Medical image data mobile terminal security display method and system

Abstract

The application belongs to the technical field of safe display, and discloses a medical image data mobile terminal safe display method and system. The method comprises the following steps: a trusted agency generates a public-private key pair and sends the private key to an intermediate server; a mobile terminal collects user login information, data request information and device location data and sends them to a cloud data center; the cloud data center performs login verification and extracts medical multi-modal data; the cloud data center selects a server, obtains a target intermediate server and sends the medical multi-modal data to the target intermediate server; the target intermediate server performs image conversion on the medical multi-modal data and encrypts sensitive information and sends them to the mobile terminal; and the mobile terminal decrypts the encrypted medical image data, obtains and displays the decrypted medical image data. The application solves the problems of insufficient data transmission security, insufficient user privacy protection, poor data integrity and traceability and low data processing efficiency in the prior art.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of safe display, and particularly relates to a medical image data mobile terminal safe display method and system. BACKGROUND

[0002] In the process of medical diagnosis and treatment, a large amount of medical data is often generated, which usually includes image, text and other multi-modal data. With the rapid development of medical informatization, remote access and online display of medical data become more and more important.

[0003] The existing medical data display technology has the following defects:

[0004] 1) Insufficient data transmission security: In the data transmission process, data encryption is required using encryption algorithms, but some existing encryption algorithms may be outdated and vulnerable to quantum computing attacks or other advanced cracking techniques, making encrypted data vulnerable to unauthorized access, and the management of keys may not be strict enough, leading to key leakage or tampering, thus greatly reducing the security of the encryption system;

[0005] 2) Insufficient user privacy protection: In medical data, there are sensitive information of patients, and existing technologies cannot effectively identify and encrypt these sensitive information, increasing the risk of privacy leakage; there is a lack of strict permission control for accessing medical data, making unauthorized users also able to access sensitive data;

[0006] 3) Poor data integrity and traceability: Existing technologies are difficult to detect whether data has been tampered with during transmission, especially on mobile terminals, the integrity and authenticity of data are difficult to guarantee, and there is a lack of transparency in the data flow process, making it difficult to trace the responsibility and find the source of the problem;

[0007] 4) Low data processing efficiency: Existing methods may not effectively select the nearest or most suitable server to the user, resulting in data transmission delay and cost increase, and in processing and generating medical image data, resources are not effectively utilized, resulting in slow data processing speed and poor user experience. SUMMARY

[0008] In order to solve the problems of insufficient data transmission security, insufficient user privacy protection, poor data integrity and traceability, and low data processing efficiency in the existing technology, the application aims to provide a medical image data mobile terminal safe display method and system.

[0009] The technical solution adopted by the application is:

[0010] A medical image data mobile terminal safe display method, comprising the following steps:

[0011] The trusted agency generates a public-private key pair of all intermediate servers and sends the private key in the public-private key pair to the corresponding intermediate server using QKD technology;

[0012] The mobile terminal collects user login information, data request information and device location data, and sends the user login information, data request information and device location data to the cloud data center;

[0013] The cloud data center performs login verification on the user login information, and after the login verification is passed, extracts the corresponding medical multi-modal data in the preset blockchain network according to the data request information;

[0014] The cloud data center uses a preset server selection model to select a target intermediate server according to the device location data, and sends the medical multi-modal data to the target intermediate server;

[0015] The target intermediate server performs image conversion and sensitive information encryption on the medical multi-modal data according to the target private key in the target public-private key pair, obtains encrypted medical image data, and sends it to the mobile terminal;

[0016] The mobile terminal calls the trusted agency to extract the target public key in the target public-private key pair, and decrypts the encrypted medical image data according to the target public key to obtain and display decrypted medical image data.

