A blockchain-based data privacy protection system

Abstract

This application relates to the field of data security and provides a blockchain-based data privacy protection system, comprising: a sensor module for acquiring detection data and using preset data in the detection data as sensitive data; a data noise-adding module for adding noise to the sensitive data based on a preset noise-adding key to obtain noisy data; a blockchain storage module for storing the noisy data in a blockchain node; and a data authorization module for obtaining the noise-adding key according to user authorization and denoising the noisy data based on the noise-adding key to obtain the sensitive data.

Description

Technical Field

[0001] This application relates to the field of data security, and in particular to a data privacy protection system based on blockchain. Background Technology

[0002] With the development of IoT technology, more and more devices in our lives have data recording and uploading capabilities. Most of the data recorded by IoT devices is intended to provide users with a better experience; however, some devices collect sensitive data, such as medical data collected by home smart medical devices or images captured by mobile devices with image acquisition capabilities. How to store and transmit this sensitive data while ensuring user privacy has become a pressing issue. Summary of the Invention

[0003] The main purpose of this application is to provide a blockchain-based data privacy protection system, which aims to improve the security of sensitive data.

[0004] Firstly, this application provides a blockchain-based data privacy protection system, comprising:

[0005] The sensor module is used to acquire detection data and use preset data in the detection data as sensitive data;

[0006] A data noise-adding module is used to add noise to the sensitive data based on a preset noise-adding key to obtain noisy data;

[0007] A blockchain storage module is used to store the noise-infused data to a blockchain node;

[0008] The data authorization module is used to obtain the noise-adding key according to the user's authorization, and to denoise the noise-adding data based on the noise-adding key to obtain the sensitive data.

[0009] In some implementations, the step of adding noise to the sensitive data based on a preset noise-adding key to obtain noisy data includes:

[0010] The sensitive data is converted into frequency domain data using Fourier transform;

[0011] The noise parameters are determined based on the noise key and the preset noise seed, and the frequency domain data is noise-added based on the noise parameters to obtain the noise-added data.

[0012] In some implementations, the step of determining noise parameters based on the noise-adding key and a preset noise seed, and then performing noise-adding processing on the frequency domain data based on the noise parameters to obtain the noise-added data, includes:

[0013] The phase noise parameters and amplitude noise parameters are determined based on the noise-adding key;

[0014] The pseudo-random parameters are determined based on the noise seed;

[0015] The frequency domain data is noise-added based on the phase noise parameter, the amplitude noise parameter, and the pseudo-random parameter to obtain the noise-added data.

[0016] In some implementations, determining the phase noise parameter and amplitude noise parameter based on the noise-adding key includes:

[0017] The phase noise parameters and amplitude noise parameters are determined based on the noise-adding key using the following formulas:

[0018]

[0019]

[0020] in, Represents phase noise parameters. The amplitude noise parameter is represented; the noise-adding key is K={α,β, , , , W}, For a random process that follows a complex Gaussian distribution, ~CN(0, ) represents the white noise substrate; It follows a real Gaussian distribution. ~CN(0, ).

[0021] In some implementations, the step of adding noise to the frequency domain data based on the phase noise parameter, the amplitude noise parameter, and the pseudo-random parameter to obtain the noisy data includes:

[0022] The frequency domain data is noise-added using the following formula to obtain noisy frequency domain data:

[0023] ;

[0024] in, This represents the original phase of the frequency domain data;

[0025] The noise-added data is determined based on the pseudo-random parameters of the noise-added frequency domain data.

[0026] In some implementations, determining the noisy data based on the pseudo-random parameters of the noisy frequency domain data includes:

[0027] The noisy frequency domain data is converted into noisy time domain data, and the pseudo-random parameters are added to the noisy time domain data to obtain the noisy data.

[0028] In some implementations, obtaining the noise-adding key based on user authorization, and denoising the noisy data based on the noise-adding key to obtain the sensitive data, includes:

[0029] Based on the user's authorization, the noise-adding key and noise seed are sent to the data authorization module in different ways. The noise-adding data is then denoised based on the noise-adding key and the noise seed to obtain the sensitive data.

