Optimization method of passenger information encryption algorithm based on big data

Abstract

The present invention discloses an optimization method based on a big data passenger information encryption algorithm, the method comprising: the method comprising: calculating the similarity between user travel data and a first travel model, a second travel model and a third travel model based on a neural network; generating an output system public key based on user identity and saving a master key; inputting a master key, selecting a user attribute list of at least one group based on similarity to group and calculate the keys of k attributes; inputting a public key, a message and an access tree; inputting a public key, a private key, a ciphertext and an incomplete access structure; if the user's attributes satisfy the entire access control structure, a root node secret value can be obtained through recursive calculation and the plaintext can be successfully calculated; wherein the user's attribute list includes three attribute lists of departure time, departure place and destination. The main purpose of the present invention is to provide an optimization method based on a big data passenger information encryption algorithm, which solves the technical problem that the existing passenger attribute encryption algorithm has a single processing method.

Description

Technical Field

[0001] The present invention relates to the field of information security technology, and in particular to an optimization method based on a big data passenger information encryption algorithm. Background Art

[0002] In traditional attribute encryption algorithms, the encryptor sends the access structure to the decryption user at the same time as the ciphertext message is sent to the decryption user, but sometimes the access structure itself contains private information. However, in the existing CP-ABE schemes that hide the access structure, the access control of most schemes only meets the AND gate structure and cannot meet the requirements of attribute cryptography for fine-grained access control. The few schemes that meet the fine-grained access structure cannot meet the actual needs because the key and ciphertext are too long and the amount of calculation is too large, so they are not very practical. Summary of the invention

[0003] The main purpose of the present invention is to provide an optimization method based on a big data passenger information encryption algorithm, which solves the technical problem of the single processing method of the existing passenger attribute encryption algorithm.

[0004] The present application proposes an optimization method based on a big data passenger information encryption algorithm, the method comprising: calculating the similarity between user travel data and a first travel model, a second travel model and a third travel model based on a neural network; generating an output system public key based on user identity and saving a master key; inputting the master key, and grouping and calculating the keys of k attributes by selecting a user's attribute list of at least one group based on similarity; inputting a public key, a message and an access tree; inputting a public key, a private key, a ciphertext and an incomplete access structure; if the user's attributes satisfy the entire access control structure, a root node secret value can be obtained through recursive calculation and the plaintext can be successfully calculated; wherein the user's attribute list includes three attribute lists of departure time, departure location and destination.

[0005] Preferably, the order data with the same departure time as the user are counted, and the travel route with the most concentrated departure points in the order data is set as the first travel model; the order data with the same departure time as the user are counted, and the travel route with the most concentrated destinations in the order data is set as the second travel model; the order data with the same departure route as the user are counted, and the travel route with the most concentrated departure times in the order data is set as the third travel model.

[0006] Preferably, the statistical order data that is the same as the user's departure route is set as the third travel model with the travel route with the most concentrated departure time in the order data, including: statistical departure locations in the statistical order data that are less than a preset threshold from the user's departure location; statistical destinations in the statistical order data that are less than a preset threshold from the user's destination; and the departure route in the statistical order data connecting the statistical departure location and the statistical destination is the same route as the user's departure route.

[0007] Preferably, the neural network-based calculation of the similarity between the user's travel data and the first travel model, the second travel model and the third travel model includes: counting the first similarity between the user's departure point and the departure point in the order data based on the neural network, the first similarity is the similarity between the user's travel data and the first travel model; counting the second similarity between the user's destination and the departure point in the order data based on the neural network, the second similarity is the similarity between the user's travel data and the second travel model; counting the third similarity between the user's departure time and the departure point in the order data based on the neural network, the third similarity is the similarity between the user's travel data and the third travel model.

[0008] Preferably, the inputting of the master key and the selecting of one group of user's attribute lists based on similarity to group and calculate the keys of k attributes include:

[0009] If the user travel data has the greatest similarity to the first travel model, the user destination and / or the user departure time are grouped to calculate the keys of k attributes, and the departure location in the order data is selected as the user departure location.

[0010] Preferably, the inputting of the master key and the selecting of one group of user's attribute lists based on similarity to group and calculate the keys of k attributes include:

[0011] If the user travel data has the greatest similarity to the second travel model, the user departure location and / or the user departure time are grouped to calculate the keys of k attributes, and the destination in the order data is selected as the user destination.

[0012] Preferably, the input master key and the selection of one group of user's attribute list based on similarity to calculate the keys of k attributes include: if the user travel data has the greatest similarity to the third travel model, then selecting the user departure time to group and calculate the keys of k attributes, and selecting the departure place in the order data as the user's departure place and selecting the destination in the order data as the user's destination.

[0013] A method for protecting user information based on a dual algorithm of user identity encryption and attribute encryption is proposed. The results meet the needs of passenger personal information protection and show that in a big data environment, dual encryption of user identity and attributes can improve the ability to protect user personal information. At the same time, the attributes that need to be encrypted can be viewed based on the similarity of the first travel model, the second travel model, and the third travel model of the user's travel data, thereby reducing the complexity of encryption. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0015] Figure 1 It is a flow chart of the optimization method of the present invention based on the big data passenger information encryption algorithm;

[0016] Figure 2 This is a flow chart of another embodiment of the optimization method of the present invention based on the big data passenger information encryption algorithm.

[0017] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] 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.

