CN110650007B - Encryption method and system based on brain consciousness - Google Patents
Encryption method and system based on brain consciousness Download PDFInfo
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- CN110650007B CN110650007B CN201810682381.4A CN201810682381A CN110650007B CN 110650007 B CN110650007 B CN 110650007B CN 201810682381 A CN201810682381 A CN 201810682381A CN 110650007 B CN110650007 B CN 110650007B
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- H04L9/08—Key distribution or management, e.g. generation, sharing or updating, of cryptographic keys or passwords
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Abstract
The invention discloses an encryption method based on brain consciousness, which comprises consciousness encryption and consciousness decryption, wherein the consciousness encryption comprises consciousness data acquisition, consciousness data acquisition B, consciousness data analysis and key generation.
Description
Technical Field
The invention relates to the technical field of block chains, in particular to an encryption method and system based on brain awareness.
Background
At present, the encryption technology is seen everywhere in work and life, and the encryption means of the encryption technology is also infinite, but although the encryption means is continuously improved, the password is still stolen and decoded. For example, face recognition based encryption techniques may be counterfeited by homozygote twins or makeup; fingerprint encryption technology can cause fingerprints left by contacted objects (or fingers are broken) to be copied, so that passwords are leaked; the voiceprint encryption technology can cause the sound characteristics to be stolen due to the existence of an eavesdropper; iris encryption techniques can be counterfeited by the presence of a custom contact lens; so many encryption technologies that look unique and reliable have some insecurity problems, mainly because the features extracted by these encryption technologies satisfy universality, uniqueness, stability and collectibility, but do not satisfy forgery resistance, stress resistance, necessary living body detection and concealment. How to provide a key encryption feature not only meets the requirements of universality, uniqueness, stability and collectibility, but also meets the requirements of forgery resistance, stress resistance, necessary living body detection and concealment, which becomes an urgent problem to be solved.
Disclosure of Invention
The invention aims to provide an encryption method based on brain consciousness, and in order to solve the technical problems, the technical scheme adopted by the invention specifically comprises the following steps:
(1) The consciousness encryption specifically comprises the following sub-steps:
(1.1) consciousness data acquisition A: the consciousness collector is worn on the head of a user, the user is prompted to pay attention to a specified position, the consciousness collector collects current generated by electromagnetic wave conversion due to brain activity and amplifies the current, the consciousness collector further records current fluctuation as an analog signal, the analog signal is converted into a digital signal, and the digital signal is stored in consciousness collector storage equipment;
(1.2) consciousness data acquisition B: the consciousness analyzer extracts the data stored in the step (1.1) from the consciousness collector storage device, restores the data into a fluctuation graph, calculates the amplitude and the wave frequency of the fluctuation graph to judge whether the amplitude is smaller than a specified value or not and whether the wave frequency is higher than the specified value or not, if one of the amplitude and the wave frequency is not satisfied, continuously collects brain activity data of a user, if both of the amplitude and the wave frequency are satisfied, the activity inducer starts to work, a screen is driven to display specific information, the user generates brain activity after seeing the specific information, the consciousness collector collects current converted by electromagnetic waves generated by the brain activity and amplifies the current, the consciousness collector further records the current fluctuation as an analog signal, the analog signal is converted into a digital signal, and the digital signal is stored in the consciousness collector storage device;
(1.3) consciousness data analysis: driving a screen to display another specific information, continuously and repeatedly reading the brain activity of the user through an awareness collector, respectively converting the repeatedly read brain activity data into oscillograms, calculating the similarity of the oscillograms through a specific algorithm, continuously and repeatedly displaying the specific information and reading the brain activity of the user if the similarity is smaller than the specific value until the similarity of a large segment is higher than the specific value, carrying out normalization processing on the region, and generating the region into a fixed characteristic value by adopting a data summarization algorithm;
(1.4) Key Generation: repeating the consciousness acquisition and analysis steps, acquiring the characteristic value again, restarting the consciousness acquisition and analysis steps again if the characteristic values acquired twice are different to generate a new characteristic value until the characteristic values are the same, using the characteristic value as a key seed to generate a key pair, then keeping the key, externally publishing a convention, encrypting data by using a password, externally publishing the encrypted data, and finishing consciousness encryption;
(2) Consciousness decryption: and (3) the decryption requester makes a verification request by using an external convention, the decryption requester repeats all the processes in the step (1) and obtains a specific private key, the private key is used for decrypting data, if the decryption fails, the decryption requester is not matched, namely the decryption requester is not an encryption person, and if the decryption succeeds, the decryption requester is matched, namely the decryption requester is an encryption person.