[0017] Further, the trusted agency generates a public-private key pair of all intermediate servers and sends the private key in the public-private key pair to the corresponding intermediate server using QKD technology, including the following steps:

[0018] The trusted agency collects the server attribute information of all intermediate servers and uses an asymmetric key generation algorithm to generate a public-private key pair for each intermediate server; the public-private key pair includes a public key and a private key;

[0019] An open communication line and a quantum communication line are deployed between the trusted agency and each intermediate server, and the private key is converted into a private key quantum state;

[0020] The private key quantum state is sent to the corresponding intermediate server through the quantum communication line, and the private key quantum state is measured to obtain a first measurement result;

[0021] The first measurement result is compared with a second measurement result obtained by measuring the private key quantum state by the intermediate server through the open communication line, and the error rate is estimated, and the private key is obtained at the intermediate server.

[0022] Further, the cloud data center performs login verification on the user login information, and after the login verification passes, extracts corresponding medical multi-modal data in the preset blockchain network according to the data request information, including the following steps:

[0023] The cloud data center calls a preset legal user database to perform login verification on the user login information, and if the login verification passes, proceeds to the next step, otherwise, ends the data extraction;

[0024] According to the data request information, match in the distributed ledger of the preset blockchain network to obtain the storage address of the matched medical multi-modal data;

[0025] According to the storage address, extract the corresponding medical multi-modal data in the blockchain network, generate a transaction record, and use the blockchain network to perform distributed storage on the transaction record and update the distributed ledger.

[0026] Further, the server selection model is constructed based on the FFWA algorithm.

[0027] Further, the cloud data center uses a preset server selection model to perform server selection according to the device location data to obtain a target intermediate server, and sends the medical multi-modal data to the target intermediate server, including the following steps:

[0028] The cloud data center acquires all candidate intermediate servers near the device according to the device location data, and inputs the candidate intermediate servers into a preset server selection model;

[0029] Based on the server selection model, the target intermediate server is obtained by performing server selection according to the device location data and the candidate intermediate servers, with the optimization target of minimizing data transmission cost;

[0030] Call a trusted agency to extract a target public key in a target public-private key pair of the target intermediate server, and encrypt the medical multi-modal data according to the target public key to obtain encrypted medical multi-modal data;

[0031] Send the encrypted medical multi-modal data to the target intermediate server.

[0032] Further, based on the server selection model, the target intermediate server is obtained by performing server selection according to the device location data and the candidate intermediate servers, with the optimization target of minimizing data transmission cost, including the following steps:

[0033] Based on the server selection model, the fitness function of the FFWA algorithm is set according to the optimization target of minimizing the data transmission cost;

[0034] According to the server location data of the alternative intermediate server, boundary conditions of the FFWA algorithm are set, and the server location data of the intermediate server is encoded into an individual vector of an FFWA individual of the FFWA algorithm;

[0035] According to the boundary conditions and the individual vector, an initial FFWA population including a plurality of initial FFWA individuals is generated;

[0036] An initial fitness value of each initial FFWA individual is obtained using a fitness function, and the initial FFWA population is updated according to the initial fitness value to obtain an updated FFWA population;

[0037] The FFWA population is iteratively updated until the number of iterations reaches a maximum iteration threshold or the fitness value of the optimal individual in the updated FFWA population meets a fitness value threshold, and the optimal individual is retained;

[0038] The individual vector of the optimal individual is decoded to obtain server location data of the optimal intermediate server, and the target intermediate server is obtained according to the server location data of the optimal intermediate server.

[0039] Further, the target intermediate server performs image conversion and sensitive information encryption on the medical multi-modal data according to the target private key in the target public-private key pair to obtain encrypted medical image data, and sends the encrypted medical image data to the mobile terminal, including the following steps:

[0040] The target intermediate server decrypts the encrypted medical multi-modal data according to the target private key in the target public-private key pair to obtain decrypted medical multi-modal data;

[0041] The decrypted medical multi-modal data is parsed to obtain a plurality of medical basic data of different modalities;

[0042] The plurality of medical basic data is written into a modal region corresponding to a preset image template, and the preset image template after writing data is converted into an image format to obtain medical image data;

[0043] The medical image data is subjected to sensitive information recognition using a preset sensitive information recognition model to obtain a plurality of sensitive information regions, and corresponding sensitive information masks are generated;

[0044] The medical image data is subjected to sensitive information encryption according to the target private key to obtain initial encrypted medical image data;

[0045] The plurality of sensitive information masks and the initial encrypted medical image data are integrated to obtain final encrypted medical image data;

[0046] According to the device position data, the final encrypted medical image data is sent to the corresponding mobile terminal.

