5G network slice privacy protection method and system based on matching degree estimation

Abstract

The invention discloses a 5G network slice privacy protection method and system based on matching degree estimation, and the method comprises the steps: user equipment constructs slice request information containing a first feature vector according to a service demand, generates a random number sequence to encrypt the first slice feature vector, and transmits the encrypted slice request information to an access and mobility management function module; the access and mobility management function module analyzes the encrypted slice request information to obtain a random number sequence, and encrypts a second slice feature vector by using the analyzed random number sequence; the ciphertext is sent to the user equipment, and the user equipment analyzes the ciphertext and calculates the Euclidean distance between the second slice feature vector and the first slice feature vector; and selecting a scheme corresponding to the minimum Euclidean distance as a network slice privacy protection scheme. The corresponding feature vectors are encrypted through the random number sequence and the secret key, so that the privacy of user data is ensured while the provision of advanced services is ensured, and key technical support is provided for the development of a smart power grid.

Description

Technical Field

[0001] The present invention belongs to the field of network communications, and in particular relates to a 5G network slicing privacy protection method and system based on matching degree estimation. Background Art

[0002] 5G network slicing is a technology that allows the physical network to be virtualized into multiple independent networks to meet specific needs.

[0003] The technology that meets service needs provides unprecedented flexibility and efficiency for various application scenarios. This is particularly important in the context of smart grids, which rely on advanced communication technologies to monitor and manage the operation of the grid and ensure the reliability and efficiency of power supply. In this context, 5G network slicing not only provides fast, reliable data transmission and low-latency communication, but also helps to achieve effective management of a large number of distributed energy resources and smart devices.

[0004] However, with the widespread application of smart grids in modern society, how to provide efficient network services while ensuring data security and user privacy has become a pressing issue. Data communications in smart grids involve a large amount of sensitive information, including user energy consumption patterns, device status, and operating efficiency. If this information is leaked, it may lead to serious privacy and security issues.

[0005] The prior art CN202311691817.3A discloses a 5G network end-to-end slice orchestration method, which includes: receiving a network slice usage request sent by a user device, and performing network slicing on the network of the user device; obtaining key indicator data of at least one network slice, and obtaining corresponding resource demand information and business demand information based on the network slice type. This method lacks protection measures for individual user privacy, especially during the transmission of user requests and network slice feature information.

[0006] Prior art CN 202311511964.8A discloses a network slice determination method, apparatus, device and storage medium, the method comprising: receiving a first network slice type set and a first random number sent by a user device; wherein the first network slice type set is encrypted by a preset encryption algorithm and a first random number, and the first network slice type set includes at least one network slice type corresponding to at least one allowed network slice selection auxiliary information NSSAI of the user device; thereafter, according to the preset encryption algorithm and the first random number, the pre-configured network slice type set is encrypted to obtain a second network slice type set; further, according to the intersection of the first network slice type set and the second network slice type set, the target network slice assigned to the user device is determined. The method relies on traditional encryption methods and does not make full use of random number sequences to optimize privacy protection.

[0007] Prior art CN202311101147.5A discloses a method and system for allocating resources in an electric power network, the method comprising: training each user agent according to a training data set to obtain new slice selection strategy parameters for each user agent; and uploading the new slice selection strategy parameters for each user agent to a cloud server, so that the cloud server performs federation aggregation according to the received new slice selection strategy parameters to obtain global slice selection strategy parameters; each user agent performs user agent training according to the global slice selection strategy parameters, and after the user agent training is completed, based on a game theory algorithm and the global slice selection strategy parameters, selects the corresponding network slice to generate the best new network slice solution. This method uses federated learning for slice selection, resulting in slow processing and response speeds because global policy parameters need to be aggregated and updated, which is not suitable for network environments that require fast response.

