Data processing method, device, apparatus, and computer storage medium

Abstract

The application discloses a data processing method, device and equipment and a computer storage medium. The method comprises the following steps: when the first data packet sent by a preset node reaches a first preset number, and the preset node sends a second data packet based on a preset probability, a target pseudo sink node is randomly determined from a pseudo sink node set, the pseudo sink node set comprises a plurality of pseudo sink nodes, and the preset probability is a preset probability of the preset node sending the second data packet; a target transmission path from the preset node to the target pseudo sink node is determined according to a preset path selection rule; and the second preset number of second data packets are sent from the preset node to the target pseudo sink node according to the target transmission path. The embodiment of the application can reduce the risk of position privacy leakage of the sink node.

Description

Technical Field

[0001] This application belongs to the field of data processing technology, and in particular relates to a data processing method, apparatus, device and computer storage medium. Background Technology

[0002] Current research on location privacy in wireless sensor networks mainly focuses on protecting the location privacy of sink nodes and source nodes. Sink nodes are the central hub of wireless sensor networks, serving as gateways connecting the sensor network to other networks. Wireless sensor networks must interact with other networks through sink nodes.

[0003] If the sink node is captured or destroyed by a malicious attacker, the entire sensor network will be unable to interact with other networks, or the information of the entire sensor network will be leaked, putting the network and the areas covered and monitored by the network at risk. Summary of the Invention

[0004] This application provides a data processing method, apparatus, device, and computer storage medium to reduce the risk of location privacy leakage of sink nodes.

[0005] In a first aspect, embodiments of this application provide a data processing method, including:

[0006] When the first data packet sent by the preset node reaches the first preset number, and the preset node is determined to send the second data packet based on the preset probability, a target pseudo-convergence node is randomly selected from the pseudo-convergence node set. The pseudo-convergence node set includes multiple pseudo-convergence nodes, and the preset probability is the probability of the preset node sending the second data packet.

[0007] The target transmission path from the preset node to the target pseudo-convergence node is determined according to the preset path selection rules;

[0008] According to the target transmission path, a second preset number of second data packets are sent from the preset node to the target pseudo-convergence node.

[0009] In an alternative implementation, before determining the target pseudo-convergence node from the set of pseudo-convergence nodes when the preset node sends the second data packet, the method further includes:

[0010] Obtain the neighbor node information of multiple nodes in the target network. Multiple nodes include non-convergence nodes and convergence nodes. The neighbor node information of each node includes the identification information of each other node in the target network and the hop count between each other node and the node. Other nodes are nodes in the target network other than the node.

[0011] Based on the neighbor node information of multiple nodes, determine the average number of hops between each node and each other node;

[0012] Calculate the similarity weight between each node and each other node based on the average number of hops from each node to each other node, and the average number of hops from the convergence node to each other node.

[0013] The probability transition matrix is ​​determined based on the similarity weights between each node and all other nodes;

[0014] Substitute the preset node state matrix into the probability transition matrix to obtain the target probability transition matrix. The preset node state matrix includes node state elements corresponding to the convergent nodes and node state elements corresponding to the non-convergent nodes.

[0015] The target probability transition matrix is ​​regularized according to the preset regularization adjustment parameters to obtain the similarity probability values ​​between each non-converging node and the convergent node.

[0016] The n non-converging nodes with the highest similarity probability values ​​are identified as pseudo-converging nodes, resulting in a set of pseudo-converging nodes, where n is a positive integer;

[0017] Non-convergence nodes, excluding pseudo-convergence nodes, are designated as preset nodes.

[0018] In one optional implementation, before determining the target transmission path from the preset node to the target pseudo-convergence node according to a preset path selection rule, the method further includes performing the following steps for each preset node:

[0019] For each pseudo-convergence node, the target other preset nodes are divided into the pseudo-sub-node set corresponding to the preset node and the pseudo-convergence node. The target other preset nodes are other preset nodes whose hop count to the pseudo-convergence node is less than the hop count from the preset node to the pseudo-convergence node.

[0020] In one optional implementation, the preset path selection rules include:

[0021] The first target pseudo-child node is determined from the first target pseudo-child node set, wherein the first target pseudo-child node set is the pseudo-preset node set corresponding to the target pseudo-convergence node;

[0022] The second target pseudo-child node is determined from the set of pseudo-child nodes of the second target, wherein the set of pseudo-child nodes of the second target is the set of pseudo-child nodes corresponding to the pseudo-first target pseudo-child nodes and the pseudo-convergence node of the target;

[0023] Use the second target pseudo-child node as the new first target pseudo-child node, and return to determine the second target pseudo-child node from the set of second target pseudo-child nodes until the set of second target pseudo-child nodes is empty;

[0024] The path from the preset node through each first target pseudo-sub-node and second target pseudo-sub-node to the target pseudo-convergence node is determined as the target transmission path.

[0025] In one alternative implementation, randomly determining the target pseudo-convergence node from the set of pseudo-convergence nodes includes:

[0026] A target pseudo-convergence node is randomly determined from at least one first pseudo-convergence node in the set of pseudo-convergence nodes, including pseudo-convergence nodes that are not the recipients of the second data packets;

[0027] After determining the target pseudo-convergence node from the set of pseudo-convergence nodes, the method includes:

[0028] In the set of pseudo-convergence nodes, the target pseudo-convergence node is marked as the second pseudo-convergence node, which includes pseudo-convergence nodes that have already acted as the receiver of the second data packet.