[0047] Further, the sensitive information identification model is constructed based on a SAM-Med2D-CTPN-MLP algorithm.

[0048] Further, the mobile terminal calls the trusted agency, extracts the target public key in the target public-private key pair, and decrypts the encrypted medical image data according to the target public key to obtain and display the decrypted medical image data, including the following steps:

[0049] The mobile terminal calls the trusted agency, extracts the target public key in the target public-private key pair of the target intermediate server, and

[0050] According to the target public key, the encrypted medical image data is decrypted to obtain initial decrypted medical image data provided with a plurality of sensitive information masks.

[0051] The user login information of the user is subjected to authority verification, and if the authority verification is passed, the plurality of sensitive information masks of the initial decrypted medical image data are removed to obtain the final decrypted medical image data.

[0052] The final decrypted medical image data is subjected to visual display.

[0053] A medical image data mobile terminal security display system for realizing a medical image data mobile terminal security display method, the system comprising a trusted agency, a cloud data center, a mobile terminal and a plurality of intermediate servers, the trusted agency and the cloud data center are respectively in communication connection with the mobile terminal and the plurality of intermediate servers, and the mobile terminal is respectively in communication connection with the plurality of intermediate servers within a communication range.

[0054] The medical image data mobile terminal security display system has the following advantages:

[0055] This invention provides a secure mobile display method and system for medical image data, enhancing data transmission security by utilizing QKD (Quick Key Distribution) technology to distribute keys, ensuring absolute key security even in the face of quantum computer threats. End-to-end encryption of data using asymmetric encryption algorithms ensures data security during transmission. It also improves user privacy by encrypting sensitive information within the data to effectively prevent leakage. An access verification mechanism ensures that only authorized users can access and view the complete medical image data. Furthermore, it guarantees data integrity and traceability by leveraging the immutability of blockchain networks. Any data tampering can be detected and traced, and the distributed ledger technology of blockchain provides transparency in data flow, facilitating auditing and supervision. Finally, it improves data processing efficiency by intelligently selecting the most suitable intermediate server based on device location data, reducing data transmission latency, increasing response speed, optimizing data processing workflows, improving the efficiency of displaying medical images on mobile devices, and enhancing user experience.

[0056] Other beneficial effects of the present invention will be further explained in the specific embodiments. Attached Figure Description

[0057] Figure 1 This is a flowchart of the method for secure display of medical image data on a mobile device in this invention.

[0058] Figure 2 This is a structural block diagram of the mobile terminal secure display system for medical image data in this invention. Detailed Implementation

[0059] The present invention will be further explained below with reference to the accompanying drawings and specific embodiments.

[0060] Example 1:

[0061] like Figure 1 As shown, this embodiment provides a method for securely displaying medical image data on a mobile device, including the following steps:

[0062] S1: A trusted institution generates public-private key pairs for all intermediate servers and uses Quantum Key Distribution (QKD) technology to send the private key from the public-private key pair to the corresponding intermediate server, including the following steps:

[0063] S1-1: Trusted institution, which collects server attribute information of all intermediate servers and uses an asymmetric key generation algorithm to generate a public-private key pair for each intermediate server; the public-private key pair includes a public key and a private key.

[0064] S1-2: Deploy an open communication line and a quantum communication line between the trusted agency and each intermediate server, and convert the private key into a private key quantum state;

[0065] S1-3: Send the private key quantum state to the corresponding intermediate server through the quantum communication line, and measure the private key quantum state to obtain a first measurement result;

[0066] S1-4: Through the open communication line, the first measurement result is compared with the second measurement result obtained by the intermediate server measuring the private key quantum state, and the error rate is estimated, and the private key is obtained at the intermediate server;

[0067] QKD technology uses the characteristics of quantum communication to provide a key distribution method that is almost impossible to crack, greatly improving the security of key transmission, ensuring that it is not tampered with during transmission, and ensuring the integrity of the information;