[0008] Existing network slicing selection methods often focus on improving communication efficiency, but are insufficient in protecting user privacy. Therefore, a new network slicing privacy protection method is urgently needed, which can not only ensure the efficient operation of smart grids, but also effectively prevent the leakage of user information. While adapting to the needs of high efficiency and low latency in smart grids, it can also strengthen the protection of sensitive data, ensuring that user privacy is properly protected while providing advanced communication services. This is of great significance to the security and sustainable development of smart grids. Summary of the invention

[0009] In order to address the deficiencies in the prior art, the present invention provides a 5G network slicing privacy protection method based on matching degree estimation; by introducing a random number sequence to encrypt slice request information, user privacy protection is enhanced; at the same time, a matching degree estimation algorithm is used to select network slices, reducing the risk of user privacy leakage.

[0010] The present invention adopts the following technical solution.

[0011] A 5G network slice privacy protection method based on matching degree estimation, the method comprising:

[0012] Step 1: According to the service requirement, the user equipment constructs slice request information including the service requirement information, and the slice request information includes a first slice feature vector;

[0013] Step 2: Map the random number sequence generated by the user equipment to the corresponding first slice feature vector for encryption, and send the encrypted slice request information including the encrypted first slice feature vector to the access and mobility management function module;

[0014] Step 3: After receiving the encrypted slice request information sent by the user equipment, the access and mobility management function module parses the encrypted slice request information to obtain a first slice feature vector and a random number sequence, and maps the parsed random number sequence to a corresponding second slice feature vector for encryption; the second slice feature vector is a feature vector corresponding to a candidate slice scheme determined by the access and mobility management function module according to the received encrypted slice request information;

[0015] Step 4: After receiving the ciphertext of the second slice feature vector including the encrypted information returned from the access and mobility management function module, the user equipment parses the ciphertext and calculates the Euclidean distance between each vector in the second slice feature vector sequence contained in the ciphertext and the first slice feature vector;

[0016] Step 5: The user device selects the slicing scheme of the second slicing feature vector corresponding to the smallest Euclidean distance as the network slicing privacy protection scheme.

[0017] The present invention further includes the following preferred embodiments:

[0018] In step 1, constructing the slice request information including the service requirement information means that the user equipment outputs the required requested slice feature vector, i.e., the first slice feature vector sequence, based on the service requirement and taking the physical network as input:

[0019] {F UE}<-Req(FNR)

[0020] Among them, {F UE} is the first slice feature vector sequence, FNR is the physical network input, FNR = (slice1, slice i ,...,slice n ,DS}, DS represents the default network slice, slice i represents the i-th network slice;

[0021] The first slice feature vector F UE =(x1,...,x j ,…,x t ), where each element represents a slice feature, including rate, bandwidth, latency, and security level.

[0022] In step 2, the user equipment randomly generates a random number sequence C and a key r, and performs a random operation on the first slice feature vector F in the slice request information. UE Encrypt and output the encrypted first slice feature vector F UE ′:

[0023] {F UE′}<-EncryReq(C,r,{F UE})

[0024] Among them, {F UE ′} is the encrypted first slice feature vector sequence, {F UE} is the first slice feature vector sequence, -EncryReq is the encryption algorithm formula.

[0025] The user equipment encrypts the first slice feature vector by using an encryption algorithm, including: randomly generating t random numbers to form a random number set C = (c1, ..., c j ,...,c t ), and randomly generate a key r; the first slice feature vector F in the slice request information UE The characteristic information is encrypted as follows:

[0026] F′ UE ={x′ j =x j +rc j ,l≤j≤t}

[0027] Among them, x j is the jth element in the first slice feature vector, c j Generate the jth element in the random number sequence C for the user device, x′ j is the jth element in the encrypted first slice feature vector.

[0028] In step 3, after receiving the encrypted slice request information, the access and mobility management function module parses the encrypted first slice feature vector F UE ′ and a random number sequence C, and the random number sequence C obtained by parsing is used to encrypt the second slice feature vector sequence generated in the access and mobility management function module.

[0029] The second slice feature vector sequence generated in the access and mobility management function module is encrypted by the random number sequence C to obtain the ciphertext set M. The ciphertext set is sent to the user equipment through the 5G base station in the form of a slice response message. The ciphertext set is expressed as:

[0030] M=(M1,...,M l ,...,M t )

[0031] Among them, M l Represents the ciphertext generated after the lth second slice feature vector is encrypted.