[0029] In one optional implementation, determining the first target pseudo-child node from the first target pseudo-child node set includes:

[0030] The first target pseudo-subnode is determined from at least one first pseudo-subnode in the first target pseudo-subnode set, wherein the first pseudo-subnode includes a pseudo-subnode that is not a second data packet forwarder that is a preset node;

[0031] After determining the first target pseudo-child node from the first target pseudo-child node set, the method also includes:

[0032] In the first target pseudo-child node set, the first target pseudo-child node is marked as the second pseudo-child node. The second pseudo-child node includes the pseudo-child node that has been used as the second data packet forwarder of the preset node.

[0033] In one optional implementation, determining the first target pseudo-child node from the first target pseudo-child node set includes:

[0034] Obtain the node energy of each pseudo-child node in the first target pseudo-child node set for data processing;

[0035] Among all the pseudo-child nodes in the set of pseudo-child nodes for the first target of data processing, the pseudo-child node with the highest node energy is determined as the pseudo-child node for the first target of data processing.

[0036] In an optional implementation, before determining the target pseudo-convergence node from the set of pseudo-convergence nodes, when the first data packet sent by the preset node reaches a first preset number and it is determined based on a preset probability that the preset node will send a second data packet, the method further includes:

[0037] Calculate the preset probability corresponding to the second preset quantity based on the preset correspondence between the second data packet quantity and the probability.

[0038] Secondly, embodiments of this application provide a data processing apparatus, including:

[0039] The determination module is used to randomly determine a target pseudo-convergence node from a set of pseudo-convergence nodes when the first data packet sent by the preset node reaches a first preset number and the preset node is determined to send a second data packet based on a preset probability. The set of pseudo-convergence nodes includes multiple pseudo-convergence nodes, and the preset probability is the probability of the preset node sending a second data packet that is set in advance.

[0040] The determination module is also used to determine the target transmission path from the preset node to the target pseudo-convergence node according to the preset path selection rules;

[0041] The sending module is used to send a second preset number of second data packets from a preset node to a target pseudo-convergence node according to the target transmission path.

[0042] Thirdly, embodiments of this application provide an electronic device, the device including: a processor and a memory storing computer program instructions;

[0043] When the processor executes computer program instructions, it implements a data processing method as described in any optional embodiment of the first aspect of this application.

[0044] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement a data processing method as described in any optional embodiment of the first aspect of this application.

[0045] Fifthly, embodiments of this application provide a computer program product in which instructions, when executed by a processor of an electronic device, cause the electronic device to perform a data processing method as described in any optional embodiment of the first aspect of this application.

[0046] The data processing method, apparatus, device, computer storage medium, and computer program product provided in this application embodiment, when the first data packet sent by a preset node reaches a first preset number, and the preset node is determined to send a second data packet based on a preset probability, randomly selects a target pseudo-convergence node from a set of pseudo-convergence nodes. The preset probability is a pre-set probability that the preset node will send the second data packet. The set of pseudo-convergence nodes includes multiple pseudo-convergence nodes. Randomly selecting a target pseudo-convergence node from multiple pseudo-convergence nodes can increase the uncertainty of the fake data packet receiver, thereby reducing the regularity of fake data packet sending. Then, a target transmission path from the preset node to the target pseudo-convergence node can be determined according to a preset path selection rule. According to the target transmission path, a second preset number of second data packets are sent from the preset node to the target pseudo-convergence node. In this way, by designing a combination of multiple pseudo-sink nodes and fake data packet streams, the tracker can be deceived, thereby reducing the risk of leakage of the location privacy of the real sink node and helping to protect the location privacy of the real sink node. Attached Figure Description

[0047] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a schematic flowchart of a data processing method provided in one embodiment of this application;

[0049] Figure 2 This is a schematic diagram of the structure of a data processing apparatus provided in another embodiment of this application;

[0050] Figure 3 This is a schematic diagram of the structure of an electronic device provided in another embodiment of this application. Detailed Implementation

[0051] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0052] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0053] In this article, a sink node can be represented as a network node that acts as a packet receiver.

[0054] In this article, hop count refers to the number of intermediate nodes (such as routers, switches, etc.) that must be traversed when two network nodes communicate. Specifically, it represents the total number of hops a data packet or signal passes through from a source node to a destination node.

[0055] As described in the background section, if the sink node is captured or destroyed by a malicious attacker, the entire sensor network will be unable to interact with other networks, or the information of the entire sensor network will be leaked, putting the network and the areas covered and monitored by the network at risk.

[0056] Current research on location privacy in wireless sensor networks largely focuses on protecting the location privacy of sink and source nodes. Regarding sink node location privacy, various strategies have been proposed, such as generating fake data packets to confuse traffic analysis attackers; injecting fake packets into multipath wireless sensor networks to prolong the time it takes for attackers to track down the sink node, thus extending the safe transmission time of data packets; using the LPR algorithm to achieve directional dynamic transmission of data packets, enabling sink nodes to resist hop-by-hop tracking and extending the safe transmission time; and concealing the sink node address, discovering routes only through broadcast messages, among others.

