A medical health data privacy security management method and system based on MLWE

Abstract

The present invention relates to the technical field of data encryption, and discloses a medical health data privacy security management method and system based on MLWE. The method initializes the installed biomedical sensor through a key generation server, and collects the patient's medical health data in real time through the biomedical sensor; at the same time, the real-time collected patient medical health data is processed by data verification and filtering, and the processed patient medical health data is transmitted to the database through the low expansion rate encryption algorithm of MLWE; at the same time, the processed patient medical health data in the database is transmitted to the medical cloud server by using the AES encryption algorithm, and decrypted and accessed through the user registration method, so as to realize the security management of medical health data privacy, and improve the security and accuracy of medical health data privacy security management.

Description

Technical Field

[0001] The present invention relates to the field of data encryption technology, and in particular to a medical health data privacy security management method and system based on MLWE. Background Art

[0002] With the continuous development of hospital informatization, the digital medical model has become more and more perfect. However, since the medical and health data in the digital medical model needs to be publicly transmitted, it is easy to cause data leakage and there are certain data security risks.

[0003] The existing Chinese patent application CN117874789A identifies and partitions private data, associates the identified and partitioned data with encryption tasks, and uses dynamic incremental encryption to only encrypt and transmit the changed data parts, and returns the encrypted data and applies it to related data blocks. However, since only one encryption method is considered, the encryption form is single and has certain limitations. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In view of the shortcomings of the prior art, the present invention provides a medical health data privacy security management method and system based on MLWE, which has the advantages of security, efficiency, accuracy, etc., and solves the problem of data security risks in the data transmission process.

[0006] (II) Technical solution

[0007] In order to solve the technical problem of data security risks in the above data transmission process, the present invention provides the following technical solutions:

[0008] The present invention discloses a medical health data privacy security management method and system based on MLWE, which specifically includes the following steps:

[0009] S1. Initialize the installed biomedical sensors through the key generation server, and collect patient medical health data in real time through the installed biomedical sensors;

[0010] S2. Preprocessing the medical and health data of patients collected in real time to obtain preprocessed medical and health data of patients;

[0011] S3, constructing a low expansion rate encryption algorithm of MLWE, and transmitting the obtained pre-processed patient medical health data to the database based on the constructed encryption algorithm;

[0012] S4, transmitting the pre-processed patient medical health data in the database to the medical cloud server by using the AES encryption algorithm;

[0013] S5. Based on the user registration method, the pre-processed patient medical health data in the accessed medical cloud server is decrypted by performing the inverse operation of the encryption algorithm corresponding to each step of the AES encryption algorithm to obtain the corresponding pre-processed patient medical health data.

[0014] The present invention initializes the installed biomedical sensors through a key generation server, and collects the medical health data of patients in real time through the biomedical sensors; at the same time, the medical health data of patients collected in real time is processed by data verification and filtering, and the processed medical health data of patients is transmitted to the database through the low expansion rate encryption algorithm of MLWE; at the same time, the processed medical health data of patients in the database is transmitted to the medical cloud server by using the AES encryption algorithm, and decrypted and accessed through the user registration method, thereby realizing the security management of the privacy of medical health data and improving the security and accuracy of the privacy security management of medical health data.

[0015] Preferably, the initialization of the installed biomedical sensor by the key generation server comprises the following steps:

[0016] S11, numbering the installed biomedical sensors, and using the numbers of the biomedical sensors as biomedical sensor IDs;

[0017] S12, setting the parameters required in the key generation process;

[0018] The key generation server randomly divides a prime field; sets G1 to be the additive group constructed by all integers in the prime field, and Z to be the set of all prime numbers in the prime field.

[0019] The present invention completes the initialization of installed biomedical sensors by numbering the installed biomedical sensors and setting the parameters required in the key generation process, thereby improving the efficiency of privacy and security management of medical health data.