[0030] In some implementations, the step of adding noise to the sensitive data based on a preset noise-adding key to obtain noisy data includes:

[0031] After the authorization period expires, a new noise-adding key is generated, and the sensitive data is noise-adding processing is performed again.

[0032] In some implementations, storing the noisy data on a blockchain node includes:

[0033] The hash value of the noisy data is stored in the blockchain node.

[0034] In some implementations, obtaining the noise-adding key based on user authorization, and denoising the noisy data based on the noise-adding key to obtain the sensitive data, includes:

[0035] Determine the authorization status based on the following authorization function:

[0036] ;

[0037] in, The output value of the authorization function is represented by θ, which represents the system state vector. Indicates the user's authorization status. This represents the permission weight coefficient, where t represents the current timestamp. Indicates the timestamp of the authorization operation. This represents the time tolerance parameter.

[0038] This application provides a blockchain-based data privacy protection system. The system utilizes a sensor module to acquire detection data and designate preset data within this data as sensitive data; a data noise-adding module to add noise to the sensitive data based on a preset noise-adding key, resulting in noisy data; a blockchain storage module to store the noisy data on a blockchain node; and a data authorization module to obtain the noise-adding key based on user authorization and to denoise the noisy data using the noise-adding key, thus obtaining the sensitive data. Because the sensitive data is noise-adding, the original sensitive data can only be restored with user authorization, improving the security of the sensitive data. Furthermore, storing the noisy data through blockchain nodes prevents the noisy data from being corrupted or tampered with. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 A schematic block diagram of a blockchain-based data privacy protection system provided in an embodiment of this application;

[0041] Figure 2 This is a flowchart illustrating a blockchain-based data privacy protection method provided in one embodiment of this application;

[0042] Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of this application. Detailed Implementation

[0043] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0044] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0045] This application provides a blockchain-based data privacy protection system.

[0046] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0047] Please refer to Figure 1 , Figure 1 This is a schematic block diagram illustrating a blockchain-based data privacy protection system according to an embodiment of this application. This blockchain-based data privacy protection system can be used in terminals or servers. The terminal can be an electronic device such as a mobile phone, tablet, laptop, desktop computer, personal digital assistant, or wearable device. The server can be a standalone server, a server cluster, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0048] like Figure 1 As shown, the blockchain-based data privacy protection system includes: a sensor module 110, a data noise-adding module 120, a blockchain storage module 130, and a data authorization module 140.

[0049] Sensor module 110 is used to acquire detection data and use preset data in the detection data as sensitive data;

[0050] The data noise-adding module 120 is used to add noise to the sensitive data based on a preset noise-adding key to obtain noisy data;

[0051] Blockchain storage module 130 is used to store the noise-added data to a blockchain node;

[0052] The data authorization module 140 is used to obtain the noise-adding key according to the user's authorization, and to denoise the noise-adding data based on the noise-adding key to obtain the sensitive data.

[0053] For example, this blockchain-based data privacy protection system can be installed in home IoT devices, such as smart blood pressure monitors, smart blood glucose meters, and other home medical devices, or self-moving devices with camera functions, such as robot vacuums and lawn mowers. The data detected by these IoT devices is often sensitive; therefore, the blockchain-based data privacy protection system provided in this application protects this sensitive data.

[0054] For example, the data detected by home IoT devices may include various types, including sensitive data and ordinary data that is not sensitive. Preset data in the detection data can be used as sensitive data. For example, the data detected by a preset sensor among multiple sensors can be used as sensitive data. Taking a robot vacuum cleaner as an example, the detection data of the laser sensor can be used as ordinary data, and the image data captured by the camera can be used as sensitive data.

[0055] For example, the blockchain-based data privacy protection system provided in this application adds noise to sensitive data. Only with user authorization can the noise-added data be de-noised to obtain the original sensitive data, so that even if a third party steals data from an IoT device, they will not be able to recover the original sensitive data.