[0057] The related technology involves a method for protecting the location privacy of sink nodes in wireless sensor networks. This method introduces the concept of pseudo-sink nodes and proposes a method to determine the location of pseudo-sink nodes by selecting the routing path with the highest hop count. However, this method has three shortcomings: first, the hop count between the pseudo-sink node and the sink node is fixed and cannot be modified; second, fake data packets are generated based on the total number of child nodes of the sink node's neighboring nodes and the number of child nodes of nearby nodes. Since these two values ​​are fixed, the fake data packets have a strong regularity and are easily identified as fake data packets; third, there is insufficient analysis on the balance between network energy consumption and network security.

[0058] Related technologies also involve another method for protecting the location privacy of sink nodes in wireless sensor networks. This method proposes to identify pseudo-sink nodes based on network traffic. However, this method has two shortcomings: first, it generates fake data packets based on the total number of child nodes of the sink node's neighboring nodes and the number of child nodes of nearby nodes. Since these two values ​​are fixed, the fake data packets have a strong regularity and are easily identified as fake data packets; second, it provides less analysis of the balance between network energy consumption and network security.

[0059] Related technologies also involve a method and system for optimizing the lifetime of chain-type wireless sensor networks. This scheme proposes setting a sink node sequence and cross-linking the sequence to form a new node sequence based on the ratio of node energy to load. However, this scheme does not analyze network security aspects.

[0060] In summary, the solutions involved in the relevant technologies still have shortcomings in protecting the location privacy of Sink nodes, and the protection of the location privacy of Sink nodes still needs to be improved.

[0061] To address the problems in the prior art, embodiments of this application provide a data processing method, apparatus, device, and computer storage medium.

[0062] The data processing method provided in this application embodiment will be described below with reference to the accompanying drawings, through specific embodiments and application scenarios. The data processing method provided in this application embodiment can be executed by a data processing device, or a portion of the data processing device used to execute the data processing method. This application embodiment uses the execution of the data processing method by a data processing device as an example to describe the data processing method provided in this application embodiment in detail.

[0063] The following is in conjunction with the appendix Figure 1 The data processing method provided in the embodiments of this application will be described in detail.

[0064] Figure 1 A flowchart illustrating a data processing method provided in one embodiment of this application is shown. Figure 1 As shown, the method includes the following steps S110 to S130.

[0065] S110, when the first data packet sent by the preset node reaches the first preset number, and the preset node is determined to send the second data packet based on the preset probability, a target pseudo-convergence node is randomly selected from the pseudo-convergence node set. The pseudo-convergence node set includes multiple pseudo-convergence nodes, and the preset probability is the probability of the preset node sending the second data packet.

[0066] In step S110, the first data packet can be a real data packet, and the second data packet can be a fake data packet (also called a pseudo data packet). According to step S110, after sending a certain number of real data packets, the preset node can determine whether to send fake data packets according to a preset probability. If fake data packets are sent, a pseudo-aggregation node is randomly selected from the set of pseudo-aggregation nodes as the target pseudo-aggregation node, and a certain number of fake data packets are sent to it.

[0067] S120, determine the target transmission path from the preset node to the target pseudo-convergence node according to the preset path selection rules.

[0068] In step S120, the preset path selection rules can be adjusted according to actual needs. For example, the target transmission path can be selected based on the shortest path principle, or it can be selected according to a certain degree of randomness, or it can be selected according to the total energy of the nodes to be consumed by the path, etc., which are not limited here.

[0069] S130, according to the target transmission path, send a second preset number of second data packets from the preset node to the target pseudo-convergence node.

[0070] The data processing method provided in this application, when a preset node sends a first preset number of data packets and a preset probability determines that the preset node will send a second data packet, randomly selects a target pseudo-convergence node from a set of pseudo-convergence nodes. The preset probability is a pre-set probability that the preset node will send the second data packet. This set of pseudo-convergence nodes includes multiple pseudo-convergence nodes. Randomly selecting a target pseudo-convergence node from these multiple pseudo-convergence nodes increases the uncertainty of the fake data packet receiver, thereby reducing the regularity of fake data packet transmission. Then, a target transmission path from the preset node to the target pseudo-convergence node can be determined according to a preset path selection rule. Following the target transmission path, a second preset number of second data packets are sent from the preset node to the target pseudo-convergence node. In this way, by designing a combination of multiple pseudo-sink nodes and fake data packet streams, the method can deceive trackers, thereby reducing the risk of location privacy leakage of the real sink node and protecting the location privacy of the real sink node.

[0071] In one embodiment, before determining the target pseudo-convergence node from the set of pseudo-convergence nodes when the preset node sends the second data packet, the method may further include the following steps S210 to S280.

[0072] S210, Obtain the neighbor node information of multiple nodes in the target network. The multiple nodes include non-convergence nodes and convergence nodes. The neighbor node information of each node includes the identification information of each other node in the target network and the hop count between each other node and the node. The other nodes are nodes in the target network other than the node in question.

[0073] In step S210, neighbor node information can be obtained in various ways. As an example, during network initialization, each node can send data packets carrying its own node location information to each other in the network, and establish a neighbor node table by obtaining data packets from other nodes. This neighbor node table can contain the aforementioned neighbor node information.