[0020] Preferably, the preprocessing of the patient medical health data collected in real time comprises the following steps:

[0021] S21, data analysis;

[0022] Classify patient medical health data collected in real time by biomedical sensors according to data types;

[0023] The classification includes: time-based data, continuous data, selective data, and descriptive data;

[0024] Time-based data refer to the patient visit time, physical examination time, and examination time of vital signs;

[0025] Continuous data refers to variables that have meaningful values ​​within a certain range, such as height, weight, and body temperature;

[0026] Selection data refers to variables with options, such as gender options and patient adverse status options;

[0027] Descriptive data refers to variables that require specific descriptions, such as the name of the patient's adverse event or the name of the medication;

[0028] S22, data editing check;

[0029] Edit and check the patient medical and health data collected in real time by the biomedical sensor through the set patient medical and health data threshold;

[0030] The editing check pointer checks the value of a single data point;

[0031] For example, in a clinical trial project, the patient's body temperature data should be greater than 30℃ and less than 45℃. When the biomedical sensor detects that a patient's body temperature is less than 30℃ or greater than 45℃, it will automatically prompt that the data is wrong and the threshold range of the value here will be prompted;

[0032] S23, data filtering;

[0033] The erroneous data prompted in the data editing check are deleted and filtered to obtain the filtered patient medical health data, and the filtered patient medical health data is set as the pre-processed patient medical health data.

[0034] The present invention completes the preprocessing of the patient medical health data collected in real time by performing data analysis, data editing inspection and data filtering on the patient medical health data collected in real time, thereby ensuring the accuracy of the privacy and security management of the medical health data.

[0035] Preferably, constructing a low expansion rate encryption algorithm of MLWE, and transmitting the pre-processed patient medical health data to a database based on the constructed encryption algorithm comprises the following steps:

[0036] S31, setting the low expansion rate encryption algorithm parameters of MLWE;

[0037] S32. Determine the homomorphic encryption scheme of the MLWE low dilation rate encryption algorithm based on the set MLWE low dilation rate encryption algorithm parameters.

[0038] Preferably, the setting of the low expansion rate encryption algorithm parameters of MLWE includes:

[0039] The preprocessed patient medical health data is set to a, n=n(a), where n represents the dimension of the vector, n(a) represents the dimension of the preprocessed patient medical health data λ vector, q=q(a), where q represents the modulus of the vector, q(a) represents the modulus of the preprocessed patient medical health data a vector.

[0040] Preferably, the determining of the MLWE low expansion rate encryption algorithm homomorphic encryption scheme based on setting the MLWE low expansion rate encryption algorithm parameters comprises the following steps:

[0041] S321, key generation;

[0042] Generate public and private keys based on key generation calculations;

[0043] Set up two one-way cryptographic hash functions;

[0044] H1: {0,1} l →G1;

[0045] H2: {0,1} * →{0,1} m+l+t ×G1;

[0046] Among them, m represents the message length, l represents the message identifier, t represents the timestamp, {0,1} * Represents a string of any length composed of 0 and 1;

[0047] Randomly select s∈Z, set the system master key to s, and calculate P pub =sP as the system public key, where P is the generator of G1 and Z is the set of all prime numbers in the prime domain;

[0048] Randomly select y∈Z, set the sensor master key to y, and calculate Y=yP as the sensor public key;

[0049] Randomly select k∈Z and calculate d=k+sQ as the private key, where Q=H1(ID,Y);

[0050] Set safety parameter λ= pub ,G1,P>;

[0051] The security parameter λ is a system shared parameter;

[0052] S322, encrypting the pre-processed patient medical health data to be sent;

[0053] Select a pre-processed patient medical health data to be sent i , by generating a public key for encryption calculation Obtain encrypted and pre-processed patient medical health data;​

[0054] Among them, a i Indicates the i-th data sent, c i Indicates that the i-th data is encrypted by a hash function, w i represents the secret value randomly generated by the i-th data, H2 represents the second one-way encryption hash function, and Y represents the sensor public key;

[0055] S323, performing homomorphic calculation on the encrypted pre-processed patient medical health data;

[0056] Input the encrypted pre-processed patient medical health data, and obtain the pre-processed patient medical health data after homomorphic calculation through homomorphic calculation;

[0057] The homomorphic calculation formula is as follows:

[0058] [a ij ]·[c i ]=[a ij ]·[s i ]+[d i ]where i = 1, 2, ..., n; j = 1, 2, ..., q;

[0059] Among them, a ij represents the preprocessed patient medical health data in the i-th row and j-th column, c i represents the encrypted pre-processed patient health data of the ith row, s i represents the system master key of the preprocessed patient health data in the i-th row, d i represents the private key of the preprocessed patient health data in row i;

[0060] S324, decryption;

[0061] Input the pre-processed patient medical health data after homomorphic calculation, obtain the pre-processed patient medical health data through decryption calculation and save it in the database;

[0062]

[0063] The present invention uses the MLWE low expansion rate encryption algorithm to ensure the encrypted transmission of patient medical health data through key generation, data encryption, homomorphic computing and decryption, thereby improving the security of medical health data privacy security management.

[0064] Preferably, the method of transmitting the pre-processed patient medical health data in the database to the medical cloud server by using the AES encryption algorithm comprises the following steps:

[0065] The process of transmitting the pre-processed patient medical health data in the database to the medical cloud server is set as a data interaction process;

[0066] S41, segmenting and completing the plaintext in the pre-processed patient medical health data that needs to be encrypted during the data interaction process, and dividing the plaintext in the pre-processed patient medical health data that needs to be encrypted into plaintext data blocks equal to the key length;

[0067] The segmentation and completion of the plaintext in the pre-processed patient medical health data that needs to be encrypted during the data interaction process includes the following steps:

[0068] S411, setting the length of the key based on the data interaction process;

[0069] Set the data exchange process to use bytes as the unit;

[0070] Since 1 byte = 8 bits; 1 bit represents a digit in the data exchange process;

[0071] Then the length of the key should be set to an integer multiple of bytes;

[0072] S412, segmenting the plaintext in the preprocessed patient medical health data based on the set key length;

[0073] segmenting the plaintext in the preprocessed patient medical health data based on the number of bytes contained in the plaintext in the preprocessed patient medical health data and the key length;

[0074]

[0075] Among them, E represents the number of plaintext data blocks after segmentation, α represents the number of bytes contained in the plaintext of the patient's medical and health data after preprocessing, and β represents the key length;

[0076] S413, padding the plaintext in the preprocessed patient medical health data that is insufficient in length, based on the byte difference between the plaintext in the preprocessed patient medical health data and the key length, padding the end of the plaintext in the preprocessed patient medical health data that is insufficient in length;

[0077] Set the data in the padding bytes to all 0;

[0078] S42, encrypting each plaintext data block after segmentation and padding using the AES encryption algorithm.

[0079] Preferably, encrypting each plaintext data block after segmentation and padding by using an AES encryption algorithm comprises the following steps:

[0080] S421, generating a vector matrix and performing XOR with the plaintext data block;

[0081] S422, encrypting the plaintext data block using the AES encryption algorithm;

[0082] S4221, setting, the key addition round to perform multiple rounds of addition of the plaintext data block and the key and outputting the result;

[0083] S4222, set a byte substitution layer;

[0084] After multiple rounds of addition, the plaintext data blocks are byte-converted in the byte substitution layer and output;

[0085] S4223, setting the byte step of the row displacement, shifting each byte in the plaintext data after the byte substitution in sequence by the byte step of the row displacement;

[0086] S4224, setting the byte step of the column shift, shifting each byte in the plaintext data after the row shift in sequence to set the byte step of the column shift;

[0087] S4225. Set the number of cycles of the AES encryption algorithm, and execute steps S4221 to S4224 based on the set number of cycles.

[0088] The present invention uses the AES encryption algorithm, sets the key length, and supplements and divides the data plaintext to perform segmented encryption on medical and health data, thereby reducing the redundancy of medical and health data, improving the efficiency of medical and health data transmission, and ensuring the high efficiency of medical and health data privacy and security management.

