Context-Aware Data Filtering for Voice Assistant Security
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional technologies fail to effectively analyze and mitigate security risks associated with the provision of user-requested information, especially in dynamically changing environments, when using personal assistants that rely on voice commands and audio channels, leading to potential unauthorized access.
Innovation Solution
A method utilizing a mobile device with multiple sensors (geolocation, audio, video, G-sensor, velocity, gyroscope, accelerometer, and wireless communication adapters) to generate a trained statistical model of the user's environment, determining data security values to filter information based on the probability of unauthorized access and confidentiality, thereby granting or denying access to user-requested data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complete refusal to use audio channel for data exchange is implemented, then information security is improved, but effectiveness and convenience of personal assistants significantly deteriorates
Solution Approach 1:
The patent applies local quality by differentiating security measures based on the specific information being accessed. Instead of completely blocking audio channels, the system evaluates each information request individually and applies filtering only when necessary. The personal assistant continues to function normally for non-sensitive queries while implementing security checks for confidential data, thus maintaining convenience while improving security where needed.
Solution Approach 2:
The patent introduces an intermediary security filtering mechanism between the user's audio commands and the personal assistant's data access. This intermediary layer analyzes the requested information, evaluates security risks based on environmental context from sensors, and selectively blocks or permits data access. This allows audio channels to remain open for convenient interaction while preventing unauthorized access to sensitive information.
2Reliability
If conventional technology solutions are used to limit interaction, then some security concerns are addressed, but direct analysis of security risks and dynamic mitigation capabilities are lost
Solution Approach 1:
The patent implements dynamics by creating a real-time, adaptive security system that continuously monitors environmental context through multiple sensors and adjusts data access permissions dynamically. Unlike static conventional solutions, this system evaluates security risks moment-by-moment based on current conditions such as location, surrounding devices, and network environment, enabling flexible and context-aware security decisions.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors environmental data from sensors, analyzes security risks based on this feedback, and adjusts information filtering decisions accordingly. The system learns from environmental patterns and user interactions, dynamically adapting its security posture to mitigate emerging risks while maintaining appropriate access to information.
3Reliability
If multiple sensors and statistical models are deployed for security assessment, then unauthorized access probability is reduced, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the security assessment system into modular components: individual sensors (microphone, accelerometer, GPS, etc.) that collect specific environmental data, a statistical model that processes this data, and a filtering mechanism that executes security decisions. This modular architecture allows the system to implement comprehensive security checks while maintaining manageable complexity through clear separation of functions.
Data Source
AI summary
A method for controlling secure access to user requested data includes retrieving information related to potential unauthorized access to user requested data. The information is collected by a plurality of sensors of user's mobile device. A trained statistical model representing an environment surrounding a user is generated based on the retrieved information. A first data security value is determined using the generated trained statistical model. The first data security value indicates a degree of information security based on user's environment. A second data security value is determined using the generated trained statistical model. The second data security value indicates a degree of confidentiality of the user requested data. The user requested data is filtered based on a ratio of the determined first data security value and the second data security value.