[0074] For example, in a wireless sensor network with N nodes, during network initialization, each node broadcasts an acknowledgment data packet carrying its own location information throughout the network. When other nodes receive the acknowledgment data packet, they first determine whether it is the first time the node has received it. If not, the packet is automatically discarded. If it is the first time, the node records its location information, embeds its own location information into the data packet, and then broadcasts the repackaged data packet. Other nodes similarly decide whether to discard the packet based on whether it is the first time they have received the repackaged data packet. After the broadcast acknowledgment information is sent and received, the entire network completes the initialization of all nodes. Each node establishes a neighbor node table, which includes the total number of its neighbors, the identity document (ID) number of each neighbor node, and the hop count from the node to its neighbors.

[0075] S220: Based on the neighbor node information of multiple nodes, determine the average number of hops between each node and each other node.

[0076] S230, calculate the similarity weight between each node and each other node based on the average number of hops from each node to each other node and the average number of hops from the aggregation node to each other node.

[0077] For example, the average number of hops from node i to other nodes is x. i The average number of hops from node j to nodes other than itself is x. j The similarity weight between nodes i and j can be calculated using Equation 1 below.

[0078]

[0079] In Equation 1, w ij It can represent the similarity weight between node i and node j, where σ is the weight adjustment parameter.

[0080] S240, determine the probability transition matrix based on the similarity weights between each node and all other nodes.

[0081] In step S240, a probability transition matrix P can be constructed based on similarity weights. Since there are N nodes in the wireless sensor network, P is an N×N matrix, where any element can represent the transition probability between two nodes. Any element can be calculated using Equation 2 below.

[0082]

[0083] S250, Substitute the preset node state matrix into the probability transition matrix to obtain the target probability transition matrix. The preset node state matrix includes node state elements corresponding to the sinking nodes and node state elements corresponding to the non-sinking nodes.

[0084] S260, the target probability transition matrix is ​​regularized according to the preset regularization adjustment parameters to obtain the similarity probability values ​​between each non-converging node and the convergent node.

[0085] The following provides an example of steps S250 and S260. For instance, steps S250 to S260 can use a label spreading algorithm to obtain the similarity probability values ​​between each non-converging node and the convergent node.

[0086] Specifically, in step S250, the preset node state matrix can be denoted as... Where Y L For labeled nodes, represent the node state element corresponding to the sink node; Y U For unlabeled nodes, it represents the node state element corresponding to the non-converging node.

[0087] Substituting the node state matrix into the probability transition matrix, we can obtain P LL P represents the transition from a labeled state to a labeled state with a probability of 1, i.e., the identity matrix I; LU This represents a zero matrix representing the transition from a labeled state to an unlabeled state with a probability of 0. After simplification, we get... After algorithm iteration, we can obtain:

[0088]

[0089] Where t can represent the number of iterations, that is, the iteration until the algorithm reaches the convergence condition, which can be when the change of the label matrix is ​​less than a certain threshold or when the maximum number of iterations is reached.

[0090] Next, F can be calculated. t+1 =F t P is used to obtain the target probability transition matrix. Through regularization calculation as shown in Equation 3, labels are applied to all nodes.

[0091] F t+1 =αF t+1 (1-α)F t+1 Formula 3

[0092] In Equation 3, α is an adjustment parameter, α∈[0,1], for example, it can be 0.2.

[0093] After labeling all nodes, the similarity probability values ​​between each non-converging node and the convergent node can be obtained.

[0094] S270, the top n non-converging nodes with the highest similarity probability values ​​are identified as pseudo-converging nodes, thus obtaining a set of pseudo-converging nodes, where n is a positive integer.

[0095] In step S270, the value of n can be set as needed. For example, it can be set according to the preset number of pseudo-convergence nodes, or it can be determined according to the preset similarity probability threshold.

[0096] S280, identify non-convergence nodes other than pseudo-convergence nodes as preset nodes.

[0097] According to the above implementation method, a probability transition matrix can be established based on the similarity between nodes. Then, a preset node state matrix is ​​substituted into the probability transition matrix to obtain the target probability transition matrix. This preset node state matrix includes node state elements corresponding to the sink node and node state elements corresponding to non-sink nodes. Thus, based on the label propagation algorithm, the target probability transition matrix can be calculated, and after regularization, the similarity probability value of each non-sink node being identified as a pseudo-sink node can be obtained. This allows for accurate measurement of the similarity between each non-sink node and the sink node, thereby selecting deceptive pseudo-sink nodes and improving the security of sink node location privacy. Furthermore, according to the above implementation method, the selection strategy for pseudo-sink nodes can be flexibly adjusted by adjusting similarity weight adjustment parameters, regularization adjustment parameters, and algorithm parameters such as n. This allows for adjustments to the selection strategy according to different network requirements, thereby improving the flexibility of pseudo-sink node selection.

[0098] In one embodiment, before determining the target transmission path from the preset node to the target pseudo-convergence node according to the preset path selection rules, the method may further include performing the following steps for each preset node:

[0099] For each pseudo-convergence node, the other preset nodes of the first target are divided into the pseudo-sub-node set corresponding to the preset node and the pseudo-convergence node. The other preset nodes of the first target are other preset nodes whose hop count to the pseudo-convergence node is less than the hop count from the preset node to the pseudo-convergence node.