[0068] S2: Mobile terminal, collect user login information, data request information and device location data, and send the user login information, data request information and device location data to the cloud data center;

[0069] S3: The cloud data center verifies the user login information, and after the login verification is passed, extracts the corresponding medical multi-modal data in the preset blockchain network according to the data request information, including the following steps:

[0070] S3-1: The cloud data center calls the preset legal user database to verify the user login information, and if the login verification is passed, it proceeds to the next step, otherwise it ends the data extraction;

[0071] S3-2: According to the data request information, match in the distributed ledger of the preset blockchain network to obtain the storage address of the matched medical multi-modal data;

[0072] The distributed ledger of the blockchain network has the characteristics of being tamper-proof, which means that once the medical multi-modal data is recorded, it cannot be modified without authorization, thereby ensuring the integrity of the medical multi-modal data; Decentralized storage, data is distributed on multiple nodes, reducing the risk of single point failure, improving the robustness of the system;

[0073] S3-3: According to the storage address, extract the corresponding medical multi-modal data in the blockchain network, generate a transaction record, and use the blockchain network to store the transaction record in a distributed manner and update the distributed ledger;

[0074] S4: The cloud data center selects a target intermediate server according to the device location data using a preset server selection model, and sends the medical multi-modal data to the target intermediate server, including the following steps:

[0075] S4-1: The cloud data center obtains all candidate intermediate servers according to the device location data, and inputs the candidate intermediate servers into a preset server selection model;

[0076] The server selection model is constructed based on a Fast Fireworks Optimization Algorithm (FFWA) algorithm; the FFWA algorithm can perform iterative optimization in a search space, meet an optimization target, generate a global optimal solution, and realize fast selection of an intermediate server;

[0077] S4-2: Based on the server selection model, a target intermediate server is selected according to the device location data and the candidate intermediate servers to minimize data transmission cost, including the following steps:

[0078] S4-2-1: Based on the server selection model, a fitness function of the FFWA algorithm is set according to an optimization target of minimizing data transmission cost;

[0079] The formula is:

[0080]

[0081] In the formula, f(q) is a fitness value of an FFWA individual q; A(q) is a transmission resource cost function of the FFWA individual q; T(q) is a transmission time cost function of the FFWA individual q; L(q) is a transmission distance cost function of the FFWA individual q; and α, β, are weight coefficients; and q is an FFWA individual reference parameter;

[0082] By selecting a server with the lowest transmission cost, the cost investment of data transmission can be reduced, and by selecting a server with low network delay, the speed and efficiency of data transmission can be improved;

[0083] S4-2-2: The boundary conditions of the FFWA algorithm are set according to the server location data of the candidate intermediate servers, and the server location data of the intermediate servers is encoded into an individual vector of an FFWA individual of the FFWA algorithm;

[0084] S4-2-3: According to the boundary conditions and the individual vector, an initial FFWA population including a plurality of initial FFWA individuals is generated using Circle chaotic mapping;

[0085] The formula is:

[0086]

[0087] where q l' is the initial FFWA individual of Circle chaotic mapping; q' l' is the initial FFWA individual generated randomly; and l' is the FFWA individual indicator;

[0088] S4-2-4: using the fitness function, obtaining the initial fitness value of each initial FFWA individual, and updating the initial FFWA population according to the initial fitness value to obtain the updated FFWA population, including the following steps:

[0089] S4-2-4-1: using the fitness function, obtaining the initial fitness value of each initial FFWA individual;

[0090] S4-2-4-2: according to the initial fitness value, obtaining the explosion radius and the number of sparks of the initial FFWA individual;

[0091] The formula is:

[0092]

[0093] where S l' is the number of sparks of the initial FFWA individual q l' ; M' is a number constant; f max is the maximum fitness value in the initialized FFWA population; f(q l' ) is the fitness value of the initial FFWA individual q l' ; τ is an infinitesimal constant; a is a convergence factor; and σ is a positive real number not equal to 0;

[0094]

[0095] where R l' is the explosion radius of the initial FFWA individual q l' ; is the explosion radius adjustment constant; f min is the minimum fitness value in the initialized FFWA population;

