A multi-device data transmission method based on differential privacy

Abstract

The present invention discloses a multi-device data transmission method based on differential privacy, which is applied to a multi-device communication system. The method comprises: the system collects raw data, resource information data, and path information data of multiple devices contained therein, pre-processes each raw data, and then constructs a super-object model. The super-object model is used to obtain the resource state vector and topology adjacency table of each device. The system performs differential privacy processing on the resource state vector, topology adjacency table, and parameters during the dynamic update of the super-object model; the shortest path is selected for the data transmission path of each device; the system fragments the data packet to be sent and distributes the fragmented data to each device according to the principle of load balancing. Each device transmits the fragmented data to the receiving device, and the receiving device reassembles and verifies the fragmented data after receiving it. The present invention improves transmission efficiency while ensuring data transmission privacy.

Description

Technical Field

[0001] The present invention belongs to the field of computer technology, in particular to the fields of distributed computing, the Internet of Things, and data privacy protection, and specifically relates to a multi-device data transmission method based on differential privacy. Background Art

[0002] With the rapid development of distributed computing and the Internet of Things (IoT), a vast number of devices are exchanging and communicating data over networks. Traditional data transmission models often rely on a single path, which can easily lead to data congestion and low efficiency. Furthermore, data transmission between devices requires privacy protection, especially when handling sensitive data. Existing privacy protection methods are not well suited for coordinated transmission across multiple devices in distributed nodes. Therefore, how to improve transmission efficiency while ensuring data privacy has become a pressing issue.

[0003] In view of this, this invention is proposed. Summary of the Invention

[0004] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a multi-device data transmission method based on differential privacy. The method constructs a super object model, obtains the resource state vector and topological structure adjacency table of each device through the super object model, and performs differential privacy processing on them. Then, the transmission path of each device is optimized and sharded data is distributed to each device according to the principle of balanced load. Each device sends sharded data to the receiving device according to the selected shortest path; in addition, during the data transmission process, the shortest transmission path of each device is dynamically adjusted according to the status data, and the sharded data is redistributed according to the new path. This method can significantly improve the performance and stability of data transmission.

[0005] The purpose of the present invention is to solve the problem through the following technical solutions:

[0006] The present invention provides a multi-device data transmission method based on differential privacy, which is applied to multi-device communication systems, such as distributed systems, Internet of Things systems, mobile application systems, etc., including:

[0007] The system collects raw data, resource information data, and path information data of multiple devices it contains, and preprocesses each raw data to obtain a data packet to be sent. It then uses the resource information data and path information data to build a super-thing model, and dynamically updates the super-thing model. The super-thing model is used to obtain the resource state vector and topology adjacency table of each device. The system performs differential privacy processing on the resource state vector, topology adjacency table, and parameters during the dynamic update of the super-thing model.

[0008] The shortest path for data transmission of each device is selected based on the path information data of each device and the data processed after differential privacy. The system fragments the data packet to be sent and distributes the fragmented data to each device according to the load balancing principle. Each device then transmits the fragmented data to the receiving device, which reassembles and verifies the fragmented data after receiving it.

[0009] During the transmission process, the receiving device monitors the status data of the shortest path in real time and feeds the status data back to the system, and then the system dynamically adjusts the shortest path and fragmentation data of each device according to the status data.

[0010] Furthermore, the process of preprocessing each raw data is as follows:

[0011] (1) All original data were processed for missing values;

[0012] (2) All original data after missing value processing are tested for outliers using the Z-score method, and the detected outliers are corrected or eliminated;

[0013] (3) All original data after outlier detection and correction are denoised.

[0014] Furthermore, the super object model is ;

[0015] Where N represents the number of devices included in the system. represents the resource state vector of device 𝑖, Represents the topological adjacency list of device 𝑖;

[0016] The resource state vector is expressed as =[𝑟 𝑖1 , 𝑟 𝑖2 , …, 𝑟 𝑖𝑛 ]; where 𝑟 𝑖𝑗 represents the 𝑗th type of resource status data of device 𝑖;

[0017] The topological adjacency table is expressed as ;in, represents the set of devices directly connected to device 𝑖.

[0018] Furthermore, the following formula is used to dynamically update the super object model:

[0019] (1)

[0020] in, is the resource consumption vector of device 𝑖 at time t, the resource consumption vector including the consumption amount of each type of resource status data;

[0021] The resource consumption vector is expressed as ,in is the consumption of the j-th resource status data at time t.

[0022] Furthermore, the devices include IoT devices, sensors, and terminal devices; and the resource status data include computing power, bandwidth, and storage space.

[0023] Furthermore, the system calculates the current resource state vector of the device , topological adjacency list , resource consumption vector Differential privacy processing is performed.