[0089] The present invention discloses a medical health data privacy security management system based on MLWE, comprising: a biomedical sensor, a key generation server, a medical cloud server, a data processing module, a database, an MLWE encryption module and an ASE encryption module;

[0090] The biomedical sensor is used to collect patient medical health data in real time;

[0091] The key generation server is used to generate public keys and private keys in real time;

[0092] The medical cloud server is used to store the collected patient medical health data in the cloud;

[0093] The data processing module is used to process and verify the medical and health data collected from patients in real time;

[0094] The database is used to store the medical and health data of patients collected in real time;

[0095] The MLWE encryption module is used to encrypt the interaction process between the biomedical sensor and the database;

[0096] The ASE encryption module is used to encrypt the interaction process between the database and the medical cloud server.

[0097] (III) Beneficial effects

[0098] Compared with the prior art, the present invention provides a medical health data privacy security management method and system based on MLWE, which has the following beneficial effects:

[0099] 1. The present invention initializes the installed biomedical sensors through a key generation server, and collects the patient's medical health data in real time through the biomedical sensors; at the same time, the real-time collected patient medical health data is processed by data verification and filtering, and the processed patient medical health data is transmitted to the database through the MLWE low expansion rate encryption algorithm; at the same time, the processed patient medical health data in the database is transmitted to the medical cloud server by using the AES encryption algorithm, and decrypted and accessed through user registration, thereby realizing the security management of medical health data privacy and improving the security and accuracy of medical health data privacy security management.

[0100] 2. The present invention completes the initialization of installed biomedical sensors by numbering the installed biomedical sensors and setting the parameters required in the key generation process, thereby improving the efficiency of privacy and security management of medical and health data.

[0101] 3. The present invention completes the preprocessing of the patient medical health data collected in real time by performing data analysis, data editing inspection and data filtering on the patient medical health data collected in real time, thereby ensuring the accuracy of the privacy and security management of the medical health data.

[0102] 4. The present invention uses the MLWE low expansion rate encryption algorithm, key generation, data encryption, homomorphic computing and decryption steps to ensure the encrypted transmission of patient medical and health data, thereby improving the security of medical and health data privacy and security management.

[0103] 5. The present invention uses the AES encryption algorithm, sets the key length, and supplements and splits the data plaintext to perform segmented encryption on the medical and health data, thereby reducing the redundancy of the medical and health data, improving the efficiency of medical and health data transmission, and ensuring the high efficiency of medical and health data privacy and security management. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] Figure 1 It is a schematic diagram of the process structure of the medical health data privacy and security management method of the present invention. DETAILED DESCRIPTION

[0105] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0106] Example 1

[0107] This embodiment discloses a medical health data privacy security management method and system based on MLWE, which specifically includes the following steps:

[0108] S1. Initialize the installed biomedical sensors through the key generation server, and collect patient medical health data in real time through the installed biomedical sensors;

[0109] Initializing the installed biomedical sensors via the key generation server includes the following steps:

[0110] S11, numbering the installed biomedical sensors, and using the numbers of the biomedical sensors as biomedical sensor IDs;

[0111] S12, setting the parameters required in the key generation process;

[0112] The key generation server randomly divides a prime field; sets G1 to be the additive group constructed by all integers in the prime field, and Z to be the set of all prime numbers in the prime field;

[0113] S2. Preprocessing the medical and health data of patients collected in real time to obtain preprocessed medical and health data of patients;

[0114] Preprocessing of patient medical health data collected in real time includes the following steps:

[0115] S21, data analysis;

[0116] Classify the patient medical health data collected in real time by biomedical sensors according to data types;

[0117] The classification includes: time-based data, continuous data, selective data, and descriptive data;

[0118] Time-based data refer to the patient visit time, physical examination time, and examination time of vital signs;

[0119] Continuous data refers to variables that have meaningful values ​​within a certain range, such as height, weight, and body temperature;

[0120] Selection data refers to variables with options, such as gender options and patient adverse status options;

[0121] Descriptive data refers to variables that require specific descriptions, such as the name of the patient's adverse event or the name of the medication;

[0122] S22, data editing check;

[0123] Edit and check the patient's medical and health data collected in real time by biomedical sensors through set patient medical and health data thresholds;