[0100] According to the above implementation method, for each preset node, other preset nodes whose hop count to the pseudo-convergence node is less than their own hop count to the pseudo-convergence node are assigned to the pseudo-child node set of that pseudo-convergence node. Thus, during the sending of fake data packets to the pseudo-convergence node, the next-hop node can be selected through the pseudo-child node set, allowing the fake data packets to gradually approach the pseudo-convergence node. This approach helps to balance the randomness of node paths and conserve node energy in the network.

[0101] In one embodiment, the method may further include performing the following steps for each preset node:

[0102] Other preset nodes of the second target are assigned to the set of child nodes of the preset node, wherein other preset nodes of the second target are other preset nodes whose number of hops to the convergence node is less than the number of hops from the preset node to the convergence node.

[0103] Other preset nodes of the third target are assigned to the parent node set of the preset nodes. The other preset nodes of the third target are other preset nodes whose number of hops to the convergence node is greater than or equal to the number of hops from the preset node to the convergence node.

[0104] According to the above implementation method, for each preset node, other preset nodes whose hop count to the sink node is less than their own hop count to the sink node are assigned to the child node set of that preset node; otherwise, they are assigned to the parent node set of that preset node. In this way, during the transmission of real data packets to the sink node, the next-hop node can be randomly selected from the child node set, allowing the real data packets to gradually approach the sink node along a random path. This not only increases the randomness of the node paths for sending real data packets, making tracking more difficult, but also helps conserve node energy in the network.

[0105] In one embodiment, the preset path selection rules may specifically include:

[0106] The first target pseudo-child node is determined from the first target pseudo-child node set, wherein the first target pseudo-child node set is the pseudo-preset node set corresponding to the target pseudo-convergence node.

[0107] The second target pseudo-child node is determined from the set of second target pseudo-child nodes, wherein the set of second target pseudo-child nodes is the set of pseudo-child nodes corresponding to the pseudo-first target pseudo-child nodes and the pseudo-convergence node of the target.

[0108] The second target pseudo-child node is used as the new first target pseudo-child node. The process of determining the second target pseudo-child node from the set of second target pseudo-child nodes continues until the set of second target pseudo-child nodes is empty.

[0109] The path from the preset node through each first target pseudo-sub-node and second target pseudo-sub-node to the target pseudo-convergence node is determined as the target transmission path.

[0110] According to the above implementation method, pseudo-child nodes can be randomly determined from the target pseudo-child node set of each preset node to serve as the next-hop node for propagating fake data packets. This increases the randomness of the transmission path while causing fake data packets to gradually approach the pseudo-convergence node, thereby reducing the regularity of fake data packet transmission. This reduces the risk of fake data packets and pseudo-convergence nodes being identified, thus improving the security of the true sink node's location privacy.

[0111] In one embodiment, randomly determining the target pseudo-convergence node from the set of pseudo-convergence nodes may specifically include:

[0112] A target pseudo-convergence node is randomly determined from at least one first pseudo-convergence node in the set of pseudo-convergence nodes, including pseudo-convergence nodes that are not the recipients of the second data packets.

[0113] After determining the target pseudo-convergence node from the set of pseudo-convergence nodes, the method may also include:

[0114] In the set of pseudo-convergence nodes, the target pseudo-convergence node is marked as the second pseudo-convergence node, which includes pseudo-convergence nodes that have already acted as the receiver of the second data packet.

[0115] Understandably, if all pseudo-convergence nodes in the pseudo-convergence node set are the second pseudo-convergence node, it can be considered that the pseudo-convergence node set has completed one round of polling. At this point, all pseudo-convergence nodes in the pseudo-convergence node set can be re-marked as the first pseudo-convergence node, and the next round of polling can begin again.

[0116] According to the above implementation method, when sending fake data packets, a target fake aggregation node can be determined from the set of fake aggregation nodes and marked so that it will not be selected again in the future. In this way, the selected fake aggregation node can be continuously changed as the receiver of fake data packets, thereby deceiving traffic analysis and monitoring personnel and making it difficult for them to track real data packets and real sink nodes.

[0117] In one embodiment, determining the first target pseudo-child node from the first target pseudo-child node set may specifically include:

[0118] The first target pseudo-subnode is determined from at least one first pseudo-subnode in the first target pseudo-subnode set, wherein the first pseudo-subnode includes pseudo-subnodes that are not the second data packet forwarders of the preset node.

[0119] After determining the first target pseudo-child node from the set of first target pseudo-child nodes, the method may further include:

[0120] In the first target pseudo-child node set, the first target pseudo-child node is marked as the second pseudo-child node. The second pseudo-child node includes the pseudo-child node that has been used as the second data packet forwarder of the preset node.

[0121] Understandably, if all pseudo-child nodes in the target pseudo-child node set are second pseudo-child nodes, it can be considered that the target pseudo-child node set has completed one round of polling. At this point, all pseudo-child nodes in the target pseudo-child node set can be re-marked as first pseudo-child nodes, and the next round of polling can begin again.

[0122] According to the above implementation method, when sending fake data packets, a target pseudo-child node can be randomly determined from at least one first pseudo-child node in the target pseudo-child node set of each preset node, and this target pseudo-child node can be marked so that it will not be selected again next time. In this way, on the one hand, the randomness of the node path can be enhanced to increase the tracking difficulty, and on the other hand, it is beneficial to balance the energy of each node.