[0096]

[0097] where a is the convergence factor; tanh(.) is the hyperbolic tangent function; t and t max are the current iteration number and the maximum iteration number, respectively; a max , a minare the maximum and minimum values of the convergence factor respectively; λ is the decreasing rate parameter, k is the decreasing period parameter, λ =-2π, k = π;

[0098] The number of sparks determines the number of sub-fireworks generated after each explosion of the fireworks, and the explosion radius determines the distribution range of the sparks in the solution space after the explosion of the fireworks. In the early iteration stage, the value of a is large, the number of sparks of the FFWA individual is small, and the explosion radius is large, which helps to reduce the calculation burden and distribute more widely, which helps to explore more solution space. In the later iteration stage, the smaller explosion radius helps to perform fine search in the local area, and the larger number of sparks helps to increase the diversity of search;

[0099] S4-2-4-3: According to the explosion radius, the number of sparks and the fitness value, the fireworks explosion is carried out to obtain a plurality of updated FFWA individuals of the updated FFWA population;

[0100] The formula is:

[0101] q' l' = q l' +S l' ×rand(-1,1)

[0102] In the formula, q' l' is the updated FFWA individual; rand(-1,1) is a random number between-1 and 1;

[0103] S4-2-4-4: Using the Gaussian mutation algorithm, the initialized FFWA population is subjected to Gaussian mutation to generate a plurality of Gaussian mutated FFWA individuals of the Gaussian mutated FFWA population;

[0104] The formula is:

[0105] q" l' = q l' +S l' ×G(1,1)

[0106] In the formula, q" l' is the Gaussian mutated FFWA individual; G(1,1) is a random number of Gaussian distribution with mean and variance of 1;

[0107] S4-2-4-5: Using the dynamic reverse learning algorithm, the initialized FFWA population is subjected to dynamic reverse learning to obtain a plurality of reverse FFWA individuals of the reverse FFWA population;

[0108] The formula is:

[0109] q' l' = γ(L max +L min )-q l'

[0110] wherein q' l' is the reverse FFWA individual; γ is a decreasing inertia coefficient; L max , L min are the maximum and minimum values of the vector space respectively;

[0111] S4-2-4-6: obtaining the fitness values of each updated FFWA individual, the Gaussian variant FFWA individual and the reverse FFWA individual, and taking the FFWA individual with the minimum fitness value as the optimal individual;

[0112] S4-2-5: iteratively updating the FFWA population until the iteration number reaches a maximum iteration threshold value, or the fitness value of the optimal individual in the updated FFWA population meets a fitness value threshold value, and retaining the optimal individual;

[0113] S4-2-6: decoding the individual vector of the optimal individual to obtain the server location data of the optimal intermediate server, and obtaining the target intermediate server according to the server location data of the optimal intermediate server;

[0114] S4-3: calling the trusted authority, extracting a target public key in a target public-private key pair of the target intermediate server, and encrypting the medical multi-modal data according to the target public key to obtain encrypted medical multi-modal data;

[0115] S4-4: sending the encrypted medical multi-modal data to the target intermediate server;

[0116] S5: the target intermediate server, performing image conversion and sensitive information encryption on the medical multi-modal data according to a target private key in the target public-private key pair to obtain encrypted medical image data, and sending the encrypted medical image data to the mobile terminal, including the following steps:

[0117] S5-1: the target intermediate server, decrypting the encrypted medical multi-modal data according to the target private key in the target public-private key pair to obtain decrypted medical multi-modal data;

[0118] The security and integrity of the data on the server side are ensured, and only the intermediate server with the private key can access the original medical multi-modal data; the data processing is decentralized to the intermediate server, reducing the resource investment and computational complexity of the cloud data center;

[0119] S5-2: performing data analysis on the decrypted medical multi-modal data to obtain a plurality of medical basic data of different modalities;

[0120] The medical multi-modal data includes user's medical image data, medical diagnosis report and clinical data, and other multi-modal data;

[0121] S5-3: Write a number of medical basic data into the modality region corresponding to the preset image template, and convert the preset image template after writing data into an image format to obtain medical image data;