[0032] The ciphertext M is generated by the random number sequence C and the corresponding encrypted first slice feature vector and second slice feature vector in the following way: l :

[0033] {M l}<-resp(F AMFl ,F UE ′,C)

[0034] Among them, F AMFl is the lth second slice eigenvector, F UE ′ is the encrypted first slice feature vector, C is a random number sequence; the ciphertext M l =(M l1 ,M l2 ,M l3 ),

[0035] Among them, x' lj represents the jth element of the first encrypted slice feature vector, y lj represents the jth element of the feature vector of the second slice of the lth slice, c j Represents the jth element in the random number sequence C.

[0036] In step 4, the user equipment obtains the Euclidean distance set d between the second slice feature vector and the first slice feature vector through the first slice feature vector, the second slice feature vector obtained by parsing the ciphertext, and the key. l :

[0037] D={d1,...,d l ,...,d n}

[0038] Among them, d l represents the Euclidean distance between the lth second slice feature vector and the corresponding first slice feature vector.

[0039] The Euclidean distance is generated by the first slice feature vector, the second slice feature vector and the key as follows:

[0040]

[0041] Among them, F UE is the first slice eigenvector, is the lth second slice feature vector in the second feature vector sequence corresponding to the first slice feature vector, and r is the key.

[0042] In step 5, the user equipment selects the minimum value d from the set D min =min{D} corresponds to the network slicing scheme.

[0043] At the same time, the present invention implements a 5G network slice privacy protection system based on matching degree estimation, the system comprising: user equipment, access and mobility management function module and 5G base station;

[0044] The user equipment is connected to the 5G base station and is used for sending slice request information, receiving forwarding response information from the 5G base station, and calculating user equipment side data;

[0045] The access and mobility management module is connected to the 5G base station and is used for sending slice request response information, receiving forwarding request information from the 5G base station, and calculating data on the access and mobility management module side;

[0046] The 5G base station is connected to the user equipment and the access and mobility management functional module, and is used to forward slice request information from the user equipment module and slice request response information from the access and mobility management functional module.

[0047] A terminal comprises a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of any one of the methods.

[0048] A computer-readable storage medium stores a computer program, which implements the steps of any one of the methods when executed by a processor.

[0049] The beneficial effect of the present invention lies in that, compared with the prior art, the present invention adopts matching degree estimation to select network slices, and selects the candidate slicing scheme corresponding to the minimum Euclidean distance by calculating the Euclidean distance between the first slicing feature vector contained in the slicing request information and the feature vector corresponding to the candidate slicing scheme as the network slicing scheme; at the same time, the corresponding feature vector is encrypted by a random number sequence and a key, which ensures the privacy of user data while ensuring the provision of advanced services, and provides key technical support for the development of smart grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of a 5G network slice privacy protection method based on matching degree estimation of the present invention;

[0051] Figure 2 It is a model diagram of a 5G network slice privacy protection system based on matching degree estimation of the present invention;

[0052] Figure 3 is based on Figure 2 A diagram of the network slice selection process of one embodiment. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention. Figure 1 The figure is a flow chart of a 5G network slice privacy protection method based on matching degree estimation in an embodiment of the present invention, and the method comprises the following steps:

[0054] Step 1: Based on business requirements, the user equipment constructs slice request information including business requirement information, and the slice request information includes a first slice feature vector.

[0055] In this embodiment, in step 1, constructing the slice request information including the service requirement information means that the user equipment outputs the required requested slice feature vector, i.e., the first slice feature vector sequence, based on the service requirement and taking the physical network as input:

[0056] {F UE}<-Req(FNR) (1)

[0057] Among them, {F UE} is the first slice feature vector sequence, FNR is the physical network input, FNR = (slice1, slice i ,...,slice n ,DS}, DS represents the default network slice, slice i represents the i-th network slice;

[0058] The first slice feature vector F UE =(x1,...,x j ,…,x t ), where each element represents a slice feature, including rate, bandwidth, latency, and security level.

