Brain Activity Graph Aggregation for Privacy-Preserving Data Handling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current solutions for handling brain activity data in immersive scenarios are not privacy-preserving and can be costly, with existing security methods being computationally expensive and prone to information leakage, especially when dealing with large numbers of users.
Innovation Solution
A monitoring device constructs a similarity module from sensor data, performs interaction analysis, and creates an aggregated activity graph, which is then provided to a network node for privacy-preserving and resource-efficient communication, allowing for two levels of aggregation: high-resolution and compact representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If brain activity data is directly shared with content generators, then personalized content recommendation can be achieved, but privacy security deteriorates
Solution Approach 1:
The patent introduces an intermediary processing system that transforms raw brain activity data into aggregated activity graphs before sharing with content generators. This intermediary layer preserves privacy by design while enabling personalized recommendations through the aggregated patterns in the activity graphs.
Solution Approach 2:
Instead of sharing original brain activity data, the system creates a copy in the form of aggregated activity graphs that capture essential patterns for personalization without containing identifiable individual information. This copy serves the recommendation function while maintaining privacy security.
2Object-affected harmful factors
If security measures are applied to protect brain activity data, then privacy security is improved, but communication cost deteriorates
Solution Approach 1:
The patent extracts only the essential aggregated patterns from brain activity data, removing unnecessary detailed information. This extraction reduces the volume of data that needs to be transmitted and stored while maintaining the core functionality for personalized recommendations.
Solution Approach 2:
Instead of securing large volumes of raw brain activity data through expensive encryption and protection mechanisms, the system inverts the approach by first aggregating and anonymizing the data, then applying lighter security measures to the already-reduced dataset.
3Measurement precision
If high-resolution brain activity data is transmitted, then measurement precision is improved, but resource efficiency deteriorates
Solution Approach 1:
The patent applies different levels of data resolution to different purposes: high-resolution data is processed locally to extract essential patterns, while only aggregated low-resolution patterns are transmitted for communication. This local quality differentiation maintains measurement precision where needed while improving resource efficiency in transmission.
4Measurement precision
If detailed sensor data from multiple sensors is collected, then measurement precision is improved, but device complexity deteriorates
Solution Approach 1:
The patent merges data from multiple sensors into a unified aggregated activity graph that captures overall brain activity patterns. This combining approach maintains the measurement benefits of multiple sensors while reducing the complexity of handling and processing individual sensor streams separately.
Data Source
AI summary
A monitoring device for handling brain activity data of a user. The monitoring device obtains sensor data from a plurality of sensors indicating a brain activity of one or more regions in a brain of the user. The monitoring device constructs a similarity module of brain activities based on an activity configuration defining one or more of the sensors to gather sensor data from and obtained sensor data from the one or more sensors, wherein the similarity module includes one or more groups of sensor data related to one another. The monitoring device performs an interaction analysis within and/or between the one or more groups in the similarity module by creating a network of interactions within and/or between the one or more groups. The monitoring device creates an aggregated activity graph based on the constructed similarity module, the performed interaction analysis, and a configured aggregation level of sensor data.