[0123] In one embodiment, determining the first target pseudo-child node from the first target pseudo-child node set may specifically include:

[0124] Obtain the node energy of each pseudo-child node in the first target pseudo-child node set.

[0125] The pseudo-child node with the highest node energy among all pseudo-child nodes in the first target pseudo-child node set is determined as the first target pseudo-child node.

[0126] According to the above implementation method, the pseudo-child node with the highest node energy can be selected as the forwarder of fake data packets. This facilitates dynamic coordination of the forwarder of fake data packets based on node energy, thereby helping to balance the energy of each node.

[0127] In one embodiment, when sending the first data packet to the aggregation node, the node energy of each child node in the preset node's child node set can also be obtained when selecting the next-hop node. The child node with the highest node energy is then selected as the next-hop node.

[0128] In one embodiment, before determining the target pseudo-convergence node from the set of pseudo-convergence nodes, when the first data packet sent by the preset node reaches a first preset number and it is determined based on a preset probability that the preset node will send a second data packet, the method may further include:

[0129] Calculate the preset probability corresponding to the second preset quantity based on the preset correspondence between the second data packet quantity and the probability.

[0130] As an example, probability p fake The number of second data packets, k, can be designed according to a Poisson distribution, and their correspondence can be shown in Equation 4.

[0131]

[0132] In Equation 4, λ can represent the parameters of the Poisson distribution.

[0133] According to the above implementation method, the probability of a preset node sending fake data packets can be flexibly adjusted according to the number of fake data packets to be sent. This helps to balance the security of the real sink node's location privacy with the node's energy consumption.

[0134] Based on the same inventive concept as the data processing method, this application also provides a data processing apparatus.

[0135] like Figure 2 As shown, the data processing device 200 may include a first determining module 201 and a sending module 202.

[0136] The first determining module 201 is used to randomly determine a target pseudo-converging node from a set of pseudo-converging nodes when the first data packet sent by the preset node reaches a first preset number and the preset node is determined to send a second data packet based on a preset probability. The set of pseudo-converging nodes includes multiple pseudo-converging nodes, and the preset probability is the probability of the preset node sending a second data packet.

[0137] The first determining module 201 is also used to determine the target transmission path from the preset node to the target pseudo-convergence node according to the preset path selection rules.

[0138] The sending module 202 is used to send a second preset number of second data packets from a preset node to a target pseudo-convergence node according to the target transmission path.

[0139] The data processing apparatus provided in this application embodiment, when the first data packet sent by a preset node reaches a first preset number, and it is determined based on a preset probability that the preset node will send a second data packet, randomly selects a target pseudo-convergence node from a set of pseudo-convergence nodes. The preset probability is a pre-set probability that the preset node will send the second data packet. The set of pseudo-convergence nodes includes multiple pseudo-convergence nodes. Randomly selecting a target pseudo-convergence node from multiple pseudo-convergence nodes can increase the uncertainty of the fake data packet receiver, thereby reducing the regularity of fake data packet sending. Then, a target transmission path from the preset node to the target pseudo-convergence node can be determined according to a preset path selection rule. According to the target transmission path, a second preset number of second data packets are sent from the preset node to the target pseudo-convergence node. In this way, by designing a combination of multiple pseudo-sink nodes and fake data packet streams, the tracker can be deceived, thereby reducing the risk of leakage of the location privacy of the real sink node and helping to protect the location privacy of the real sink node.

[0140] In some embodiments, the apparatus may further include:

[0141] The acquisition module is used to acquire the neighbor node information of multiple nodes in the target network before determining the target pseudo-convergence node from the pseudo-convergence node set when the preset node sends the second data packet. The multiple nodes include non-convergence nodes and convergence nodes. The neighbor node information of each node includes the identification information of each other node in the target network and the hop count between each other node and the node. The other nodes are nodes in the target network other than the node.

[0142] The second determination module is used to determine the average number of hops between each node and each other node based on the neighbor node information of multiple nodes.

[0143] The calculation module is used to calculate the similarity weight between each node and each other node in a set of nodes, based on the average number of hops from each node to each other node and the average number of hops from the aggregation node to each other node.

[0144] The second determining module is also used to determine the probability transition matrix based on the similarity weights between each node and each other node.

[0145] The substitution module is used to substitute the preset node state matrix into the probability transition matrix to obtain the target probability transition matrix. The preset node state matrix includes node state elements corresponding to the convergent nodes and node state elements corresponding to the non-convergent nodes.

[0146] The first calculation module is also used to perform regularization calculation on the target probability transition matrix according to the preset regularization adjustment parameters, so as to obtain the similarity probability value between each non-converging node and the convergent node.

[0147] The second determining module is also used to determine the top n non-converging nodes with the largest similarity probability values ​​as pseudo-converging nodes, thereby obtaining a set of pseudo-converging nodes, where n is a positive integer.

[0148] The second determining module is also used to determine non-convergence nodes other than pseudo-convergence nodes as preset nodes.