[0059] Step 2: Map the random number sequence generated by the user equipment to the corresponding first slice feature vector for encryption, and send the encrypted slice request information containing the encrypted first slice feature vector to the access and mobility management function module.

[0060] In a preferred but non-limiting embodiment, in step 2, the user equipment randomly generates a random number sequence C and a key r, and performs a random operation on the first slice feature vector F in the slice request information. UE Encrypt and output the encrypted first slice feature vector F UE ′:

[0061] {F UE ′}<-EncryReq(C,r, {F UE}) (2)

[0062] Among them, {F UE ′} is the encrypted first slice feature vector sequence, {F UE} is the first slice feature vector sequence, -EncryReq is the encryption algorithm formula.

[0063] Furthermore, the user equipment encrypts the first slice feature vector by using an encryption algorithm, including the following contents:

[0064] The user equipment encrypts the first slice feature vector by using an encryption algorithm, including:

[0065] Randomly generate t random numbers to form a random number set C = (c1,...,c j ,...,c t ), and randomly generate a key r;

[0066] The first slice feature vector F in the slice request information UE The characteristic information is encrypted as follows:

[0067] F′ UE ={x′ j =x j +rc j ,l≤j≤t} (3)

[0068] Among them, x j is the jth element in the first slice feature vector, c j Generate the jth element in the random number sequence C for the user device, x′ j is the jth element in the encrypted first slice feature vector.

[0069] Step 3: After the access and mobility management function module receives the encrypted slice request information sent by the user equipment, it parses the encrypted slice request information to obtain a first slice feature vector and a random number sequence, and maps the parsed random number sequence to the corresponding second slice feature vector for encryption; the second slice feature vector is a feature vector corresponding to the candidate slice scheme determined by the access and mobility management function module based on the received encrypted slice request information.

[0070] In a preferred but non-limiting embodiment, in step 3, after receiving the encrypted slice request information, the access and mobility management function module parses the encrypted first slice feature vector F UE ′ and a random number sequence C, and the random number sequence C obtained by parsing is used to encrypt the second slice feature vector sequence generated in the access and mobility management function module.

[0071] Furthermore, the second slice feature vector sequence generated in the access and mobility management function module is encrypted by the random number sequence C to obtain the ciphertext set M, which is sent to the user equipment through the 5G base station in the form of a slice response message. The ciphertext set is expressed as:

[0072] M=(M1,...,M l ,...,M t ) (4)

[0073] Among them, M l Represents the ciphertext generated after the lth second slice feature vector is encrypted.

[0074] The ciphertext M is generated by the random number sequence C and the corresponding encrypted first slice feature vector and second slice feature vector in the following way: l :

[0075] {M l}<-resp(F AMFl ,F UE ′,C) (5)

[0076] Among them, F AMFl is the lth second slice eigenvector, F UE ′ is the encrypted first slice feature vector, and C is a random number sequence;

[0077] Ciphertext M l =(M l1 ,M l2 ,M l3 ) (6)

[0078]

[0079] Among them, x' lj represents the jth element of the first encrypted slice feature vector, y lj represents the jth element of the feature vector of the second slice of the lth slice, c j Represents the jth element in the random number sequence C.

[0080] Step 4: After receiving the ciphertext of the second slice feature vector including encrypted information returned from the access and mobility management functional module, the user equipment parses the ciphertext and calculates the Euclidean distance between each vector in the second slice feature vector sequence contained in the ciphertext and the first slice feature vector.