[0024] Furthermore, each device includes multiple transmission paths when transmitting data to the receiving device, and the shortest path set P corresponding to each device is established by the following formula: optimal ;

[0025] (2)

[0026] (3)

[0027] in, is the weight of path p, is the delay of path p, is the weight coefficient, and its values ​​are 0.3, 0.3, and 0.4 respectively. Bandwith p 、Delay p 、LossRate p denote the bandwidth, delay and packet loss rate of path p respectively.

[0028] Furthermore, the receiving device reassembles the fragmented data into data of a data packet to be sent according to the timestamp and sequence number of the received fragmented data, and performs verification and recovery on the reassembled data.

[0029] Furthermore, during the transmission process, the rate on each shortest path is dynamically adjusted according to its corresponding packet loss rate.

[0030] Furthermore, the status data includes bandwidth, delay, and packet loss rate; during the transmission process, the system dynamically adjusts the shortest path of each device based on the bandwidth, delay, and packet loss rate fed back by the receiving device, and redistributes the fragmented data to the new shortest path based on the load balancing principle.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1) The present invention provides a multi-device data transmission method based on differential privacy. The method constructs a super-object model and dynamically updates it. The resource state vector and topology adjacency table of each device are obtained through the super-object model, and differential privacy processing is performed on the resource state vector, topology adjacency table and resource consumption vector. Then, the transmission path of each device is optimized, and sharded data is distributed to each device according to the load balancing principle. Each device sends the sharded data to the receiving device according to the selected shortest path. At the same time, during the transmission process, the system uses a path optimization algorithm to reselect the optimal path based on the status data fed back by the receiving device, and maximizes bandwidth utilization by adjusting the sharding strategy, reducing transmission delay and packet loss rate. Moreover, the optimization of dynamic path selection and sharding strategy can significantly improve the performance and stability of data transmission, ensure reliable data transmission in complex network environments, and solve the problems of insufficient privacy protection and low transmission efficiency in multi-device data transmission in the existing technology.

[0033] 2) The present invention provides a multi-device data transmission method based on differential privacy, which dynamically adjusts the transmission rate according to the packet loss rate of the transmission path during the transmission process, thereby ensuring the reliability of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings are incorporated in and constitute a part of this specification and, together with the description, serve to explain the principles of the invention.

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0036] Figure 1 This is a flowchart of the multi-device data transmission method based on differential privacy of the present invention. DETAILED DESCRIPTION

[0037] Here, exemplary embodiments are described in detail. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples consistent with some aspects of the present invention as detailed in the appended claims.

[0038] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the present invention is described in further detail below with reference to the accompanying drawings and embodiments.

[0039] like Figure 1As shown, an embodiment of the present invention provides a multi-device data transmission method based on differential privacy, which is applied to a distributed system, including:

[0040] The system collects raw data, resource information data, and path information data of multiple devices it contains, and preprocesses each raw data to obtain a data packet to be sent. It then uses the resource information data and path information data to build a super-thing model, and dynamically updates the super-thing model. The super-thing model is used to obtain the resource state vector and topology adjacency table of each device. The system performs differential privacy processing on the resource state vector, topology adjacency table, and parameters during the dynamic update of the super-thing model.

[0041] The shortest path for each device's data transmission path is selected based on the path information data of each device and the data processed after differential privacy. The system fragments the data packets to be sent and distributes the fragmented data to each device based on the load balancing principle. Each device transmits the fragmented data to the receiving device, which reassembles and verifies the fragmented data after receiving it.

[0042] During the transmission process, the receiving device monitors the status data of the shortest path in real time and feeds the status data back to the system, and then the system dynamically adjusts the shortest path and fragmentation data of each device according to the status data.

[0043] Furthermore, the process of preprocessing each raw data is as follows:

[0044] (1) All original data are processed for missing values. Specifically, the mean interpolation method or K-nearest neighbor interpolation algorithm is used to supplement the missing data;

[0045] (2) All original data after missing value processing are tested for outliers using the Z-score method, and the detected outliers are corrected or eliminated;

[0046] The calculation formula of Z-score is:

[0047] (1)

[0048] in, represents the i-th data in the original data, 𝜇 represents the mean of the original data, and 𝜎 represents the standard deviation of the original data;

[0049] When | 𝑍 𝑖 | > 3, judge is an outlier;

[0050] (3) All original data after outlier detection and correction are subjected to denoising. Specifically, the denoising is performed using a Kalman filter or a wavelet transform.

[0051] Furthermore, a super object model is constructed using the resource information data and the path information data to represent the physical resources and logical topology of each device node.