Further, five segments of user brain activities are repeatedly acquired in the step (1.3), and the specific algorithm is as follows:
(a) Intercepting the 1 st section of the first section of the fluctuating graph, then comparing the first section of the fluctuating graph with other four sections of the fluctuating graph, and recording the position and the similarity percentage if the first section of the fluctuating graph is overlapped with the other four sections of the fluctuating graph;
(b) Comparing the 2 nd small section in the first section of fluctuation diagram with other four sections in sequence, and recording the position and the similarity percentage if the 2 nd small section is overlapped;
(c) Comparing all small sections in the first section of fluctuation graph with other four sections in sequence, and recording the position and the similarity percentage if the small sections are overlapped;
(d) Cutting the 1 st section of the second section of the oscillogram, then comparing in other four sections, and recording the position and the similarity percentage if the sections are overlapped;
(e) Comparing all small segments of the second segment fluctuation diagram with other four segments, and recording the position and the similarity percentage if the small segments are overlapped;
(f) Cutting the remaining three sections of fluctuation graphs into small sections in sequence, comparing the small sections with the other four sections in sequence, and recording the position and the similarity percentage if the three sections are overlapped;
(g) Collecting all comparison results, grouping and summarizing according to small sections, summarizing the total matching number of each small section, combining the small sections which are continuously matched into a large section, extracting the maximum common contract area of the large section in each original section, and calculating the average similarity of the common contract area matching.
Further, a specific value in the step (1.3) is 97%.
Further, the specific information in steps (1.2) and (1.3) is a sentence, a picture or a video.
The invention also discloses an encryption system based on the brain consciousness, which comprises a consciousness encryption module and a consciousness decryption module, wherein the consciousness encryption module further comprises a consciousness data acquisition submodule A, a consciousness data acquisition submodule B, a consciousness data analysis submodule and a secret key generation submodule;
the consciousness data acquisition submodule A has the working process that: the consciousness collector is worn on the head of a user, the user is prompted to pay attention to a specified position, the consciousness collector collects current generated by electromagnetic wave conversion due to brain activity and amplifies the current, the consciousness collector further records current fluctuation as an analog signal, the analog signal is converted into a digital signal, and the digital signal is stored in consciousness collector storage equipment;
the workflow of the consciousness data acquisition submodule B is as follows: the consciousness analyzer extracts the stored data from the consciousness collector storage device, restores the data into a fluctuation graph, calculates the amplitude and the frequency of the fluctuation graph to judge whether the amplitude is smaller than a specified value or not and whether the frequency is higher than the specified value or not, continues to collect brain activity data of a user if one of the amplitude and the frequency is not satisfied, starts to work if both of the amplitude and the frequency are satisfied, drives a screen to display specific information, generates brain activity after the user sees the specific information, collects current converted by electromagnetic waves generated by the brain activity and amplifies the current, and further records the current fluctuation as an analog signal, converts the analog signal into a digital signal and stores the digital signal into the consciousness collector storage device;
the work flow of the consciousness data analysis submodule is as follows: driving a screen to display another specific information, continuously and repeatedly reading the brain activity of the user through an awareness collector, respectively converting the repeatedly read brain activity data into oscillograms, calculating the similarity of the oscillograms through a specific algorithm, continuously and repeatedly displaying the specific information and reading the brain activity of the user if the similarity is smaller than the specific value until the similarity of a large segment is higher than the specific value, carrying out normalization processing on the region, and generating the region into a fixed characteristic value by adopting a data summarization algorithm;
the work flow of the key generation submodule is as follows: repeating the work flow of the consciousness acquisition and analysis submodule, acquiring the characteristic value again, restarting the consciousness acquisition and analysis step again if the characteristic values acquired twice are different to generate a new characteristic value until the characteristic values are the same, generating a key pair by using the characteristic value as a key seed, then reserving the key, externally publishing a convention, encrypting data by using a password, externally publishing the encrypted data, and completing consciousness encryption;
the working process of the consciousness decryption module comprises the following steps: the decryption requester uses an external convention to provide a verification request, the decryption requester repeatedly realizes all work flows in the encryption module and obtains a specific private key, the private key is used for decrypting data, if decryption fails, the description is not matched, namely the decryption requester is not an encryption person, and if decryption succeeds, the description is matched, namely the decryption requester is the encryption person.