[0081] In a preferred but non-restrictive implementation, in step 4, after the user equipment receives the slice request message forwarded by the 5G base station, it parses the ciphertext M, and uses the following ExtraDis algorithm to calculate the Euclidean distance (i.e., d ) between the requested slice feature vector and the feature vector corresponding to each candidate slice scheme provided by the access and mobility management function module according to M and the key r. l ), for example, to calculate the feature vector corresponding to the requested slice (i.e., F UE ) and the feature vector corresponding to the candidate slicing scheme (i.e., F AMFl ) The Euclidean distance between the corresponding feature vectors; ExtraDis algorithm: {d l}<-ExtraDis(F UE ,F AMFl ,r) is run by the user device to calculate the feature vector of the requested slice (i.e., F UE ) and the feature vector corresponding to each candidate slicing scheme provided by the access and mobility management functional module (i.e., F AMFl ) and outputs the Euclidean distance (i.e., d l ), specifically, including:

[0082] (1) The user equipment receives the response request message forwarded by the 5G base station and parses the ciphertext M;

[0083] (2) Calculation of user devices separately E l2 =M l1 -rM l2 , E l =E l1 -2E l2 +M l3 ;

[0084] Among them, E li 、E l An intermediate variable for calculating Euclidean distance;

[0085] (3) User equipment calculation F UE and F AMFl ∈F AMF The Euclidean distance between

[0086] (4) and obtain a set of distance order D = {d1,...,d l ,...,d n}.

[0087] Step 5: The user device selects the slicing scheme of the second slicing feature vector corresponding to the smallest Euclidean distance as the network slicing privacy protection scheme.

[0088] In a preferred but non-limiting implementation, in step 5, the user equipment performs the above-mentioned operation of extracting the Euclidean distance on all candidate slicing schemes provided by the access and mobility management function module to obtain the Euclidean distances between the feature vectors corresponding to each candidate slicing scheme, and the set of these distances is D. Finally, the user equipment selects the d with the smallest Euclidean distance from the set D. min =min{D}, and sends the corresponding slicing scheme number to the access and mobility management functional module as the slicing scheme selected by itself.

[0089] In a preferred but non-limiting embodiment, after step 5, the data before and after encryption are analyzed and compared to verify the effectiveness of the random number sequence in protecting the information of the first slice feature vector and the second slice feature vector; the specific verification method is as follows:

[0090] The Euclidean distance is defined as follows:

[0091]

[0092] Where X=(x1,...,x j ,...,x n ), Y=(y1,...y,...,y n ) are two vectors; in the present invention, the above formula (1) is used to calculate the difference between the feature vectors of the two slices as an estimate of the matching degree between the two slices, such as the EncryReq algorithm as shown in formula (11):

[0093] x' j =x j +rc j ,1≤j≤t (11)

[0094] x' j Considering the Resp algorithm, we get:

[0095]

[0096] M l1 and M l2 Substituting into the ExtraDis algorithm, we get:

[0097]

[0098] E1, E2 and M l3 Substitute it into the ExtraDis algorithm and we get:

[0099]

[0100] This is the Euclidean distance between two vectors defined above, and its correctness has been verified.

[0101] In a preferred but non-limiting embodiment, after step 5, the security of the network slice privacy protection method is analyzed by the fine granularity of the network slice, which is specifically described as follows:

[0102] The privacy requirement is to prevent the leakage of slice feature information during the slice selection process. The attacker is a malicious network node between the user equipment and the access and mobility management function module. The slice response information consists of three parts, namely, M l1 , M l2 , M l3 , all three parts contain F AMFl , therefore, the following equation must be solved when cracking:

[0103]

[0104] In the above formula, the slice response information sent from the access and mobility management function module to the user equipment includes M l1 、M l2 、M l3 , may be intercepted by the opponent; similarly, the opponent can intercept x j ′,c j And where j = 1, ..., t; Therefore, for the adversary, the above three equations have t unknown variables; Slicing in a fine-grained network, t is unknown and large enough, and these three equations have infinite solutions; that is, F AMFl The privacy of is ensured by the above three equations; Table I is the definition of symbols in the implementation process of the present invention.

[0105] Table I Symbols used in this paper

[0106]

[0107] In one embodiment of the present invention, after step 5, the computational efficiency and communication cost of the network slice privacy protection method are evaluated based on the dimension of the feature vector corresponding to the network slice and the number of network slice candidates provided by the access and mobility management functional module.