[0124] The editing check pointer checks the value of a single data point;

[0125] For example, in a clinical trial project, the patient's body temperature data should be greater than 30℃ and less than 45℃. When the biomedical sensor detects that a patient's body temperature is less than 30℃ or greater than 45℃, it will automatically prompt that the data is wrong and the threshold range of the value here will be prompted;

[0126] S23, data filtering;

[0127] Deleting and filtering the erroneous data prompted in the data editing check to obtain filtered patient medical health data, and setting the filtered patient medical health data as the pre-processed patient medical health data;

[0128] S3, constructing a low expansion rate encryption algorithm of MLWE, and transmitting the pre-processed patient medical health data to the database based on the constructed encryption algorithm;

[0129] Constructing a low expansion rate encryption algorithm of MLWE and transmitting the pre-processed patient medical health data to the database based on the constructed encryption algorithm includes the following steps:

[0130] S31, setting the low expansion rate encryption algorithm parameters of MLWE;

[0131] The preprocessed patient medical health data is set to a, n=n(a), where n represents the dimension of the vector, n(a) represents the dimension of the preprocessed patient medical health data λ vector, q=q(a), where q represents the modulus of the vector;

[0132] S32, determining a homomorphic encryption scheme of a low expansion rate encryption algorithm of MLWE based on setting parameters of the low expansion rate encryption algorithm of MLWE;

[0133] Determining the homomorphic encryption scheme of the low expansion rate encryption algorithm of MLWE based on setting the parameters of the low expansion rate encryption algorithm of MLWE includes the following steps:

[0134] S321, key generation;

[0135] Generate public and private keys based on key generation calculations;

[0136] Set up two one-way cryptographic hash functions;

[0137] H1: {0,1} l →G1;

[0138] H2: {0,1} * →{0,1} m+l+t ×G1;

[0139] Among them, m represents the message length, l represents the message identifier, t represents the timestamp, {0,1} * Represents a string of any length composed of 0 and 1;

[0140] Randomly select s∈Z, set the system master key to s, and calculate P pub =sP as the system public key, where P is the generator of G1;

[0141] Randomly select y∈Z, set the sensor master key to y, and calculate Y=yP as the sensor public key;

[0142] Randomly select k∈Z and calculate d=k+sQ as the private key, where Q=H1(ID,Y);

[0143] Furthermore, the safety parameter λ is set to be pub ,G1,P>;

[0144] Furthermore, the security parameter λ is a system shared parameter;

[0145] S322, encrypting the pre-processed patient medical health data to be sent;

[0146] Select a pre-processed patient medical health data to be sent i , by generating a public key for encryption calculation Obtain encrypted and pre-processed patient medical health data;

[0147] Among them, a i Indicates the i-th data sent, c i Indicates that the i-th data is encrypted by a hash function, w i represents the secret value randomly generated by the i-th data, H2 represents the second one-way encryption hash function, and Y represents the sensor public key;

[0148] S323, performing homomorphic calculation on the encrypted pre-processed patient medical health data;

[0149] ​Input the encrypted pre-processed patient medical health data, and obtain the pre-processed patient medical health data after homomorphic calculation through homomorphic calculation;

[0150] The homomorphic calculation formula is as follows:

[0151] [a ij ]·[c i ]=[a ij ]·[s i ]+[d i ]wherein, i=1,2,...n;j=1,2,...q;

[0152] Among them, a ij represents the preprocessed patient medical health data in row i and column j, c i represents the encrypted pre-processed patient health data of the ith row, s i represents the system master key of the preprocessed patient health data in the i-th row, d i The private key representing the preprocessed patient health data of the i-th row;

[0153] S324, decryption;

[0154] Input the pre-processed patient medical health data after homomorphic calculation, obtain the pre-processed patient medical health data through decryption calculation and save it in the database;

[0155]

[0156] S4, transmitting the pre-processed patient medical health data in the database to the medical cloud server by using the AES encryption algorithm;

[0157] The following steps are involved in transferring the pre-processed patient medical health data in the database to the medical cloud server using the AES encryption algorithm:

[0158] The process of transmitting the pre-processed patient medical health data in the database to the medical cloud server is set as a data interaction process;

[0159] S41, segmenting and completing the plaintext in the pre-processed patient medical health data that needs to be encrypted during the data interaction process, and dividing the plaintext in the pre-processed patient medical health data that needs to be encrypted into plaintext data blocks equal to the key length;

[0160] The segmentation and completion of the plaintext in the pre-processed patient medical health data that needs to be encrypted during the data interaction process includes the following steps:

[0161] S411, setting the length of the key based on the data interaction process;

[0162] Set the data exchange process to use bytes as the unit;

[0163] Since 1 byte = 8 bits; 1 bit represents a digit in the data exchange process;

[0164] Then the length of the key should be set to an integer multiple of bytes;

[0165] S412, segmenting the plaintext in the preprocessed patient medical health data based on the set key length;

[0166] segmenting the plaintext in the preprocessed patient medical health data based on the number of bytes contained in the plaintext in the preprocessed patient medical health data and the key length;

[0167]

[0168] Among them, E represents the number of plaintext data blocks after segmentation, α represents the number of bytes contained in the plaintext of the patient's medical and health data after preprocessing, and β represents the key length;

[0169] S413, padding the plaintext in the preprocessed patient medical health data that is insufficient in length, padding the end of the plaintext in the preprocessed patient medical health data that is insufficient in length based on the byte difference between the plaintext in the preprocessed patient medical health data and the key length;

[0170] Set the data in the padding bytes to all 0;

[0171] S42, encrypting each plaintext data block after segmentation and padding by using the AES encryption algorithm;

[0172] Encrypting each plaintext data block after segmentation and padding using the AES encryption algorithm includes the following steps:

[0173] S421, generating a vector matrix and performing XOR with the plaintext data block;

[0174] S422, encrypting the plaintext data block using the AES encryption algorithm;

[0175] S4221, setting, the key addition round to perform multiple rounds of addition of the plaintext data block and the key and outputting the result;

[0176] S4222, set a byte substitution layer;

[0177] After multiple rounds of addition, the plaintext data blocks are byte-converted in the byte substitution layer and output;

[0178] S4223, setting the byte step of the row displacement, shifting each byte in the plaintext data after byte substitution in sequence by the byte step of the row displacement;

[0179] S4224, setting the byte step of the column shift, shifting each byte in the plaintext data after the row shift in sequence to set the byte step of the column shift;

[0180] S4225, setting the number of cycles of the AES encryption algorithm, and executing steps S4221 to S4224 based on the set number of cycles;

[0181] S5. Based on the user registration method, the pre-processed patient medical health data in the accessed medical cloud server is decrypted by performing the inverse operation of the encryption algorithm corresponding to each step of the AES encryption algorithm to obtain the corresponding pre-processed patient medical health data.

[0182] Example 2

[0183] This embodiment discloses a medical health data privacy security management system based on MLWE that implements the above-mentioned embodiment method, including: a biomedical sensor, a key generation server, a medical cloud server, a data processing module, a database, an MLWE encryption module, and an AES encryption module;

[0184] The biomedical sensor is used to collect patient medical health data in real time;

[0185] The key generation server is used to generate public keys and private keys in real time;

[0186] The medical cloud server is used to store the collected patient medical health data in the cloud;

[0187] The data processing module is used to process and verify the medical and health data collected from patients in real time;

[0188] The database is used to store the medical and health data of patients collected in real time;

[0189] The MLWE encryption module is used to encrypt the interaction process between the biomedical sensor and the database;

[0190] The AES encryption module is used to encrypt the interaction process between the database and the medical cloud server.