Further, five segments of brain activities of the user are repeatedly collected in the consciousness data analysis submodule, and the specific algorithm is as follows:
(a) Intercepting the 1 st section of the first section of the fluctuation graph, then comparing in other four sections, and recording the position and the similarity percentage if the first section of the fluctuation graph is overlapped;
(b) Comparing the 2 nd subsection in the first section of fluctuation graph with other four subsections in sequence, and recording the position and the similarity percentage if the subsections are overlapped;
(c) Comparing all small sections in the first section of fluctuation graph with other four sections in sequence, and recording the position and the similarity percentage if the small sections are overlapped;
(d) Intercepting the 1 st section of the second section of the fluctuating graph, then comparing in other four sections, and recording the position and the similarity percentage if the sections are overlapped;
(e) Comparing all the small sections of the second section of the fluctuation chart with other four sections, and recording the position and the similarity percentage if the small sections are overlapped;
(f) Cutting the remaining three waved images into small segments in sequence, comparing the small segments with the other four segments in sequence, and recording the position and similarity percentage if the small segments are overlapped;
(g) Collecting all comparison results, grouping and summarizing according to small sections, summarizing the total matching number of each small section, combining the small sections which are continuously matched into a large section, extracting the maximum common contract area of the large section in each original section, and calculating the average similarity of the common contract area matching.
Further, the characteristic value in the consciousness data analysis submodule is 97%.
Further, the specific information in the consciousness data acquisition submodule B and the consciousness data analysis submodule is a sentence, a picture or a video.
The invention has the advantages of realizing the encryption protection function that only the principal can decrypt the encryption, and solving a series of problems of encryption decryption, password theft, password loss and the like.
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FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a block diagram of the system of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples. It should be noted that the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in fig. 1, the present embodiment discloses a brain consciousness-based encryption method, and the solution is to use brain consciousness activities as encryption features, because the brain consciousness activities not only satisfy universality, uniqueness, stability, but also satisfy forgery resistance, concealment, and activity detection resistance, and whether to be forced to use passwords to a certain extent can also be distinguished, and the brain activities generate currents and form electromagnetic waves, which can be captured by coils or electrode plates, that is, simultaneously satisfy collectability, so that the brain consciousness activities as encryption features will satisfy all the characteristic requirements in the above problems, and will also become the safest encryption scheme so far.
The consciousness acquiring device in the embodiment is mainly used for acquiring brain activity signals. The consciousness collector mainly comprises four core modules, a signal capturing module, a signal amplifying module, a signal collecting module and a signal storing module. In the signal capturing module, the signal capturing module is mainly used for converting brain activity information into current pulsation information, the core principle is that a coil which is wound into a certain number of turns by using a very fine lead is attached to the scalp, and brain electromagnetic waves form current when passing through the coil, so that the effect of converting brain signals into electric signals is achieved; in the signal amplification module, the collected weak current pulse converted from brain signals is mainly enhanced so as to reach the standard of collection and analysis, and the core principle is that a triode or an equivalent amplification device is connected with external current to amplify the current pulse in the same proportion; in the signal collection module, the main use is to record the amplified current pulse as the analog signal, and further convert the analog signal into the binary signal, the core principle of the conversion is to define a pulse maximum value according to the pulse intensity, the maximum value covers all the pulse current peak values, and the fluctuation peak value in the analog signal is converted into the quota number according to the fluctuation proportion, and the number is converted into the binary signal for recording; the signal storage module is mainly used for storing the pulse information converted into the binary system to the equipment such as a buffer area, a flash memory, a magnetic disk and the like to prepare for subsequent analysis.