[0122] The format of the medical image is standardized, facilitating subsequent display on a mobile terminal. Moreover, the medical image data in image format is not easy to be copied, tampered with, and exported, improving the security of the medical image data;

[0123] S5-4: Use a preset sensitive information recognition model to recognize sensitive information in the medical image data, obtain a number of sensitive information regions, and generate corresponding sensitive information masks;

[0124] The patient's privacy is protected, and the sensitive information is ensured not to be leaked during transmission. The sensitive information mask provides an additional privacy protection layer, so that even if the data is decrypted, the sensitive information will not be directly exposed;

[0125] The sensitive information recognition model is constructed based on the Segment Anything Model-Medicine 2 Dimensionality (SAM-Med2D), Detecting Text in Natural Image with Connectionist Text Proposal Network (CTPN), and Multilayer Perceptron (MLP) algorithms;

[0126] The sensitive information recognition model includes an image segmentation module constructed based on the SAM-Med2D algorithm, a text detection module constructed based on the CTPN, and a sensitive information recognition module constructed based on the MLP algorithm. The image segmentation module is used to segment the lesion tissue region in the medical image data and extract the first image features of the lesion tissue region. The text detection module recognizes and extracts features of the text region in the medical image data to obtain the second image features of the text region. The sensitive information recognition module recognizes sensitive information according to the first and second image features, outputs a number of sensitive information regions and positions, and realizes sensitive information recognition of the medical image data;

[0127] S5-5: According to the target private key, the sensitive information of the medical image data is encrypted to obtain the initial encrypted medical image data;

[0128] S5-6: Integrate a number of sensitive information masks with the initial encrypted medical image data to obtain the final encrypted medical image data;

[0129] S5-7: According to the device location data, send the final encrypted medical image data to the corresponding mobile terminal;

[0130] S6: The mobile terminal calls the trusted agency, extracts the target public key in the target public-private key pair, and decrypts the encrypted medical image data according to the target public key to obtain and display the decrypted medical image data, including the following steps:

[0131] S6-1: The mobile terminal calls the trusted agency and extracts the target public key in the target public-private key pair of the target intermediate server;

[0132] S6-2: According to the target public key, the encrypted medical image data is decrypted to obtain the initial decrypted medical image data with several sensitive information masks;

[0133] S6-3: The user login information of the user is verified for authority, and if the authority verification is passed, the several sensitive information masks of the initial decrypted medical image data are removed to obtain the final decrypted medical image data;

[0134] S6-4: The final decrypted medical image data is visually displayed;

[0135] Commonly ensure the secure decryption and display of medical image data on the mobile terminal, by using public key encryption and authority verification, this method not only protects the confidentiality and integrity of the data, but also provides fine control of sensitive information, in addition, the visualization of the mobile terminal improves the accessibility and practicality of medical data, which helps to improve the quality and efficiency of medical services.

[0136] Embodiment 2:

[0137] As shown in Figure 2 The present embodiment provides a medical image data mobile terminal secure display system for realizing a medical image data mobile terminal secure display method, the system includes a trusted agency, a cloud data center, a mobile terminal and several intermediate servers, the trusted agency and the cloud data center are respectively connected with the mobile terminal and the several intermediate servers, and the mobile terminal is respectively connected with the several intermediate servers within the communication range;

[0138] The trusted agency is used to generate the public-private key pairs of all intermediate servers, and uses QKD technology to send the private key in the public-private key pair to the corresponding intermediate server;

[0139] The cloud data center is configured to build a server selection model and a sensitive information identification model, and deploy the server selection model and the sensitive information identification model on all intermediate servers; perform login verification on user login information, and after the login verification passes, extract corresponding medical multi-modal data in a preset blockchain network according to data request information; perform server selection by using a preset server selection model according to device location data, obtain a target intermediate server, and send the medical multi-modal data to the target intermediate server;

[0140] The intermediate server is configured to perform image conversion and sensitive information encryption on the medical multi-modal data according to a target private key in a target public-private key pair, obtain encrypted medical image data, and send the encrypted medical image data to the mobile terminal;

[0141] The mobile terminal is configured to collect user login information, data request information, and device location data, and send the user login information, the data request information, and the device location data to the cloud data center; call a trusted agency, extract a target public key in a target public-private key pair, and decrypt the encrypted medical image data according to the target public key to obtain and display decrypted medical image data.