[0108] Specifically, the Euclidean distance calculation technology is used to measure the similarity between two slices. The computational efficiency and communication cost of the network slice privacy protection method of the present invention are calculated by mainly calculating the computational cost consumed by the method. The experiment is carried out on a hardware platform using an Intel i5-8300H processor and a Windows 10 operating system. The rand function in the c++cstdio library is used to generate random numbers. Table II shows the average time cost consumed by the CPU instructions running the method of the present invention on the hardware platform.

[0109] Table II Time cost of CPU instructions (unit: ns)

[0110] MOV 2 RAND 66.4 ADD 2 MUL 2

[0111] Table III shows the CPU instructions required for the request phase, response phase and slice selection phase in the method described in the embodiment of the present invention and the corresponding time of each phase.

[0112] Table III Number of CPU instructions in the slice privacy protection scheme based on matching degree estimation of the present invention

[0113]

[0114] The time cost in Table II is brought into Table III to obtain Table IV, which shows the time cost of the algorithms used in each stage of the method described in the embodiment of the present invention. The following conclusions can be drawn from Table IV:

[0115] 1) The Req algorithm is only related to the thickness of the slice granularity, that is, the computational cost of the Req algorithm is linearly related to the dimension of the slice feature vector;

[0116] 2) The computational costs of the EncryReq algorithm, the Resp algorithm, and the ExtraDis algorithm are related to the dimensions of the slice feature vectors and the number of candidate solutions provided by the access and mobility management functional module; generally speaking, the dimensions of the slice feature vectors and the number of candidate slice solutions provided by the access and mobility management functional module are at a constant level, and the computational complexity of the final algorithm is at the ns level, so the slice selection scheme based on matching degree estimation of the present invention is quite lightweight.

[0117] The communication overhead is defined as the total length of the messages transmitted in the slice selection scheme based on matching degree estimation of the present invention; there are two messages in the slice selection scheme based on matching degree estimation of the present invention; in the request phase, the user equipment sends the encrypted slice feature vector and random number set C to the 5G base station through the slice request message; in the response phase, the access and mobility management function module sends M to the 5G base station l =(M l1 ,M l2 ,M l3); In the slice privacy protection scheme based on matching degree estimation of the present invention, all variables are 32-bit integers. When the slice feature vector dimension t and the number of candidate solutions n provided by the access and mobility management functional module are 16 and 12 respectively, the length of the slice request message is 16*32+16=528 bits; similarly, we can calculate that the length of the response message is 16*32*3*12=18432bit; thus, in a slice selection process, the total length of the message transmitted in the slice selection scheme based on matching degree estimation of the present invention is 528+18432=18960bit≈19kb; from the above analysis, it can be seen that the total length of the message transmitted in the slice privacy protection scheme based on matching degree estimation of the present invention is related to the feature vector dimension of the slice and the number of candidate scenarios provided by the access and mobility management functional module; therefore, slice selection using the slice privacy protection scheme based on matching degree estimation of the present invention requires a trade-off between the granularity of the slice and the number of candidates and the computational and communication costs.

[0118] In a specific embodiment of the present invention, the determination of the candidate scene is affected by the following factors:

[0119] 1) Resource limitations: computing resources, storage resources, and communication resources. These limitations may affect the scalability and efficiency of the system.

[0120] 2) Algorithm complexity: The increase in the number of candidate scenarios may increase the complexity and computational cost of the algorithm; therefore, it is necessary to weigh the relationship between algorithm complexity and performance to ensure that the algorithm is feasible and effective in practical applications;

[0121] 3) Slice selection accuracy: Increasing the number of candidate scenes may improve the accuracy of slice selection, but it will also increase communication and computing costs; therefore, the number of candidate scenes needs to be determined based on the accuracy requirements of slice selection and the actual situation of the system.

[0122] Figure 2 It is a model diagram of a 5G network slice privacy protection system for matching degree estimation according to an exemplary embodiment, the system comprising: a user equipment module, an access and mobility management function module and a 5G base station; the user equipment module is connected to the 5G base station, and is used to send slice request information and receive forwarding response information from the 5G base station;

[0123] The access and mobility management module is connected to the 5G base station and is used to send slice request response information and receive forwarding request information from the 5G base station;

[0124] The 5G base station is connected to the user equipment module and the access and mobility management function module, and is used to forward slice request information from the user equipment module and slice request response information from the access and mobility management function module.