[0149] In one embodiment, the apparatus may further include:

[0150] The partitioning module is used to determine the target transmission path from the preset node to the target pseudo-convergence node according to the preset path selection rules. For each preset node, the following is performed: For each pseudo-convergence node, the other preset nodes of the target are partitioned into the pseudo-sub-node set corresponding to the preset node and the pseudo-convergence node. The other preset nodes of the target are other preset nodes whose hop count to the pseudo-convergence node is less than the hop count from the preset node to the pseudo-convergence node.

[0151] In one embodiment, the preset path selection rules may specifically include:

[0152] The first target pseudo-child node is determined from the first target pseudo-child node set, wherein the first target pseudo-child node set is the pseudo-preset node set corresponding to the target pseudo-convergence node.

[0153] The second target pseudo-child node is determined from the set of second target pseudo-child nodes, wherein the set of second target pseudo-child nodes is the set of pseudo-child nodes corresponding to the pseudo-first target pseudo-child nodes and the pseudo-convergence node of the target.

[0154] The second target pseudo-child node is used as the new first target pseudo-child node. The process of determining the second target pseudo-child node from the set of second target pseudo-child nodes continues until the set of second target pseudo-child nodes is empty.

[0155] The path from the preset node through each first target pseudo-sub-node and second target pseudo-sub-node to the target pseudo-convergence node is determined as the target transmission path.

[0156] In one embodiment, the first determining module is configured to randomly determine a target pseudo-convergence node from a set of pseudo-convergence nodes, which may specifically include:

[0157] A target pseudo-convergence node is randomly determined from at least one first pseudo-convergence node in the set of pseudo-convergence nodes, including pseudo-convergence nodes that are not the recipients of the second data packets.

[0158] The device may also include:

[0159] The first marking module is used to determine the target pseudo-convergence node from the set of pseudo-convergence nodes, and then mark the target pseudo-convergence node as the second pseudo-convergence node in the set of pseudo-convergence nodes. The second pseudo-convergence node includes pseudo-convergence nodes that have acted as the receiver of the second data packet.

[0160] In one embodiment, determining the first target pseudo-child node from the first target pseudo-child node set includes:

[0161] The first target pseudo-subnode is determined from at least one first pseudo-subnode in the first target pseudo-subnode set, wherein the first pseudo-subnode includes pseudo-subnodes that are not the second data packet forwarders of the preset node.

[0162] The device may also include:

[0163] The second marking module is used to mark the first target pseudo-sub-node as the second pseudo-sub-node after determining the first target pseudo-sub-node from the first target pseudo-sub-node set. The second pseudo-sub-node includes the pseudo-sub-node that has been used as the second data packet forwarder of the preset node.

[0164] In one embodiment, determining the first target pseudo-child node from the first target pseudo-child node set may specifically include:

[0165] Obtain the node energy of each pseudo-child node in the first target pseudo-child node set for data processing.

[0166] Among all the pseudo-child nodes in the set of pseudo-child nodes for the first target of data processing, the pseudo-child node with the highest node energy is determined as the pseudo-child node for the first target of data processing.

[0167] In one embodiment, the apparatus may further include:

[0168] The second calculation module is used to calculate the preset probability corresponding to the second preset quantity before determining the target pseudo-convergence node from the pseudo-convergence node set, based on the preset correspondence between the number of second data packets and the probability, when the first data packet sent by the preset node reaches the first preset quantity and the preset node is determined to send the second data packet based on the preset probability.

[0169] The data processing device provided in this application embodiment can achieve... Figure 1 The various processes implemented in the method implementation examples will not be described again here to avoid repetition.

[0170] Figure 3 A schematic diagram of the hardware structure of the terminal device provided in an embodiment of this application is shown.

[0171] The terminal device may include a processor 301 and a memory 302 storing computer program instructions.

[0172] Specifically, the processor 301 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0173] Memory 302 may include mass storage for data or instructions. For example, and not limitingly, memory 302 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 302 may include removable or non-removable (or fixed) media. Where appropriate, memory 302 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 302 is non-volatile solid-state memory.

[0174] In certain embodiments, memory 302 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Thus, generally, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this disclosure.

[0175] The processor 301 implements any of the data processing methods described in the above embodiments by reading and executing computer program instructions stored in the memory 302.

[0176] In one example, the terminal device may also include a communication interface 303 and a bus 310. Wherein, for example... Figure 3 As shown, the processor 301, memory 302, and communication interface 303 are connected through bus 310 and complete communication with each other.

[0177] The communication interface 303 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0178] Bus 310 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 310 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0179] Furthermore, in conjunction with the data processing methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the data processing methods in the above embodiments.

[0180] This application also provides a computer program product, including a computer program, which, when executed, implements any of the data processing methods described in the above embodiments.

[0181] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0182] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0183] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0184] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0185] The above are merely specific embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A data processing method, characterized in that, include: When the number of first data packets sent by the preset node reaches a first preset number, and the preset node is determined to send a second data packet based on a preset probability, a target pseudo-convergence node is randomly determined from the set of pseudo-convergence nodes. The set of pseudo-convergence nodes includes multiple pseudo-convergence nodes, and the preset probability is a pre-set probability that the preset node will send a second data packet. The target transmission path from the preset node to the target pseudo-convergence node is determined according to the preset path selection rules; According to the target transmission path, a second preset number of second data packets are sent from the preset node to the target pseudo-convergence node.