[0056] For example, storing the noisy data on a blockchain node prevents the noisy data from being tampered with and improves the security of the noisy data.

[0057] In some implementations, the step of adding noise to the sensitive data based on a preset noise-adding key to obtain noisy data includes:

[0058] The sensitive data is converted into frequency domain data using Fourier transform;

[0059] The noise parameters are determined based on the noise key and the preset noise seed, and the frequency domain data is noise-added based on the noise parameters to obtain the noise-added data.

[0060] For example, adding noise directly in the time domain will result in noise being evenly distributed across all time points. In the frequency domain, however, the noise power of different frequency components can be precisely controlled. Noise can be added selectively based on the frequency characteristics of the data, conforming to the physical characteristics of noise and facilitating the simulation of real-world noise environments.

[0061] For example, converting sensitive data from the time domain to the frequency domain using Fourier transform (FFT) is much more efficient than processing the data directly in the time domain.

[0062] In some implementations, the step of determining noise parameters based on the noise-adding key and a preset noise seed, and then performing noise-adding processing on the frequency domain data based on the noise parameters to obtain the noise-added data, includes:

[0063] The phase noise parameters and amplitude noise parameters are determined based on the noise-adding key;

[0064] The pseudo-random parameters are determined based on the noise seed;

[0065] The frequency domain data is noise-added based on the phase noise parameter, the amplitude noise parameter, and the pseudo-random parameter to obtain the noise-added data.

[0066] For example, the noise parameters include phase noise parameters, amplitude noise parameters, and pseudo-random parameters. The phase noise parameters and amplitude noise parameters are determined by an encryption key, while the pseudo-random parameters are determined by a noise seed. The noise key and noise seed can be pre-set by the user or randomly generated and stored in the system.

[0067] In this way, even if a thief obtains either the noisy key or the pseudo-random parameters, they will not be able to recover the original sensitive data.

[0068] In some implementations, determining the phase noise parameter and amplitude noise parameter based on the noise-adding key includes:

[0069] The phase noise parameters and amplitude noise parameters are determined based on the noise-adding key using the following formulas:

[0070]

[0071]

[0072] in, Represents phase noise parameters. The amplitude noise parameter is represented; the noise-adding key is K={α,β, , , , W}, For a random process that follows a complex Gaussian distribution, ~CN(0, ) represents the white noise substrate; It follows a real Gaussian distribution. ~CN(0, ).

[0073] For example, the noisy key K={α, β, , , , W}, where α is the phase noise intensity coefficient and β is the amplitude noise intensity coefficient, both of which are scalars; The standard deviation of phase noise, expressed in radians; The standard deviation of amplitude noise is a relative amplitude. The noise center frequency is represented by , and W represents the noise bandwidth, both in Hz.

[0074] For example, phase noise parameters In the frequency domain, it manifests as a random phase shift, which is a random process following a complex Gaussian distribution. ~CN(0, and Gaussian window function It is confirmed that the Gaussian window function concentrates noise energy in the frequency band. -W, Within +W], simulate band-limited characteristics.

[0075] For example, amplitude noise parameters For the random perturbation of frequency domain amplitude, the denominator term A smooth out-of-band transition is achieved through Butterworth attenuation, avoiding the ringing effect of an ideal filter.

[0076] In some implementations, the step of adding noise to the frequency domain data based on the phase noise parameter, the amplitude noise parameter, and the pseudo-random parameter to obtain the noisy data includes:

[0077] The frequency domain data is noise-added using the following formula to obtain noisy frequency domain data:

[0078] ;

[0079] in, This represents the original phase of the frequency domain data;

[0080] The noise-added data is determined based on the pseudo-random parameters of the noise-added frequency domain data.

[0081] For example, in the above formula Indicates phase noise, To represent amplitude noise, the above formula can also be equivalent to:

[0082]

[0083] In some implementations, determining the noisy data based on the pseudo-random parameters of the noisy frequency domain data includes:

[0084] The noisy frequency domain data is converted into noisy time domain data, and the pseudo-random parameters are added to the noisy time domain data to obtain the noisy data.