The activity inducer in this embodiment is mainly used to induce the brain to perform a specific activity. When the brain enters a focus state, a user is required to perform a period of brain interaction according to an application scenario, and the interaction may involve brain activity induced by vision, brain activity induced by motion, and brain activity induced by touch or external reflection, so the activity inducer mainly comprises three core modules, namely a vision induction module, a motion induction module and an event induction module. In the visual induction module, a specific picture, which may be a photo, a video or a character, is displayed for a user mainly according to the application scene requirement, so that the brain activity of the user is aroused, and the corresponding brain activity data is recorded; in the motion induction module, the motion induction module is mainly used for brain activities related to motion under a specific scene, such as raising hands, turning left a steering wheel, running and jumping, and the like, the motion induction is not only suitable for normal people with healthy limbs, but also suitable for amputees, such as amputees, who can imagine walking motion, and thus the motion of the brain can be stimulated, and the motion induction module is very practical for an encryption scene that walking is set to be an encryption characteristic after amputees, and can be used as an unlocking password for driving an artificial limb; in the event induction module, the event induction module is mainly used for recording brain activities when a user interacts with a scene, for example, the user enters a warm room, hears a good-hearing song, is stepped on the toes by a person, is injured by the body and the like, and the external events can also excite the brain activities of the user, so that the characteristics of the brain activities are very useful for unlocking the scene or encrypting private information according to the scene in a live-action game or a VR game, and the effect that the user can decrypt the private information only when the user returns to the current scene again can be achieved.
The consciousness key-sealing device in the embodiment is mainly used for extracting the characteristics of brain activity and generating a key. In the awareness key device, the awareness key device is mainly used for extracting the read brain activity features, the process may need to be performed for many times until stable features can be acquired, and because people who do not have special training often have great derivation ideas along with brain activities, the awareness key device comprises three main core modules, a feature extraction module, a feature convention analysis module and a feature key module. The characteristic extraction module is mainly used for carrying out normalization processing on the acquired fluctuation data after removing noise; in the characteristic common divisor analysis module, the method is mainly used for extracting common divisors from multiple sections of normalized fluctuation data, extracting a region with the highest overlapping degree in multiple sections of fluctuation, cutting out the maximum complete overlapping region of the region, generating the fluctuation data of the region into a characteristic value by adopting a data summarization algorithm, requiring a user to perform the same brain recognition interaction again, comparing the extraction characteristics of the fluctuation data which are active again, if the extraction characteristics are not consistent, extracting the data acquisition together as the basic data of common divisor extraction until the common divisor which can be matched repeatedly is extracted, and if the common divisor cannot be obtained in the common divisor calculation process due to the continuous acquisition of the data, resetting the data from the beginning and acquiring the data; in the feature key module, the feature key module is mainly used for generating a fixed key pair by taking the extracted features as the seeds of the key pair, reserving a private key and publishing a convention to the outside.
The embodiment comprises the following specific steps:
(1) The consciousness encryption specifically comprises the following sub-steps:
(1.1) consciousness data acquisition A: the consciousness collector is worn on the head of a user, the user is prompted to pay attention to a specified position, the consciousness collector collects current generated by electromagnetic wave conversion due to brain activity and amplifies the current, the consciousness collector further records current fluctuation as an analog signal, the analog signal is converted into a digital signal, and the digital signal is stored in consciousness collector storage equipment;
(1.2) consciousness data acquisition B: the consciousness analyzer extracts the data stored in the step (1.1) from the consciousness collector storage device, restores the data into a fluctuation graph, calculates the amplitude and the frequency of the fluctuation graph to judge whether the amplitude is smaller than a specified value or not and whether the frequency is higher than the specified value or not, if one of the amplitude and the frequency is not satisfied, continuously collects brain activity data of a user, if both of the amplitude and the frequency are satisfied, the activity inducer starts to work to drive a screen to display specific information, the specific information is a word, a picture or a sentence video, the user generates brain activity after seeing the specific information, the consciousness collector collects current converted by electromagnetic waves generated by the brain activity and amplifies the current, the consciousness collector further records the current fluctuation as an analog signal, converts the analog signal into a digital signal and stores the digital signal into the consciousness collector storage device;
(1.3) consciousness data analysis: driving a screen to display another piece of specific information, wherein the specific information is a sentence, a picture or a video, continuously and repeatedly reading five segments of brain activities of the user through a consciousness acquisition device, respectively converting the repeatedly read brain activity data into oscillograms, and then executing the following algorithm:
(a) Intercepting the 1 st section of the first section of the fluctuation graph, then comparing in other four sections, and recording the position and the similarity percentage if the first section of the fluctuation graph is overlapped;
(b) Comparing the 2 nd small section in the first section of fluctuation diagram with other four sections in sequence, and recording the position and the similarity percentage if the 2 nd small section is overlapped;
(c) Comparing all the small sections in the first section of fluctuation diagram with other four sections in turn, and recording the position and the similarity percentage if the small sections are overlapped;
(d) Intercepting the 1 st section of the second section of the fluctuating graph, then comparing in other four sections, and recording the position and the similarity percentage if the sections are overlapped;
(e) Comparing all small segments of the second segment fluctuation diagram with other four segments, and recording the position and the similarity percentage if the small segments are overlapped;
(f) Cutting the remaining three sections of fluctuation graphs into small sections in sequence, comparing the small sections with the other four sections in sequence, and recording the position and the similarity percentage if the three sections are overlapped;
(g) Collecting all comparison results, grouping and summarizing according to small sections, summarizing the total matching number of each small section, combining the small sections which are continuously matched into a large section, extracting the maximum common contract area of the large section in each original section, and calculating the average similarity of the common contract area matching.