[0019] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0020] In addition, the descriptions of the first, second, etc. in the present invention are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as the first and second may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0021] This application proposes an optimization method based on big data passenger information encryption algorithm, referring to Figure 1-2The method includes: step S1, calculating the similarity between the user travel data and the first travel model, the second travel model and the third travel model based on the neural network; step S2 generating an output system public key based on the user identity and saving the master key; inputting the master key, and grouping and calculating the keys of k attributes by selecting the attribute list of the user with at least one group selected based on the similarity; step S3; inputting the public key, message and access tree; inputting the public key, private key, ciphertext and incomplete access structure; if the user's attributes meet the entire access control structure, the root node secret value can be obtained and the plaintext can be successfully calculated through recursive calculation; wherein, the user's attribute list includes three attribute lists of departure time, departure place and destination.

[0022] A method for protecting user information based on a dual algorithm of user identity encryption and attribute encryption is proposed. The results meet the needs of passenger personal information protection and show that in a big data environment, dual encryption of user identity and attributes can improve the ability to protect user personal information. At the same time, the attributes that need to be encrypted can be viewed based on the similarity of the first travel model, the second travel model, and the third travel model of the user's travel data, thereby reducing the complexity of encryption.

[0023] Preferably, the order data with the same departure time as the user are counted, and the travel route with the most concentrated departure points in the order data is set as the first travel model; the order data with the same departure time as the user are counted, and the travel route with the most concentrated destinations in the order data is set as the second travel model; the order data with the same departure route as the user are counted, and the travel route with the most concentrated departure times in the order data is set as the third travel model.

[0024] Preferably, the order data that is the same as the user's departure route is counted, and the travel route with the most concentrated departure time in the order data is set as the third travel model, including: counting the departure locations in the order data that are less than a preset threshold from the user's departure location; counting the destinations in the order data that are less than a preset threshold from the user's destination; and the departure route in the order data that connects the statistical departure location and the statistical destination is the same route as the user's departure route.

[0025] Preferably, step S11, calculating the similarity between the user travel data and the first travel model, the second travel model and the third travel model based on the neural network includes: calculating the first similarity between the user's departure location and the departure location in the order data based on the neural network, the first similarity is the similarity between the user's travel data and the first travel model; step S12, calculating the second similarity between the user's destination and the departure location in the order data based on the neural network, the second similarity is the similarity between the user's travel data and the second travel model. Step S13, calculating the third similarity between the user's departure time and the departure location in the order data based on the neural network, the third similarity is the similarity between the user's travel data and the third travel model.

[0026] Preferably, in step 2, the master key is input, and the key of k attributes is calculated by selecting a group of user attribute lists based on similarity, including:

[0027] Step 21, if the user travel data has the greatest similarity to the first travel model, select the user destination and / or the user departure time to group and calculate the keys of k attributes, and select the departure place in the order data as the user departure place.

[0028] Preferably, in step 2, the master key is input, and the key of k attributes is calculated by selecting a group of user attribute lists based on similarity, including:

[0029] Step 22: If the user travel data has the greatest similarity to the second travel model, the user departure location and / or the user departure time are selected to group and calculate the keys of k attributes, and the destination in the order data is selected as the user destination.

[0030] Preferably, step 2, inputting the master key, selecting one group of user's attribute list based on similarity to group and calculate the keys of k attributes includes: step 23, if the similarity between the user travel data and the third travel model is the greatest, then selecting the user departure time to group and calculate the keys of k attributes, and selecting the departure place in the order data as the user's departure place and selecting the destination in the order data as the user's destination.

[0031] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. An optimization method based on big data passenger information encryption algorithm, characterized in that: The method includes: Collect the order data with the same departure time as the user, and set the travel route with the most concentrated departure locations in the order data as the first travel model; Collect the order data with the same departure time as the user, and set the travel route with the most concentrated destinations in the order data as the second travel model; The order data with the same departure route as the user is counted, and the travel route with the most concentrated departure time in the order data is set as the third travel model, specifically: Count the departure locations in the order data whose difference from the user's departure location is less than a preset threshold; Count the destinations in the order data whose differences from the user's destination are less than a preset threshold; The departure route connecting the statistical departure location and the statistical destination in the statistical order data is the same route as the user's departure route; The similarity between the user travel data and the first travel model, the second travel model and the third travel model is calculated based on the neural network, specifically: Counting a first similarity between the user's departure location and the departure location in the order data based on a neural network, where the first similarity is a similarity between the user's travel data and the first travel model; Counting a second similarity between the user's destination and the departure point in the order data based on a neural network, where the second similarity is a similarity between the user's travel data and the second travel model; Counting the third similarity between the departure time of the user and the departure place in the order data based on the neural network, where the third similarity is the similarity between the user travel data and the third travel model; Generate and output system public key based on user identity and save master key; Enter the master key, select the user's attribute list based on the least similarity and group them to calculate the key of k attributes, specifically: The user's attribute list includes three attribute lists: departure time, departure location, and destination; If the user travel data has the greatest similarity to the first travel model, the user destination and / or the user departure time are selected to group and calculate the keys of k attributes, and the departure location in the order data is selected as the user departure location; If the user travel data has the greatest similarity to the second travel model, the user departure location and / or the user departure time are selected to group and calculate the keys of k attributes, and the destination in the order data is selected as the user destination; If the user travel data has the greatest similarity to the third travel model, the user departure time is selected to group and calculate the key of k attributes, and the departure place in the order data is selected as the user's departure place and the destination in the order data is selected as the user's destination; Input the public key, message, and access tree; Input the public key, private key, ciphertext and incomplete access structure; if the user's attributes satisfy the entire access control structure, through recursive calculation, the root node secret value can be obtained and the plaintext can be successfully calculated.

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