[0085] For example, the noisy frequency domain data is converted to the time domain to obtain noisy time domain data, and then pseudo-random parameters are added to obtain noisy data. In this way, even if a third party steals the noise seed and determines the pseudo-random parameters, they can only restore the noisy time domain data, but cannot restore the original sensitive data.

[0086] In some implementations, obtaining the noise-adding key based on user authorization, and denoising the noisy data based on the noise-adding key to obtain the sensitive data, includes:

[0087] Based on the user's authorization, the noise-adding key and noise seed are sent to the data authorization module in different ways. The noise-adding data is then denoised based on the noise-adding key and the noise seed to obtain the sensitive data.

[0088] For example, to improve system security, it is necessary to enhance the noise key and noise seed used in the noise enhancement process. These can be stored in different locations and encrypted using different methods. However, during user authorization, the noise key and noise seed need to be transmitted, a process that is undoubtedly vulnerable to interception. Therefore, the noise key and noise seed can be transmitted separately using different methods, such as different communication methods or different channels of the same communication method, which will not be elaborated upon here.

[0089] In some implementations, the step of adding noise to the sensitive data based on a preset noise-adding key to obtain noisy data includes:

[0090] After the authorization period expires, a new noise-adding key is generated, and the sensitive data is noise-adding processing is performed again.

[0091] For example, to prevent users from permanently obtaining sensitive data once authorized, after each authorization period expires, a new noise-adding key and noise seed are generated, the sensitive data is re-noised, and the noise-adding key and noise seed are stored.

[0092] In some implementations, storing the noisy data on a blockchain node includes:

[0093] The hash value of the noisy data is stored in the blockchain node.

[0094] For example, if noisy data is stored on blockchain nodes, due to the immutable and undeletable nature of the blockchain, the noisy data will exist permanently on the blockchain. If the historical noisy key is subsequently leaked, the noisy data on the blockchain can be easily cracked. To avoid this situation, only the hash value of the noisy data is stored on the blockchain nodes. When noisy data needs to be de-noised, comparing the hash value of the off-chain noisy data with the hash value on the blockchain node ensures that the noisy data has not been tampered with.

[0095] In some implementations, obtaining the noise-adding key based on user authorization, and denoising the noisy data based on the noise-adding key to obtain the sensitive data, includes:

[0096] Determine the authorization status based on the following authorization function:

[0097] ;

[0098] in, The output value of the authorization function is represented by θ, which represents the system state vector. Indicates the user's authorization status. This represents the permission weight coefficient, where t represents the current timestamp. Indicates the timestamp of the authorization operation. This represents the time tolerance parameter.

[0099] For example, the authorization function is used to dynamically control data access permissions and user authorization status. It can be 0 or 1, when t≤ hour Return the noisy key to ensure that the data is reversible only during the authorized period.

[0100] Among them, the output value of the authorization function Used to determine whether to return the noisy key; the system state vector θ includes at least user identity and time, serving as the input for dynamically adjusting permissions; user authorization status. Used to identify whether a user has authorized access and to control the conditions under which permissions take effect; permission weight coefficient. Adjust the priority or strength of different permissions; the current timestamp 't' is used for comparison with the authorization time to trigger the validity of permissions; the timestamp of the authorization operation. This indicates the base time point at which permissions take effect; each authorized user corresponds to one. Time tolerance parameter The width used to control the validity period of permissions; the larger the standard deviation, the longer the validity period.

[0101] This application provides a blockchain-based data privacy protection system. The system utilizes a sensor module to acquire detection data and designate preset data within this data as sensitive data; a data noise-adding module to add noise to the sensitive data based on a preset noise-adding key, resulting in noisy data; a blockchain storage module to store the noisy data on a blockchain node; and a data authorization module to obtain the noise-adding key based on user authorization and to denoise the noisy data using the noise-adding key, thus obtaining the sensitive data. Because the sensitive data is noise-adding, the original sensitive data can only be restored with user authorization, improving the security of the sensitive data. Furthermore, storing the noisy data through blockchain nodes prevents the noisy data from being corrupted or tampered with.