[0191] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A medical health data privacy security management method based on MLWE, characterized in that: The following steps are involved: S1. Initialize the installed biomedical sensors through the key generation server, and collect patient medical health data in real time through the installed biomedical sensors; S2. Preprocessing the medical and health data of patients collected in real time to obtain preprocessed medical and health data of patients; S3, constructing a low expansion rate encryption algorithm of MLWE, and transmitting the pre-processed patient medical health data to the database based on the constructed encryption algorithm; S4, transmitting the pre-processed patient medical health data in the database to the medical cloud server by using the AES encryption algorithm; S5. Based on the user registration method, decrypt the pre-processed patient medical health data in the accessed medical cloud server by performing the inverse operation of the encryption algorithm corresponding to each step of the AES encryption algorithm to obtain the corresponding pre-processed patient medical health data; The method of constructing a low expansion rate encryption algorithm of MLWE and transmitting the pre-processed patient medical health data to a database based on the constructed encryption algorithm includes the following steps: S31, setting the low expansion rate encryption algorithm parameters of MLWE; S32, determining a homomorphic encryption scheme of a low expansion rate encryption algorithm of MLWE based on setting parameters of the low expansion rate encryption algorithm of MLWE; The method of determining the homomorphic encryption scheme of the low expansion rate encryption algorithm of MLWE based on setting the low expansion rate encryption algorithm parameters of MLWE comprises the following steps: S321, key generation; S322, encrypting the pre-processed patient medical health data to be sent; Select a pre-processed patient medical health data to be sent i , by generating a public key for encryption calculation Obtain encrypted and pre-processed patient medical health data; Among them, a i Indicates the i-th data sent, c i Indicates that the i-th data is encrypted by a hash function, w i represents the secret value randomly generated by the i-th data, H2 represents the second one-way encryption hash function, and Y represents the sensor public key; S323, performing homomorphic calculation on the encrypted pre-processed patient medical health data; Input the encrypted pre-processed patient medical health data, and obtain the pre-processed patient medical health data after homomorphic calculation through homomorphic calculation; The homomorphic calculation formula is as follows: [and ij ]·[c i ]=[a ij ]·[with i ]+[d i ] among others,i=1,2,...n;j=1,2,...q; Among them, a ij represents the preprocessed patient medical health data in row i and column j, c i represents the encrypted pre-processed patient health data of the ith row, s i represents the system master key of the preprocessed patient health data in the i-th row, d i represents the private key of the preprocessed patient health data in row i; S324, decryption; Input the pre-processed patient medical health data after homomorphic calculation, obtain the pre-processed patient medical health data through decryption calculation and save it in the database; 2. According to the MLWE-based medical health data privacy security management method of claim 1, it is characterized in that: The biomedical sensor installed by initializing the key generation server comprises the following steps: S11, numbering the installed biomedical sensors, and using the numbers of the biomedical sensors as biomedical sensor IDs; S12, setting the parameters required in the key generation process; The key generation server randomly divides a prime field; sets G1 to be the additive group constructed by all integers in the prime field, and Z to be the set of all prime numbers in the prime field.

3. According to a medical health data privacy security management method based on MLWE according to claim 1, it is characterized in that: The preprocessing of the patient medical health data collected in real time includes the following steps: S21, data analysis; Classify the patient medical health data collected in real time by biomedical sensors according to data types; The classification includes: time-based data, continuous data, selective data, and descriptive data; Time-based data refer to the patient visit time, physical examination time, and examination time of vital signs; Continuous data refers to variables that have meaningful values ​​within a certain range; Choice data refers to variables with options; Descriptive data refers to variables that require specific descriptions; S22, data editing check; Edit and check the patient's medical and health data collected in real time by biomedical sensors through set patient medical and health data thresholds; The editing check is only performed on the value of a single data point; S23, data filtering; The erroneous data prompted in the data editing check are deleted and filtered to obtain the filtered patient medical health data, and the filtered patient medical health data is set as the pre-processed patient medical health data.

4. According to a method for privacy and security management of medical health data based on MLWE according to claim 1, it is characterized in that: The setting of the low expansion rate encryption algorithm parameters of MLWE includes: The preprocessed patient medical health data is set to a, n=n(a), where n represents the dimension of the vector, n(a) represents the dimension of the preprocessed patient medical health data λ vector, q=q(a), where q represents the modulus of the vector, q(a) represents the modulus of the preprocessed patient medical health data a vector.