If the similarity is less than 90%, continuously and repeatedly displaying the specific information and reading the brain activity of the user until the similarity of a large segment is higher than 90%, carrying out normalization processing on the region, and generating the region into a fixed characteristic value by adopting a data summarization algorithm;
(1.4) Key Generation: repeating the consciousness acquisition and analysis steps, acquiring the characteristic values again, restarting the consciousness acquisition and analysis steps again if the characteristic values acquired twice are different, generating new characteristic values until the characteristic values are the same, using the characteristic values as key seeds to generate key pairs, then keeping the keys, publishing the convention outwards, encrypting data by using the passwords, publishing the encrypted data outwards, and completing consciousness encryption;
(2) Consciousness decryption: and (2) the decryption requester makes a verification request by using an external convention, the decryption requester repeats all the processes in the step (1) and obtains a specific private key, the private key is used for decrypting data, if the decryption fails, the decryption requester is not matched, namely the decryption requester is not an encryption person, and if the decryption succeeds, the decryption requester is matched, namely the decryption requester is the encryption person.
As shown in fig. 2, the present embodiment discloses a brain consciousness-based encryption system, which includes a consciousness encryption module and a consciousness decryption module, wherein the consciousness encryption module further includes a consciousness data acquisition sub-module a, a consciousness data acquisition sub-module B, a consciousness data analysis sub-module and a key generation sub-module;
the consciousness data acquisition submodule A has the working process that: the consciousness collector is worn on the head of a user, the user is prompted to pay attention to a specified position, the consciousness collector collects current generated by electromagnetic wave conversion due to brain activity and amplifies the current, the consciousness collector further records current fluctuation as an analog signal, the analog signal is converted into a digital signal, and the digital signal is stored in consciousness collector storage equipment;
the workflow of the consciousness data acquisition submodule B is as follows: the consciousness analyzer extracts the stored data from the consciousness collector storage device, restores the data into a fluctuation graph, calculates the amplitude and the frequency of the fluctuation graph to judge whether the amplitude is smaller than a specified value or not and whether the frequency is higher than the specified value or not, continues to collect brain activity data of a user if one of the amplitude and the frequency is not satisfied, starts to work if both of the amplitude and the frequency are satisfied, drives a screen to display specific information, generates brain activity after the user sees the specific information, collects current converted by electromagnetic waves generated by the brain activity and amplifies the current, and further records the current fluctuation as an analog signal, converts the analog signal into a digital signal and stores the digital signal into the consciousness collector storage device;
the workflow of the consciousness data analysis submodule is as follows: driving a screen to display another specific information, wherein the specific information is a sentence, a picture or a video, continuously and repeatedly reading five sections of brain activities of the user through a consciousness acquisition device, respectively converting the repeatedly read brain activity data into oscillograms, and then executing the following algorithm:
(a) Intercepting the 1 st section of the first section of the fluctuating graph, then comparing the first section of the fluctuating graph with other four sections of the fluctuating graph, and recording the position and the similarity percentage if the first section of the fluctuating graph is overlapped with the other four sections of the fluctuating graph;
(b) Comparing the 2 nd small section in the first section of fluctuation diagram with other four sections in sequence, and recording the position and the similarity percentage if the 2 nd small section is overlapped;
(c) Comparing all small sections in the first section of fluctuation graph with other four sections in sequence, and recording the position and the similarity percentage if the small sections are overlapped;
(d) Cutting the 1 st section of the second section of the oscillogram, then comparing in other four sections, and recording the position and the similarity percentage if the sections are overlapped;
(e) Comparing all the small sections of the second section of the fluctuation chart with other four sections, and recording the position and the similarity percentage if the small sections are overlapped;
(f) Cutting the remaining three sections of fluctuation graphs into small sections in sequence, comparing the small sections with the other four sections in sequence, and recording the position and the similarity percentage if the three sections are overlapped;
(g) Collecting all comparison results, grouping and summarizing according to small sections, summarizing the total matching number of each small section, combining the small sections which are continuously matched into a large section, extracting the maximum common contract area of the large section in each original section, and calculating the average similarity of the common contract area matching.