[0102] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating a blockchain-based data privacy protection method provided in one embodiment of this application.

[0103] This application also provides a blockchain-based data privacy protection method.

[0104] like Figure 2 As shown, the blockchain-based data privacy protection method includes steps S101 to S104.

[0105] Step S101: Obtain detection data and use preset data in the detection data as sensitive data;

[0106] Step S102: Add noise to the sensitive data based on a preset noise-adding key to obtain noisy data;

[0107] Step S103: Store the noise-added data to the blockchain node;

[0108] Step S104: Obtain the noise-adding key according to the user's authorization, and denoise the noise-adding data based on the noise-adding key to obtain the sensitive data.

[0109] In some implementations, the step of adding noise to the sensitive data based on a preset noise-adding key to obtain noisy data includes:

[0110] The sensitive data is converted into frequency domain data using Fourier transform;

[0111] The noise parameters are determined based on the noise key and the preset noise seed, and the frequency domain data is noise-added based on the noise parameters to obtain the noise-added data.

[0112] In some implementations, the step of determining noise parameters based on the noise-adding key and a preset noise seed, and then performing noise-adding processing on the frequency domain data based on the noise parameters to obtain the noise-added data, includes:

[0113] The phase noise parameters and amplitude noise parameters are determined based on the noise-adding key;

[0114] The pseudo-random parameters are determined based on the noise seed;

[0115] The frequency domain data is noise-added based on the phase noise parameter, the amplitude noise parameter, and the pseudo-random parameter to obtain the noise-added data.

[0116] In some implementations, determining the phase noise parameter and amplitude noise parameter based on the noise-adding key includes:

[0117] The phase noise parameters and amplitude noise parameters are determined based on the noise-adding key using the following formulas:

[0118]

[0119]

[0120] in, Represents phase noise parameters. The amplitude noise parameter is represented; the noise-adding key is K={α,β, , , , W}, For a random process that follows a complex Gaussian distribution, ~CN(0, ) represents the white noise substrate; It follows a real Gaussian distribution. ~CN(0, ).

[0121] In some implementations, the step of adding noise to the frequency domain data based on the phase noise parameter, the amplitude noise parameter, and the pseudo-random parameter to obtain the noisy data includes:

[0122] The frequency domain data is noise-added using the following formula to obtain noisy frequency domain data:

[0123] ;

[0124] in, This represents the original phase of the frequency domain data;

[0125] The noise-added data is determined based on the pseudo-random parameters of the noise-added frequency domain data.

[0126] In some implementations, determining the noisy data based on the pseudo-random parameters of the noisy frequency domain data includes:

[0127] The noisy frequency domain data is converted into noisy time domain data, and the pseudo-random parameters are added to the noisy time domain data to obtain the noisy data.

[0128] In some implementations, obtaining the noise-adding key based on user authorization, and denoising the noisy data based on the noise-adding key to obtain the sensitive data, includes:

[0129] Based on the user's authorization, the noise-adding key and noise seed are sent to the data authorization module in different ways. The noise-adding data is then denoised based on the noise-adding key and the noise seed to obtain the sensitive data.

[0130] In some implementations, the step of adding noise to the sensitive data based on a preset noise-adding key to obtain noisy data includes:

[0131] After the authorization period expires, a new noise-adding key is generated, and the sensitive data is noise-adding processing is performed again.

[0132] In some implementations, storing the noisy data on a blockchain node includes:

[0133] The hash value of the noisy data is stored in the blockchain node.

[0134] In some implementations, obtaining the noise-adding key based on user authorization, and denoising the noisy data based on the noise-adding key to obtain the sensitive data, includes:

[0135] Determine the authorization status based on the following authorization function:

[0136] ;

[0137] in, The output value of the authorization function is represented by θ, which represents the system state vector. Indicates the user's authorization status. This represents the permission weight coefficient, where t represents the current timestamp. Indicates the timestamp of the authorization operation. This represents the time tolerance parameter.