[0052] Specifically, the super object model is expressed as ;

[0053] Where N represents the number of devices included in the system. represents the resource state vector of device 𝑖, Represents the topological adjacency list of device 𝑖;

[0054] Specifically, the resource state vector is expressed as =[𝑟 𝑖1 , 𝑟 𝑖2 , …, 𝑟 𝑖𝑛 ]; where 𝑟 𝑖𝑗 Represents the 𝑗th type of resource status data of device 𝑖;

[0055] The connection status of device i and other devices is represented by the topological structure adjacency table of the device. Specifically, the topological structure adjacency table is represented as ;in, represents the set of devices directly connected to device 𝑖.

[0056] Furthermore, at each time step t, the resource state vector of device i is updated according to the current load and resource consumption. Specifically, the following formula (2) is used to dynamically update the hyper-object model:

[0057] (2)

[0058] in, is the resource consumption vector of device 𝑖 at time t, the resource consumption vector including the consumption amount of each type of resource status data;

[0059] The resource consumption vector is expressed as ,in is the consumption of the j-th resource status data at time t.

[0060] In this embodiment, the devices include IoT devices, sensors, and terminal devices; the resource status data include computing power, bandwidth, storage space, latency, bandwidth utilization, throughput, etc.; the resource information data includes computer resources, storage resources, network resources, service resources, security resources, configuration information, environmental information, and performance indicators, etc.

[0061] Differential privacy data processing: The system processes the resource state vector of the current device , topological adjacency list and resource consumption vectors Sensitive data such as data are differentially privately processed by adding Gaussian noise to prevent the overall statistical information of the system from being leaked, thereby ensuring the privacy security of data transmission.

[0062] Specifically, the process of differential privacy processing using Gaussian noise is as follows:

[0063] Assume that the data to be encrypted is 𝑓(𝑥), and the encrypted data is 𝑀(𝑥). The encryption algorithm is as follows:

[0064] 𝑀(𝑀)=𝑓(𝑁)+𝑁(0,σ 2 ) (3)

[0065] in In formula (3), the noise has a mean of 0 and a variance of σ 2 The calculation of σ depends on Δf (function sensitivity), ε (privacy budget) and δ (allowable probability failure).

[0066] In this embodiment, the transmission path selection includes: according to the path information data of each device and the data processed by differential privacy (the current resource state vector of the device , topological adjacency list and resource consumption vectors The optimal multi-path selection scheme is determined based on the sensitive data after differential privacy processing, which is also the shortest path selection. The goal is to find an optimal path for each device and then obtain the set of all optimal paths.

[0067] Specifically, each device includes multiple transmission paths when transmitting data to the receiving device. The shortest path set P corresponding to each device is established by the following formula: optimal ;

[0068] (4)

[0069] (5)

[0070] in, is the weight of path p, is the delay of path p, is the weight coefficient, and its values ​​are 0.3, 0.3, and 0.4 respectively. The value can be adjusted according to the specific scenario. p 、Delay p 、LossRate p denote the bandwidth, delay and packet loss rate of path p respectively.

[0071] Its specific implementation adopts heuristic algorithm or dynamic programming algorithm.

[0072] In this embodiment, fragmentation and allocation of data packets to be transmitted includes: based on the above-mentioned path selection results, fragmenting the data packets to be transmitted and distributing them to the shortest path selected by each device. Allocation is based on the bandwidth and load capacity of the shortest path to ensure load balancing on each path during transmission. The path fragment size allocation formula is as follows:

[0073] (6)

[0074] Among them, D j represents the number of shard data allocated to path j, w j represents the weight of path j (calculated by formula 5), Indicates the size of the data packet to be sent.

[0075] Furthermore, the receiving device reassembles the fragmented data into data packets to be sent based on the timestamps and sequence numbers of the received fragmented data, and performs data verification and recovery using an error correction algorithm, wherein the error correction algorithm may be RS code or LDPC code.

[0076] Specifically, the data verification and recovery process is as follows:

[0077] (1) Sequence number sorting: The receiving device first sorts the fragments in ascending order according to the sequence number of each fragment to ensure that the data packets are reassembled in the correct order;

[0078] (2) Timestamp alignment: The receiving device reassembles the fragmented data into the data packet to be sent based on the sequence number and timestamp. The formula for the data reassembly process is:

[0079] (7)

[0080] Among them, d i Indicates the received i-th fragment data, D reconstructed Indicates the complete data after final reorganization;

[0081] (3) Verification and Error Correction: An error correction algorithm is used to perform integrity verification and error correction on the final reconstructed complete data to address data loss or errors that may occur during network transmission. This embodiment uses RS (Reed-Solomon) codes or LDPC codes for data correction.

[0082] During the transmission process, the rate on each shortest path is dynamically adjusted based on its packet loss rate to ensure that the fragmented data on each path is transmitted independently. This reduces data loss and congestion, ensuring transmission stability and efficiency. The formula for calculating the data transmission rate on each path is:

[0083] (8)

[0084] in, represents the packet loss rate of path i.