5. According to a method for privacy and security management of medical health data based on MLWE according to claim 2, it is characterized in that: The key generation step S321 includes the following steps: Generate public and private keys based on key generation calculations; Set up two one-way cryptographic hash functions; H1: {0,1} l →G1; H2:{0,1} * →{0,1} m+l+t ×G1; Where m represents the message length, l represents the message identifier, t represents the timestamp, and {0,1} represents a string of any length composed of 0 and 1. Randomly select s∈Z, set the system master key to s, and calculate P pub =sP as the system public key, where P is the generator of G1 and Z is the set of all prime numbers in the prime domain; Randomly select y∈Z, set the sensor master key to y, and calculate Y=yP as the sensor public key; Randomly select k∈Z and calculate d=k+sQ as the private key of the patient's medical health data, where Q=H1(ID,Y); Set safety parameter λ= pub ,G1,P>;​ The security parameter λ is a system shared parameter.

6. According to a method for privacy and security management of medical health data based on MLWE according to claim 1, it is characterized in that: The method of transmitting the pre-processed patient medical health data in the database to the medical cloud server by using the AES encryption algorithm includes the following steps: The process of transmitting the pre-processed patient medical health data in the database to the medical cloud server is set as a data interaction process; S41, segmenting and completing the plaintext in the pre-processed patient medical health data that needs to be encrypted during the data interaction process, and dividing the plaintext in the pre-processed patient medical health data that needs to be encrypted into plaintext data blocks equal to the key length; The segmentation and completion of the plaintext in the pre-processed patient medical health data that needs to be encrypted during the data interaction process includes the following steps: S411, setting the length of the key based on the data interaction process; Set the data exchange process to use bytes as the unit; S412, segmenting the plaintext in the preprocessed patient medical health data based on the set key length; segmenting the plaintext in the preprocessed patient medical health data based on the number of bytes contained in the plaintext in the preprocessed patient medical health data and the key length; Among them, E represents the number of plaintext data blocks after segmentation, α represents the number of bytes contained in the plaintext of the patient's medical and health data after preprocessing, and β represents the key length; S413, padding the plaintext in the preprocessed patient medical health data that is insufficient in length, based on the byte difference between the plaintext in the preprocessed patient medical health data and the key length, padding the end of the plaintext in the preprocessed patient medical health data that is insufficient in length; Set the data in the padding bytes to all 0; S42, encrypting each plaintext data block after segmentation and padding using the AES encryption algorithm.

7. According to claim 6, a medical health data privacy security management method based on MLWE is characterized in that: The process of encrypting each of the divided and padded plaintext data blocks by using the AES encryption algorithm comprises the following steps: S421, generating a vector matrix and performing XOR with the plaintext data block; S422, encrypting the plaintext data block using the AES encryption algorithm; S4221, setting, the key addition round to perform multiple rounds of addition of the plaintext data block and the key and outputting the result; S4222, set a byte substitution layer; After multiple rounds of addition, the plaintext data blocks are byte-converted in the byte substitution layer and output; S4223, setting the byte step of the row displacement, shifting each byte in the plaintext data after byte substitution in sequence by the byte step of the row displacement; S4224, setting the byte step of the column shift, shifting each byte in the plaintext data after the row shift in sequence to set the byte step of the column shift; S4225. Set the number of cycles of the AES encryption algorithm, and execute steps S4221 to S4224 based on the set number of cycles.

8. A system for implementing the medical health data privacy security management method based on MLWE according to any one of claims 1 to 7, characterized in that: It includes biomedical sensors, key generation servers, medical cloud servers, data processing modules, databases, MLWE encryption modules, and AES encryption modules; The biomedical sensor is used to collect patient medical health data in real time; The key generation server is used to generate public keys and private keys in real time; The medical cloud server is used to store the collected patient medical health data in the cloud; The data processing module is used to process and verify the medical and health data collected from patients in real time; The database is used to store the medical and health data of patients collected in real time; The MLWE encryption module is used to encrypt the interaction process between the biomedical sensor and the database; The AES encryption module is used to encrypt the interaction process between the database and the medical cloud server.

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