Emotion-Aware Data Transmission Control via Facial Recognition
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Solution Overview
Problem
There is a need to prevent the malicious transmission of sensitive information and misleading information from secure networks to unauthorized locations, as employees with legitimate access can inadvertently or intentionally leak confidential data, and unauthorized access can also lead to the transmission of false or private information outside the secure network.
Innovation Solution
A system and method that uses facial expression recognition and mood classification to detect the emotional state of end users, implementing a buffer delay and image capture to assess reactions before allowing the transmission of sensitive files, thereby canceling transmissions if negative reactions are detected, ensuring that sensitive information is not leaked outside the secure network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional access control methods are used to prevent data leakage, then network security is maintained, but employees can still inadvertently or intentionally transmit sensitive information outside the secure network
Solution Approach 1:
The system continuously monitors user facial expressions and emotional states, using this feedback to dynamically adjust transmission permissions. The mood classification system provides real-time feedback about user emotional state, which triggers appropriate responses (allowing or blocking transmission) based on the assessed risk level
Solution Approach 2:
The system introduces an intermediary layer between the user and the data transmission process. This intermediary (the mood analysis system) assesses user emotional state and acts as a gatekeeper, deciding whether to permit or block transmission based on the detected mood, thereby preventing direct unmonitored access
2Reliability
If all transmission attempts are blocked to prevent data leakage, then data security is improved, but legitimate business communications are hindered
Solution Approach 1:
The system applies different levels of security control to different transmission scenarios based on local conditions (user mood state). Rather than uniformly blocking all transmissions, it selectively permits or blocks based on the specific emotional context, allowing legitimate communications during positive/neutral moods while blocking during negative moods
Solution Approach 2:
The system changes the security parameter (transmission permission) based on the detected emotional parameter (mood classification). When user mood transitions from positive/neutral to negative, the system dynamically adjusts the transmission permission from allowed to blocked, creating adaptive security control
3Measurement precision
If facial recognition and mood analysis systems are implemented to assess user emotional state, then transmission control accuracy is improved, but system complexity and processing time increase
Solution Approach 1:
The system uses a multi-functional approach where the facial recognition system serves multiple purposes: identification, emotion detection, and mood classification. This universal system handles various functions through a single integrated approach, reducing the need for separate specialized systems for each function
4Measurement precision
If buffer delay is implemented to capture images and assess user reactions, then transmission control accuracy is improved, but transmission speed is reduced
Solution Approach 1:
The system performs preliminary mood assessment and image capture during the buffer delay period before final transmission decisions are made. This preliminary action allows the system to prepare the emotional context information in advance, enabling faster final decision-making when transmission is actually needed
Data Source
AI summary
Methods for preventing the transmission of sensitive information to locations outside of a secure network by a person who has legitimate access to the sensitive information are described. In some embodiments, in order for an end user of a computing device to establish a secure connection with a secure network and access data stored on the secure network, a client application running on the computing device may be required by the secure network. The client application may monitor visual cues (e.g., facial expressions and gestures) associated with the end user, detect suspicious activity performed by the end user based on the visual cues, and in response to detecting suspicious activity may perform mitigating actions to prevent the transmission of sensitive information such as alerting human resources personnel or requiring authorization prior to sending information to locations outside of the secure network.