If the similarity is less than 90%, continuously and repeatedly displaying the specific information and reading the brain activity of the user until the similarity of a large segment is higher than 90%, carrying out normalization processing on the region, and generating the region into a fixed characteristic value by adopting a data summarization algorithm;
driving a screen to display another specific information, wherein the specific information is a sentence, a picture or a video, continuously and repeatedly reading the brain activity of the user through a consciousness collector, respectively converting the repeatedly read brain activity data into oscillograms, calculating the similarity of the oscillograms through a specific algorithm, continuously and repeatedly displaying the specific information and reading the brain activity of the user if the similarity is smaller than the specific value until the similarity of a large segment is higher than the specific value, normalizing the region, and generating the region into a fixed characteristic value through a data summarization algorithm;
the work flow of the key generation submodule is as follows: repeating the work flow of the consciousness acquisition and analysis submodule, acquiring the characteristic value again, restarting the consciousness acquisition and analysis step again if the characteristic values acquired twice are different to generate a new characteristic value until the characteristic values are the same, generating a key pair by using the characteristic value as a key seed, then reserving the key, externally publishing a convention, encrypting data by using a password, externally publishing the encrypted data, and completing consciousness encryption;
the workflow of the consciousness decryption module is as follows: the decryption requester uses an external convention to provide a verification request, the decryption requester repeatedly realizes all work flows in the encryption module and obtains a specific private key, the private key is used for decrypting data, if decryption fails, the description is not matched, namely the decryption requester is not an encryption person, and if decryption succeeds, the description is matched, namely the decryption requester is the encryption person.
The above-mentioned embodiments are only preferred embodiments of the present invention, and do not limit the technical scope of the present invention, so that the changes and modifications made by the claims and the specification of the present invention should fall within the scope of the present invention.
Claims (6)
1. A brain-awareness-based encryption method, characterized by comprising the steps of:
(1) The consciousness encryption specifically comprises the following sub-steps:
(1.1) consciousness data acquisition A: the consciousness collector is worn on the head of a user, the user is prompted to pay attention to a specified position, the consciousness collector collects current generated by electromagnetic wave conversion due to brain activity and amplifies the current, the consciousness collector further records current fluctuation as an analog signal, the analog signal is converted into a digital signal, and the digital signal is stored in consciousness collector storage equipment;
(1.2) consciousness data acquisition B: the consciousness analyzer extracts the data stored in the step (1.1) from the consciousness collector storage device, restores the data into a fluctuation graph, calculates the amplitude and the wave frequency of the fluctuation graph to judge whether the amplitude is smaller than a specified value or not and whether the wave frequency is higher than the specified value or not, if one of the amplitude and the wave frequency is not satisfied, continuously collects brain activity data of a user, if both of the amplitude and the wave frequency are satisfied, the activity inducer starts to work, a screen is driven to display specific information, the user generates brain activity after seeing the specific information, the consciousness collector collects current generated by the brain activity and converted by electromagnetic waves and amplifies the current, the consciousness collector further records the current fluctuation as an analog signal, converts the analog signal into a digital signal and stores the digital signal into the consciousness collector storage device;
(1.3) consciousness data analysis: driving a screen to display another specific information, continuously and repeatedly reading the brain activity of the user through an consciousness collector, respectively converting the brain activity data which are repeatedly read into oscillograms, calculating the similarity of the oscillograms through a specific algorithm, continuously and repeatedly displaying the specific information and reading the brain activity of the user if the similarity is smaller than a specific value until a large segment of the similarity is higher than the specific value, normalizing the region with the similarity higher than the specific value, and generating a fixed characteristic value by adopting a data summarization algorithm for the region with the similarity higher than the specific value;