[0125] like Figure 3 Shown is based on Figure 2 A diagram of a network slice selection process of an embodiment, wherein the network slice selection process comprises the following steps:

[0126] 1) The user equipment generates and sends an encrypted message containing the requested slice characteristics according to its service requirements;

[0127] 2) The 5G base station acts as a relay, receiving the request from the user equipment and forwarding it to the access and mobility management functional module;

[0128] 3) The access and mobility management function module processes the user equipment's request, selects the appropriate slicing scheme, and sends an encrypted response containing the slicing scheme information back to the 5G base station;

[0129] 4) The 5G base station acts as a relay again and forwards the encrypted response of the access and mobility management function module back to the user equipment;

[0130] 5) The user equipment receives the encrypted response forwarded by the access and mobility management functional module via the 5G base station, decrypts it, and obtains information about each candidate slicing scheme from it; based on the decrypted information, the user equipment evaluates the degree of match between the characteristics of each candidate slicing scheme and its service requirements, and thus selects the optimal slicing scheme.

[0131] At the same time, the present invention implements a 5G network slice privacy protection system based on matching degree estimation, including user equipment, access and mobility management function module and 5G base station:

[0132] The user equipment is connected to the 5G base station and is used for sending slice request information, receiving forwarding response information from the 5G base station, and calculating user equipment side data;

[0133] The access and mobility management module is connected to the 5G base station and is used for sending slice request response information, receiving forwarding request information from the 5G base station, and calculating data on the access and mobility management module side;

[0134] The 5G base station is connected to the user equipment and the access and mobility management functional module, and is used to forward slice request information from the user equipment module and slice request response information from the access and mobility management functional module.

[0135] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0136] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0137] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0138] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0139] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A 5G network slice privacy protection method based on matching degree estimation, characterized in that The method comprises the following steps: Step 1: According to the service needs, the user equipment constructs slice request information including business demand information, and the slice request information includes the first slice feature vector; Step 2: Map the random number sequence generated by the user equipment to the corresponding first slice feature vector for encryption, and send the encrypted slice request information including the encrypted first slice feature vector to the access and mobility management function module; Step 3: After receiving the encrypted slice request information sent by the user equipment, the access and mobility management function module parses the encrypted slice request information to obtain a first slice feature vector and a random number sequence, and maps the parsed random number sequence to a corresponding second slice feature vector for encryption; the second slice feature vector is a feature vector corresponding to a candidate slice scheme determined by the access and mobility management function module according to the received encrypted slice request information; Step 4: After receiving the ciphertext of the second slice feature vector including the encrypted information returned from the access and mobility management function module, the user equipment parses the ciphertext and calculates the Euclidean distance between each vector in the second slice feature vector sequence contained in the ciphertext and the first slice feature vector; Step 5: The user equipment selects the slice scheme of the second slice feature vector corresponding to the smallest Euclidean distance as the network slice privacy protection scheme.

2. The 5G network slice privacy protection method based on matching degree estimation according to claim 1, characterized in that: In step 1, constructing the slice request information including the service requirement information means that the user equipment outputs the required requested slice feature vector, i.e., the first slice feature vector sequence, based on the service requirement and taking the physical network as input: {F UE }<-Req(FNR) Among them, {F UE } is the first slice feature vector sequence, FNR is the physical network input, FNR = (slice1, slice i ,...,slice n ,DS}, DS represents the default network slice, slice i represents the i-th network slice; The first slice feature vector F UE =(x1,...,x j ,...,x t ), wherein each element represents a slice feature, which includes rate, bandwidth, delay, security level.

3. The 5G network slice privacy protection method based on matching degree estimation according to claim 2, characterized in that: In step 2, the user equipment randomly generates a random number sequence C and a key r to the first slice feature vector F in the slice request information UE Perform encryption and output the encrypted first slice feature vector F UE ′: {F UE ′}<-EncryReq(C,r,{F UE }) Among them, {F UE ′} is the encrypted first slice feature vector sequence, {F UE } is the first slice feature vector sequence, and -EncryReq is the encryption algorithm formula.