[0142] The medical image data mobile terminal security display method and system provided by the application enhance data transmission security, distribute keys by using QKD technology, ensure the absolute security of the keys, ensure the security of data transmission even in the face of quantum computer threats, ensure the security of data in the transmission process by using an asymmetric encryption algorithm to encrypt the data end to end, improve user privacy protection, encrypt sensitive information in the data to effectively prevent sensitive information leakage, ensure that only authorized users can access and view complete medical image data through a permission verification mechanism, ensure data integrity and traceability, ensure data integrity and traceability by using the non-tamperability of the blockchain network, detect and trace any tampering with the data, and the distributed ledger technology of the blockchain network provides transparency of data flow, which is convenient for auditing and supervision, improves data processing efficiency, intelligently selects the most suitable intermediate server according to device location data, reduces data transmission delay, improves response speed, optimizes data processing flow, improves the efficiency of displaying medical images on the mobile terminal, and improves user experience.

[0143] The application is not limited to the above optional embodiments, and anyone can derive other various forms of products under the inspiration of the application. The above specific embodiments should not be understood as limiting the protection scope of the application, and the protection scope of the application should be defined by the claims, and the specification can be used to interpret the claims.

Claims

1. A method for securely displaying medical image data on a mobile device, characterized in that: Includes the following steps: A trusted organization generates public-private key pairs for all intermediate servers and uses QKD technology to send the private key from the public-private key pair to the corresponding intermediate server. On the mobile device, user login information, data request information, and device location data are collected and sent to the cloud data center. The cloud data center verifies user login information. After successful login verification, it extracts the corresponding medical multimodal data from the preset blockchain network based on the data request information. The cloud data center selects a target intermediate server based on device location data using a preset server selection model, and then sends the medical multimodal data to the target intermediate server. The target intermediate server, based on the target private key in the target public-private key pair, performs image conversion and encryption of sensitive information on the medical multimodal data to obtain encrypted medical image data, which is then sent to the mobile terminal. On the mobile device, a trusted institution is invoked to extract the target public key from the target public-private key pair. Based on the target public key, the encrypted medical image data is decrypted, and the decrypted medical image data is obtained and displayed. A trusted organization generates public-private key pairs for all intermediate servers and uses QKD technology to send the private key from the public-private key pair to the corresponding intermediate server, including the following steps: A trusted institution collects server attribute information from all intermediate servers and uses an asymmetric key generation algorithm to generate a public-private key pair for each intermediate server; the public-private key pair includes a public key and a private key. Between the trusted institution and each intermediate server, public communication lines and quantum communication lines are deployed, and private keys are converted into private key quantum states; The private key quantum state is sent to the corresponding intermediate server via a quantum communication line, and the private key quantum state is measured to obtain the first measurement result. By using a public communication line, the first measurement result is compared with the second measurement result obtained by the intermediate server measuring the quantum state of the private key using public basis vectors and error rate estimation, and the private key is obtained from the intermediate server. The cloud data center, based on device location data, uses a preset server selection model to select a target intermediate server and sends the medical multimodal data to the target intermediate server, including the following steps: The cloud data center obtains all nearby alternative intermediate servers based on device location data and inputs these alternative intermediate servers into a preset server selection model. Based on the server selection model, with the goal of minimizing data transmission costs, the target intermediate server is selected by considering device location data and candidate intermediate servers. The system calls upon a trusted authority to extract the target public key from the target public-private key pair of the target intermediate server, and then encrypts the medical multimodal data based on the target public key to obtain the encrypted medical multimodal data. The encrypted medical multimodal data is sent to the target intermediate server; The target intermediate server, based on the target private key in the target public-private key pair, performs image conversion and encryption of sensitive information on the medical multimodal data to obtain encrypted medical image data, which is then sent to the mobile device. This includes the following steps: The target intermediate server decrypts the encrypted medical multimodal data based on the target private key in the target public-private key pair, and obtains the decrypted medical multimodal data. Data parsing was performed on the decrypted multimodal medical data to obtain several basic medical data in different modalities; Several basic medical data are written into the modal region corresponding to a preset image template, and the preset image template after the data is written is converted into an image format to obtain medical image data. Using a pre-defined sensitive information recognition model, sensitive information is identified in medical image data to obtain several sensitive information regions and generate corresponding sensitive information masks. Based on the target private key, sensitive information in the medical image data is encrypted to obtain the initial encrypted medical image data; Several sensitive information masks are integrated with the initial encrypted medical image data to obtain the final encrypted medical image data. Based on the device location data, the final encrypted medical image data is sent to the corresponding mobile device.