2. The method according to claim 1, characterized in that, Before determining the target pseudo-convergence node from the set of pseudo-convergence nodes when the preset node sends the second data packet, the method further includes: Obtain neighbor node information of multiple nodes in the target network. The multiple nodes include non-convergence nodes and convergence nodes. The neighbor node information of each node includes the identification information of each other node in the target network and the hop count between each other node and the node. The other nodes are nodes in the target network other than the node. Based on the neighbor node information of the multiple nodes, determine the average number of hops between each node and each other node; Based on the average number of hops from each node to each other node, and the average number of hops from the convergence node to each other node, calculate the similarity weight between each node and each other node among the plurality of nodes; The probability transition matrix is ​​determined based on the similarity weights between each node and all other nodes; Substituting the preset node state matrix into the probability transition matrix yields the target probability transition matrix. The preset node state matrix includes node state elements corresponding to the converging nodes and node state elements corresponding to the non-converging nodes. The target probability transition matrix is ​​regularized according to the preset regularization adjustment parameters to obtain the similarity probability value between each non-converging node and the convergent node. The top n non-converging nodes with the highest similarity probability values ​​are identified as pseudo-converging nodes, thus obtaining the set of pseudo-converging nodes, where n is a positive integer; Non-convergence nodes other than the pseudo-convergence node are identified as preset nodes.

3. The method according to claim 2, characterized in that, Before determining the target transmission path from the preset node to the target pseudo-convergence node according to the preset path selection rules, the method further includes performing the following steps for each preset node: For each pseudo-convergence node, the target other preset nodes are divided into the pseudo-sub-node set corresponding to the preset node and the pseudo-convergence node, wherein the target other preset nodes are other preset nodes whose hop count to the pseudo-convergence node is less than the hop count from the preset node to the pseudo-convergence node.

4. The method according to claim 3, characterized in that, The preset path selection rules include: A first target pseudo-child node is determined from the first target pseudo-child node set, wherein the first target pseudo-child node set is a pseudo-child node set corresponding to the preset node and the target pseudo-convergence node; The second target pseudo-sub-node is determined from the second target pseudo-sub-node set, wherein the second target pseudo-sub-node set is a pseudo-sub-node set corresponding to the first target pseudo-sub-node and the target pseudo-convergence node; The second target pseudo-child node is used as the new first target pseudo-child node, and the process of determining the second target pseudo-child node from the set of the second target pseudo-child nodes is repeated until the set of the second target pseudo-child nodes is empty. The path from the preset node through each of the first target pseudo-sub-nodes and the second target pseudo-sub-nodes to the target pseudo-convergence node is determined as the target transmission path.

5. The method according to claim 1, characterized in that, The step of randomly determining the target pseudo-convergence node from the set of pseudo-convergence nodes includes: The target pseudo-convergence node is randomly determined from at least one first pseudo-convergence node in the set of pseudo-convergence nodes, wherein the first pseudo-convergence node includes pseudo-convergence nodes that are not acting as recipients of the second data packet; After determining the target pseudo-convergence node from the set of pseudo-convergence nodes, the method includes: The target pseudo-convergence node in the set of pseudo-convergence nodes is marked as a second pseudo-convergence node, which includes pseudo-convergence nodes that have acted as recipients of the second data packets.

6. The method according to claim 4, characterized in that, The first target pseudo-child node is determined from the set of first target pseudo-child nodes, including: The first target pseudo-subnode is determined from at least one first pseudo-subnode in the first target pseudo-subnode set, wherein the first pseudo-subnode includes pseudo-subnodes that are not the second data packet forwarders of the preset node; After determining the first target pseudo-child node from the first target pseudo-child node set, the method further includes: In the first set of target pseudo-sub-nodes, the first target pseudo-sub-node is marked as the second pseudo-sub-node, and the second pseudo-sub-node includes pseudo-sub-nodes that have already acted as the second data packet forwarder of the preset node.

7. The method according to claim 4, characterized in that, The step of determining the first target pseudo-child node from the first target pseudo-child node set includes: Obtain the node energy of each pseudo-child node in the first target pseudo-child node set; The pseudo-child node with the highest node energy among all pseudo-child nodes in the first target pseudo-child node set is determined as the first target pseudo-child node.

8. The method according to claim 1, characterized in that, Before determining the target pseudo-convergence node from the set of pseudo-convergence nodes, when the first data packet sent by the preset node reaches a first preset number and the preset node is determined to send a second data packet based on a preset probability, the method further includes: According to the preset correspondence between the number of second data packets and the probability, the preset probability corresponding to the second preset number is calculated.

9. A data processing apparatus, characterized in that, include: The determination module is used to randomly determine a target pseudo-convergence node from a set of pseudo-convergence nodes when the first data packet sent by a preset node reaches a first preset number and the preset node is determined to send a second data packet based on a preset probability. The set of pseudo-convergence nodes includes multiple pseudo-convergence nodes, and the preset probability is a pre-set probability that the preset node sends a second data packet. The determining module is further configured to determine the target transmission path from the preset node to the target pseudo-convergence node according to a preset path selection rule; The sending module is used to send a second preset number of second data packets from the preset node to the target pseudo-convergence node according to the target transmission path.

10. An electronic device, characterized in that, The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the data processing method as described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the data processing method as described in any one of claims 1-8.

12. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device performs the data processing method as described in any one of claims 1-8.

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