(1.4) Key Generation: repeating the consciousness acquisition and analysis steps, acquiring the characteristic values again, restarting the consciousness acquisition and analysis steps again if the characteristic values acquired twice are different, generating new characteristic values until the characteristic values are the same, using the characteristic values as key seeds to generate key pairs, then reserving the keys, externally publishing the public keys, encrypting data by using the passwords, externally publishing the encrypted data pairs, and completing consciousness encryption;
(2) Consciousness decryption: a decryption requester uses an external public key to make a verification request, the decryption requester repeats all the processes in the step (1) and obtains a specific private key, the private key is used for decrypting data, if decryption fails, the decryption requester is not matched, namely the decryption requester is not an encryption person, and if decryption succeeds, the decryption requester is matched, namely the decryption requester is an encryption person;
in the step (1.3), five segments of brain activities of the user are repeatedly collected, and the specific algorithm is as follows:
(a) Intercepting the 1 st section of the first section of the fluctuation graph, then comparing in other four sections, and recording the position and the similarity percentage if the first section of the fluctuation graph is overlapped;
(b) Comparing the 2 nd small section in the first section of fluctuation diagram with other four sections in sequence, and recording the position and the similarity percentage if the 2 nd small section is overlapped;
(c) Comparing all the small sections in the first section of fluctuation diagram with other four sections in turn, and recording the position and the similarity percentage if the small sections are overlapped;
(d) Cutting the 1 st section of the second section of the oscillogram, then comparing in other four sections, and recording the position and the similarity percentage if the sections are overlapped;
(e) Comparing all small segments of the second segment fluctuation diagram with other four segments, and recording the position and the similarity percentage if the small segments are overlapped;
(f) Cutting the remaining three sections of fluctuation graphs into small sections in sequence, comparing the small sections with the other four sections in sequence, and recording the position and the similarity percentage if the three sections are overlapped;
(g) Collecting all comparison results, grouping and summarizing according to small sections, summarizing the total matching number of each small section, combining the small sections which are continuously matched into a large section, extracting the maximum common contract area of the large section in each original section, and calculating the average similarity of the common contract area matching.
2. A brain-consciousness based encryption method as claimed in claim 1, wherein the specific value in the step (1.3) is 97%.
3. A brain-awareness-based encryption method according to claim 1, wherein said specific information in said steps (1.2) and (1.3) is a sentence, a picture or a video.
4. An encryption system based on brain consciousness comprises a consciousness encryption module and a consciousness decryption module, wherein the consciousness encryption module further comprises a consciousness data acquisition submodule A, a consciousness data acquisition submodule B, a consciousness data analysis submodule and a secret key generation submodule;
the consciousness data acquisition submodule A has the working process that: the consciousness collector is worn on the head of a user, the user is prompted to pay attention to a specified position, the consciousness collector collects current generated by electromagnetic wave conversion due to brain activity and amplifies the current, the consciousness collector further records current fluctuation as an analog signal, the analog signal is converted into a digital signal, and the digital signal is stored in consciousness collector storage equipment;
the consciousness data acquisition submodule B comprises the following working procedures: the consciousness analyzer extracts the stored data from the consciousness collector storage device, restores the data into a fluctuation graph, calculates the amplitude and the frequency of the fluctuation graph to judge whether the amplitude is smaller than a specified value or not and whether the frequency is higher than the specified value or not, continues to collect brain activity data of a user if one of the amplitude and the frequency is not satisfied, starts to work if both of the amplitude and the frequency are satisfied, drives a screen to display specific information, generates brain activity after the user sees the specific information, collects current converted by electromagnetic waves generated by the brain activity and amplifies the current, and further records the current fluctuation as an analog signal, converts the analog signal into a digital signal and stores the digital signal into the consciousness collector storage device;