2. The method for secure display of medical image data on a mobile device according to claim 1, characterized in that: The cloud data center verifies user login information. After successful login verification, it extracts the corresponding medical multimodal data from a pre-defined blockchain network based on the data request information, including the following steps: The cloud data center calls the preset database of legitimate users to verify the user's login information. If the login verification is successful, it proceeds to the next step; otherwise, the data extraction ends. Based on the data request information, a match is made in the distributed ledger of the preset blockchain network to obtain the storage address of the successfully matched medical multimodal data. Based on the storage address, the corresponding medical multimodal data is extracted from the blockchain network to generate transaction records. The blockchain network is then used to distribute and store the transaction records and update the distributed ledger.

3. The method for secure display of medical image data on a mobile device according to claim 2, characterized in that: The server selection model described is constructed based on the FFWA algorithm.

4. The method for secure display of medical image data on a mobile device according to claim 1, characterized in that: Based on the server selection model, with the optimization objective of minimizing data transmission cost, the target intermediate server is selected by considering device location data and candidate intermediate servers. The process includes the following steps: Based on the server selection model, the optimization objective is to minimize data transmission cost, and the fitness function of the FFWA algorithm is set according to the optimization objective. Based on the server location data of the candidate intermediate servers, the boundary conditions of the FFWA algorithm are set, and the server location data of the intermediate servers are encoded into individual vectors of the FFWA individuals of the FFWA algorithm. Based on the boundary conditions and individual vectors, an initial FFWA population is generated, which includes several initial FFWA individuals. The fitness function is used to obtain the initial fitness value of each initial FFWA individual, and the initial FFWA population is updated according to the initial fitness value to obtain the updated FFWA population. The FFWA population is iteratively updated until the number of iterations reaches the maximum iteration threshold, or the fitness value of the best individual in the updated FFWA population meets the fitness value threshold, in which case the best individual is retained. The individual vector of the optimal individual is decoded to obtain the server location data of the optimal intermediate server, and the target intermediate server is obtained based on the server location data of the optimal intermediate server.

5. A method for secure display of medical image data on a mobile device according to claim 1, characterized in that: The sensitive information identification model is constructed based on the SAM-Med2D-CTPN-MLP algorithm.

6. A method for secure display of medical image data on a mobile device according to claim 5, characterized in that: On the mobile device, a trusted institution is invoked to extract the target public key from the target public-private key pair. Based on the target public key, the encrypted medical image data is decrypted, and the decrypted medical image data is obtained and displayed. This process includes the following steps: On the mobile device, a trusted authority is invoked to extract the target public key from the target public-private key pair of the target intermediate server; Based on the target public key, the encrypted medical image data is decrypted to obtain the initial decrypted medical image data with several sensitive information masks set. The user's login information is verified for permissions. If the permission verification is successful, several sensitive information masks of the initial decrypted medical image data are removed to obtain the final decrypted medical image data. The final decrypted medical image data is then visualized.

7. A mobile terminal secure display system for medical image data, used to implement the mobile terminal secure display method for medical image data as described in any one of claims 1-6, characterized in that: The system includes a trusted organization, a cloud data center, a mobile terminal, and several intermediate servers. The trusted organization and the cloud data center are respectively connected to the mobile terminal and several intermediate servers. The mobile terminal is connected to several intermediate servers within its communication range.

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