Brain Pattern Access Control for Private Digital Object Retrieval
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Solution Overview
Problem
Existing access control methods for digital objects are cumbersome and insecure, particularly in public spaces where speaking or typing sensitive information is inconvenient and risky.
Innovation Solution
A system that associates a user's brain patterns with digital objects, allowing access through measured physiological signals, such as EEG readings, enabling secure and private access without revealing the digital object information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional access control methods (speaking or typing sensitive information) are used, then access to digital objects can be obtained, but security and privacy are compromised in public spaces
Solution Approach 1:
The patent replaces mechanical input methods (typing, speaking) with a physiological signal-based access control system. EEG sensors detect brain wave patterns to authenticate users, substituting the mechanical interaction of typing passwords or speaking verification information with a non-invasive neurological detection system that provides both security and convenience.
Solution Approach 2:
The system introduces an intermediary layer between the user and the digital object access. Instead of direct authentication through passwords or biometric scanning, the EEG-based brain pattern recognition acts as an intermediary that translates neurological signals into authentication credentials, enabling secure access without direct exposure of sensitive information.
2Loss of information
If sensitive information is spoken or typed in public spaces, then access to digital objects is achieved, but privacy is compromised
Solution Approach 1:
The patent substitutes information-based authentication (passwords, verbal verification) with physiological signal-based authentication. By using EEG sensors to detect and analyze brain wave patterns, the system eliminates the need for users to input or expose sensitive information, thereby preserving privacy while managing authentication through neurological patterns.
Solution Approach 2:
The system creates a copy of the user's brain pattern rather than requiring the user to reveal actual sensitive information. The EEG sensors capture and store a representation of the user's neurological state, which serves as the authentication credential. This copied physiological data enables authentication without exposing the user's actual private information.
3Reliability
If brain patterns are used for access control, then security and privacy are enhanced, but measurement and identification of brain patterns becomes necessary
Solution Approach 1:
The patent replaces complex manual authentication processes with automated physiological signal detection. EEG sensors continuously monitor brain wave patterns, and the system automatically processes these signals to identify unique neurological signatures. This substitution eliminates the need for manual password entry while providing robust security through automated brain pattern recognition.
Solution Approach 2:
The system enables self-service authentication through the user's own brain patterns. During the training phase, the user's EEG signals are captured and stored as reference patterns. During authentication, the system automatically compares incoming EEG signals against stored patterns without requiring user intervention beyond maintaining normal brain activity, thereby simplifying the measurement and identification process.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables secure and private access to digital objects by using brain patterns, enhancing privacy and security in public spaces and allowing revocable access delegation.
Implementation Method 1
Electroencephalograph (EEG) devices include a number of electrodes that are typically positioned at locations along the scalp and face of a person and can measure patterns of voltage fluctuations that result from electrical communications between the person's neurons
Data Source
AI summary
The present disclosure relates generally to systems and methods that enable access control for digital objects based on measured physiological signals of a user. A method of operation of a human mind interface (HMI) system includes measuring, via a physiological sensor of a HMI device, a brain pattern of a user while the user retrieves a digital object after training. The method includes identifying, via a processor of the HMI device, the measured brain pattern stored in a memory of the HMI device, and determining, via the processor, the digital object stored in the memory that is associated with the identified brain pattern. The method includes accessing, via the processor, the digital object and retrieving information contained in the digital object and providing, via the processor of the HMI device, the information contained in the digital object to a recipient on behalf of the user.