the workflow of the consciousness data analysis submodule is as follows: driving a screen to display another specific information, continuously and repeatedly reading brain activities of a user through an awareness collector, respectively converting the repeatedly read brain activity data into a fluctuation graph, calculating the similarity of the fluctuation graph through a specific algorithm, continuously and repeatedly displaying the specific information and reading the brain activities of the user if the similarity is smaller than a specific value until a large segment of similarity is higher than the specific value, normalizing a region with the similarity higher than the specific value, and generating a fixed characteristic value in a region with the similarity higher than the specific value through a data summarization algorithm;
the work flow of the key generation submodule is as follows: repeating the work flow of the consciousness acquisition and analysis submodule, acquiring the characteristic value again, restarting the consciousness acquisition and analysis step again if the characteristic values acquired twice are different to generate a new characteristic value until the characteristic values are the same, generating a key pair by using the characteristic value as a key seed, then reserving the key, externally publishing a public key, encrypting data by using a password, externally publishing the encrypted data, and finishing consciousness encryption;
the workflow of the consciousness decryption module is as follows: a decryption requester uses an external public key to make a verification request, the decryption requester repeatedly realizes all work flows in the encryption module and obtains a specific private key, the private key is used for decrypting data, if decryption fails, the description is not matched, namely the decryption requester is not an encryption person, and if decryption succeeds, the description is matched, namely the decryption requester is an encryption person;
five segments of brain activities of the user are repeatedly collected in the consciousness data analysis submodule, and the specific algorithm is as follows:
(a) Intercepting the 1 st section of the first section of the fluctuation graph, then comparing in other four sections, and recording the position and the similarity percentage if the first section of the fluctuation graph is overlapped;
(b) Comparing the 2 nd small section in the first section of fluctuation diagram with other four sections in sequence, and recording the position and the similarity percentage if the 2 nd small section is overlapped;
(c) Comparing all small sections in the first section of fluctuation graph with other four sections in sequence, and recording the position and the similarity percentage if the small sections are overlapped;
(d) Cutting the 1 st section of the second section of the oscillogram, then comparing in other four sections, and recording the position and the similarity percentage if the sections are overlapped;
(e) Comparing all small segments of the second segment fluctuation diagram with other four segments, and recording the position and the similarity percentage if the small segments are overlapped;
(f) Cutting the remaining three sections of fluctuation graphs into small sections in sequence, comparing the small sections with the other four sections in sequence, and recording the position and the similarity percentage if the three sections are overlapped;
(g) Collecting all comparison results, grouping and summarizing according to small sections, summarizing the total matching number of each small section, combining the small sections which are continuously matched into a large section, extracting the maximum common contract area of the large section in each original section, and calculating the average similarity of the common contract area matching.
5. A brain-consciousness based encryption system according to claim 4 wherein said consciousness data analysis submodule wherein said characteristic value is 97%.
6. The brain-awareness-based encryption system according to claim 4, wherein said specific information in said awareness data collecting sub-module B and awareness data analyzing sub-module is a sentence, a picture or a video.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009010001A1 (en) * | 2007-07-19 | 2009-01-22 | Zhiping Meng | A method and system for encryption and personal idendification based on brain wave |
| US9672760B1 (en) * | 2016-01-06 | 2017-06-06 | International Business Machines Corporation | Personalized EEG-based encryptor |
| CN107171791A (en) * | 2017-05-24 | 2017-09-15 | 舒翔 | A kind of data encryption/decryption method and encrypting and deciphering system based on biological characteristic |
| CA2992333A1 (en) * | 2018-01-19 | 2018-05-07 | Nymi Inc. | User access authorization system and method, and physiological user sensor and authentication device therefor |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009010001A1 (en) * | 2007-07-19 | 2009-01-22 | Zhiping Meng | A method and system for encryption and personal idendification based on brain wave |
| US9672760B1 (en) * | 2016-01-06 | 2017-06-06 | International Business Machines Corporation | Personalized EEG-based encryptor |
| CN107171791A (en) * | 2017-05-24 | 2017-09-15 | 舒翔 | A kind of data encryption/decryption method and encrypting and deciphering system based on biological characteristic |
| CA2992333A1 (en) * | 2018-01-19 | 2018-05-07 | Nymi Inc. | User access authorization system and method, and physiological user sensor and authentication device therefor |
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