[0085] Furthermore, the status data includes bandwidth, delay, and packet loss rate. During the transmission process, the receiving device feeds back bandwidth, delay, packet loss rate and other indicators to the system. The system updates the data transmission path in real time based on the received indicators and redistributes the fragmented data to each path based on the load balancing principle. That is, for paths with degraded performance, the allocation of fragmented data is reduced or stopped, and for paths with improved performance, the allocation of fragmented data is increased. Specifically, it includes:

[0086] Monitoring feedback: The receiving device continuously monitors the bandwidth, latency, packet loss rate and other status data of the transmission path.

[0087] Weight update: Based on the latest feedback status data, the system reselects the optimal multi-path selection scheme under the current system status according to the above transmission path selection method, and dynamically adjusts the path selection.

[0088] Shard redistribution: Based on the new path selection results, shard data is redistributed and the transmission tasks on each path are adjusted.

[0089] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention.

[0090] It should be understood that the present invention is not limited to the above description and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A multi-device data transmission method based on differential privacy, characterized in that: Applicable to multi-device communication systems, including: The system collects raw data, resource information data, and path information data of multiple devices it contains, and preprocesses each raw data to obtain a data packet to be sent. It then uses the resource information data and path information data to build a super-thing model, and dynamically updates the super-thing model. The super-thing model is used to obtain the resource state vector and topology adjacency table of each device. The system performs differential privacy processing on the resource state vector, topology adjacency table, and parameters during the dynamic update of the super-thing model. The shortest path for data transmission of each device is selected based on the path information data of each device and the data processed after differential privacy. The system fragments the data packet to be sent and distributes the fragmented data to each device according to the load balancing principle. Each device then transmits the fragmented data to the receiving device, which reassembles and verifies the fragmented data after receiving it. During the transmission process, the receiving device monitors the status data of the shortest path in real time and feeds the status data back to the system, and then the system dynamically adjusts the shortest path and fragmentation data of each device according to the status data; The super object model is ; Where N represents the number of devices included in the system. represents the resource state vector of device 𝑖, Represents the topological adjacency table of device 𝑖; The resource state vector is expressed as =[𝑟 𝑖1 ,𝑟 𝑖2 ,…,𝑟 𝑖𝑛 ]; where 𝑟 𝑖𝑗 Represents the 𝑗th type of resource status data of device 𝑖; The topological adjacency table is expressed as ;in, represents the set of devices directly connected to device 𝑖; When each device transmits data to the receiving device, it includes multiple transmission paths. The shortest path set P corresponding to each device is established by the following formula. optimal ; （1） （2） in, is the weight of path p, is the delay of path p, is the weight coefficient, Bandwith p 、Delay p 、LossRate p denote the bandwidth, delay and packet loss rate of path p respectively.

2. The multi-device data transmission method based on differential privacy according to claim 1, characterized in that: The preprocessing process for each raw data is as follows: (1) All original data were processed for missing values; (2) All original data after missing value processing are tested for outliers using the Z-score method, and the detected outliers are corrected or eliminated; (3) All original data after outlier detection and correction are denoised.

3. The multi-device data transmission method based on differential privacy according to claim 1, characterized in that: The following formula is used to dynamically update the super object model: （3） in, is the resource consumption vector of device 𝑖 at time t, the resource consumption vector including the consumption of each type of resource status data; The resource consumption vector is expressed as ,in is the consumption of the j-th resource status data at time t.

4. The multi-device data transmission method based on differential privacy according to claim 1, characterized in that: The devices include IoT devices, sensors, and terminal devices; the resource status data includes computing power, bandwidth, and storage space.

5. The multi-device data transmission method based on differential privacy according to claim 1, characterized in that: The system's current resource status vector for the device , topological adjacency list , resource consumption vector Differential privacy processing is performed.

6. The multi-device data transmission method based on differential privacy according to claim 1, characterized in that: The receiving device reassembles the fragmented data into data of a data packet to be sent according to the timestamp and sequence number of the received fragmented data, and performs verification and recovery on the reassembled data.

7. The multi-device data transmission method based on differential privacy according to claim 1, characterized in that: During the transmission process, the rate on each shortest path is dynamically adjusted according to its corresponding packet loss rate.

8. The multi-device data transmission method based on differential privacy according to claim 1, characterized in that: The status data includes bandwidth, delay, and packet loss rate; during the transmission process, the system dynamically adjusts the shortest path of each device in accordance with the method of claim 7 based on the bandwidth, delay, and packet loss rate fed back by the receiving device, and redistributes the fragmented data to the new shortest path based on the load balancing principle.

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