removes personally identifying information from audio data before logging, keeping only non-identifying portions. This allows the system to maintain logging capabilities for quality improvement and dispute resolution while protecting customer privacy and security.
Solution Approach 2:
The system applies different processing treatments to different portions of the audio data based on its sensitivity. Personally identifying information is suppressed or encrypted, while non-identifying information is logged in plain text, creating local variations in data protection quality throughout the log.
2Adaptability or versatility
If third-party hosts are used for dialog system services, then system functionality is improved, but customer trust is reduced due to PII exposure risks
Solution Approach 1:
The system introduces an intermediary processing layer that anonymizes audio data before it is stored or accessed by third-party hosts. This intermediary step removes personally identifying information while preserving the functional utility of the dialog data, allowing third-party hosting without compromising customer trust.
Solution Approach 2:
The system performs preliminary suppression of personally identifying information before the data reaches third-party hosts. By removing PII in advance, the system prevents potential security risks and maintains customer trust while still enabling third-party hosting for system functionality.
3Reliability
If all audio data is encrypted for security, then data security is improved, but processing efficiency and accessibility are reduced
Solution Approach 1:
The system applies encryption or suppression only to the portion of audio data that contains personally identifying information, rather than encrypting all audio data. This partial action maintains security for sensitive portions while preserving processing efficiency and accessibility for non-sensitive portions.
Solution Approach 2:
The system applies different security treatments to different portions of the audio data based on sensitivity. Personally identifying information receives encryption or suppression treatment, while non-identifying information remains in plain text for efficient processing and accessibility.
Data Source
AI summary
In one embodiment, a method comprises classifying a representation of audio data of a dialog turn in a dialog system to a classification. The method may further comprise taking a security action on the classified representation of the audio data of the dialog turn as a function of the classification. The security action can be suppressing the representation of the audio data, encrypting the representation of the audio data, releasing the representation of the audio data, partially suppressing the representation of the audio data, partially encrypting the representation of the audio data, partially releasing the representation of the audio data, or a command.