4. The 5G network slice privacy protection method based on matching degree estimation according to claim 3, characterized in that: The user equipment encrypts the first slice feature vector through an encryption algorithm including: Randomly generate t random numbers to form a random number set C=(c1,...,c j ,...,c t ), and randomly generate a key r; The first slice feature vector F in the slice request information UE The characteristic information is encrypted as follows: F′ UE ={x′ j =x j +rc j ,l≤j≤t} Among them, x j It is the jth element in the first slice feature vector, c j Randomly generates the jth element in the random number sequence C for the user equipment, x' j It is the jth element in the encrypted first slice feature vector.

5. The 5G network slice privacy protection method based on matching degree estimation according to claim 1 or 4, characterized in that: In step 3, after receiving the encrypted slice request information with the mobility management function module, the encrypted first slice feature vector F is parsed out UE ′ and random number sequence C, the random number sequence C obtained by analyzing encrypts the access and second slice feature vector sequence generated in the mobility management function module.

6. The 5G network slice privacy protection method based on matching degree estimation according to claim 5, characterized in that: The second slice feature vector sequence generated in the access and mobility management function module is encrypted by the random number sequence C to obtain the ciphertext set M. The ciphertext set is sent to the user equipment through the 5G base station in the form of a slice response message. The ciphertext set is expressed as: M=(M1,...,M l ,...,M t ) Among them, M l It represents the ciphertext generated after the cryptographic process of the 1st second slice feature vector.

7. The 5G network slice privacy protection method based on matching degree estimation according to claim 6, characterized in that: The ciphertext M is generated in the following way through the random number sequence C, corresponding to the encrypted first slice feature vector and the second slice feature vector l : {M l }<-resp(F AMFl ,F UE ′,C) Among them, F is the lth second slice feature vector, F UE ' is the encrypted first slice feature vector AMF1 quantity, C is a random number sequence; Ciphertext M l =(M l1 ,M l2 ,M l3 ); Among them, x′ lj The jth element representing the first slice feature vector after the lth encrypted, y lj The jth element representing the lth second slice feature vector, c j Represents the jth element in the random number sequence C.

8. The 5G network slice privacy protection method based on matching degree estimation according to claim 7, characterized in that: In step 4, the user equipment obtains the Euclidean distance set d between the second slice feature vector and the first slice feature vector through the first slice feature vector, the second slice feature vector obtained by parsing the ciphertext, and the key. l : D={d1,...,d l ,...,d n } Among them, d l It represents the Euclidean distance between the 1st second slice feature vector and the corresponding first slice feature vector.

9. The 5G network slice privacy protection method based on matching degree estimation according to claim 8, characterized in that: Euclidean distance is generated by the first slice feature vector, the second slice feature vector and the key in the following manner: {d l }<-ExtraDis(F UE ,F AMFl ,r) Among them, F UE is the first slice eigenvector, F AMFl The 1st second slice feature vector in the second sequence of eigenvectors corresponding to the first slice feature vector, r is the key.

10. The 5G network slice privacy protection method based on matching degree estimation according to claim 8 or 9, characterized in that: In step 5, the user device selects the minimum value d from the set D min =min{D} corresponds to the network slicing scheme.

11. A 5G network slice privacy protection system based on matching degree estimation for implementing the method described in any one of claims 1 to 10, comprising a user device, an access and mobility management function module and a 5G base station, characterized in that: The user equipment is connected to a 5G base station for sending slice request information, receiving forwarding response information from the 5G base station, and calculating data on the user equipment side; The access and mobility management module is connected to the 5G base station and is used for sending slice request response information, receiving forwarding request information from the 5G base station, and calculating data on the access and mobility management module side; The 5G base station is connected to the user equipment and the access and mobility management functional module, and is used to forward slice request information from the user equipment module and slice request response information from the access and mobility management functional module.

12. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-10.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: The step of implementing the method of any one of claims 1-10 when executed by the processor.

Images