Privacy Policy-Driven Emotion Detection With Selective Data Masking
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Solution Overview
Problem
Existing emotion detection systems lack a nuanced approach to user privacy, often employing an all-or-nothing method that fails to consider user preferences and conditions for sharing emotion data.
Innovation Solution
Implementing an affective consent engine (ACE) that applies user-defined privacy policies to emotion detection, allowing users to specify which emotions can be captured and shared under what conditions, with masking techniques applied at various network locations to protect privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If emotion sensing is deployed to detect user emotions, then user privacy is compromised, but emotion detection capability is lost
Solution Approach 1:
The patent segments emotion data into private and non-private categories based on user-defined privacy policies. The affective consent engine divides the emotion detection functionality to allow certain emotions to be detected and shared while keeping others private, thus resolving the contradiction between emotion detection capability and privacy protection.
Solution Approach 2:
The affective consent engine acts as an intermediary between the emotion sensing system and external applications. It enforces privacy policies by filtering and controlling which emotion data is shared, allowing emotion detection to proceed while protecting user privacy through policy-based mediation.
2Loss of information
If all emotion data is collected and processed, then comprehensive emotion analysis is achieved, but computational and storage resources are consumed
Solution Approach 1:
The system extracts only the necessary emotion data that users have permitted to be shared, removing private emotion data from the processing pipeline. This extraction approach maintains comprehensive analysis capability for permitted emotions while reducing computational and storage resource consumption by excluding private data.
Solution Approach 2:
The system performs partial emotion detection by focusing only on emotions that users have authorized for sharing. Rather than processing all possible emotion data, it applies selective processing to permitted emotions, reducing resource consumption while maintaining useful analysis capability.
3Adaptability or versatility
If emotion data is shared with external applications, then application functionality is enhanced, but data security and potential leaks increase
Solution Approach 1:
The affective consent engine serves as a security intermediary that controls data flow between emotion sensing and external applications. It enforces privacy policies to prevent unauthorized data sharing, thus enabling application functionality while mitigating data security risks through policy-based access control.
Solution Approach 2:
The system applies different sharing permissions to different emotion types based on user preferences. Certain emotions are permitted for sharing with specific applications while others remain private, creating localized quality variations in data sharing that enhance application functionality where appropriate while protecting security where needed.
Data Source
AI summary
This disclosure describes techniques for protecting privacy of a user with respect to emotion detection via a computer network. The techniques may include receiving sensed data associated with a user. A privacy policy of the user may be used with processing of the sensed data. For example, based at least in part on the privacy policy, a private subset of the sensed data may be filtered from remaining sensed data. The remaining sensed data may be used to determine an emotion classification result. The emotion classification result may indicate a sharable emotion of the user, for instance.