[0138] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the above-described apparatus and its modules and units can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0139] The methods and apparatus of this application can be used in a wide variety of general-purpose or special-purpose computing system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0140] For example, the above-described method and apparatus can be implemented as a computer program, which can be used in, for example... Figure 3 It runs on the computer device shown.

[0141] Please see Figure 3 , Figure 3 This is a schematic block diagram illustrating the structure of a computer device provided in an embodiment of this application. The computer device may be a server or a terminal.

[0142] like Figure 3 As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus, wherein the memory may include a storage medium and internal memory.

[0143] The storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause the processor to perform any blockchain-based data privacy protection method.

[0144] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0145] Internal memory provides an environment for the execution of computer programs stored in storage media. When these computer programs are executed by a processor, the processor can perform any blockchain-based data privacy protection method.

[0146] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0147] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0148] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps:

[0149] Acquire detection data, and use preset data from the detection data as sensitive data;

[0150] The sensitive data is denoised based on a preset denoising key to obtain denoised data;

[0151] The noise-infused data is stored in a blockchain node;

[0152] The noise-adding key is obtained based on the user's authorization, and the noise-adding data is denoised based on the noise-adding key to obtain the sensitive data.

[0153] In some implementations, the step of adding noise to the sensitive data based on a preset noise-adding key to obtain noisy data includes:

[0154] The sensitive data is converted into frequency domain data using Fourier transform;

[0155] The noise parameters are determined based on the noise key and the preset noise seed, and the frequency domain data is noise-added based on the noise parameters to obtain the noise-added data.

[0156] In some implementations, the step of determining noise parameters based on the noise-adding key and a preset noise seed, and then performing noise-adding processing on the frequency domain data based on the noise parameters to obtain the noise-added data, includes:

[0157] The phase noise parameters and amplitude noise parameters are determined based on the noise-adding key;

[0158] The pseudo-random parameters are determined based on the noise seed;

[0159] The frequency domain data is noise-added based on the phase noise parameter, the amplitude noise parameter, and the pseudo-random parameter to obtain the noise-added data.

[0160] In some implementations, determining the phase noise parameter and amplitude noise parameter based on the noise-adding key includes:

[0161] The phase noise parameters and amplitude noise parameters are determined based on the noise-adding key using the following formulas:

[0162]

[0163]

[0164] in, Represents phase noise parameters. The amplitude noise parameter is represented; the noise-adding key is K={α,β, , , , W}, For a random process that follows a complex Gaussian distribution, ~CN(0, ) represents the white noise substrate; It follows a real Gaussian distribution. ~CN(0, ).

[0165] In some implementations, the step of adding noise to the frequency domain data based on the phase noise parameter, the amplitude noise parameter, and the pseudo-random parameter to obtain the noisy data includes:

[0166] The frequency domain data is noise-added using the following formula to obtain noisy frequency domain data:

[0167] ;

[0168] in, This represents the original phase of the frequency domain data;

[0169] The noise-added data is determined based on the pseudo-random parameters of the noise-added frequency domain data.

[0170] In some implementations, determining the noisy data based on the pseudo-random parameters of the noisy frequency domain data includes:

[0171] The noisy frequency domain data is converted into noisy time domain data, and the pseudo-random parameters are added to the noisy time domain data to obtain the noisy data.

[0172] In some implementations, obtaining the noise-adding key based on user authorization, and denoising the noisy data based on the noise-adding key to obtain the sensitive data, includes:

[0173] Based on the user's authorization, the noise-adding key and noise seed are sent to the data authorization module in different ways. The noise-adding data is then denoised based on the noise-adding key and the noise seed to obtain the sensitive data.

[0174] In some implementations, the step of adding noise to the sensitive data based on a preset noise-adding key to obtain noisy data includes:

[0175] After the authorization period expires, a new noise-adding key is generated, and the sensitive data is noise-adding processing is performed again.

[0176] In some implementations, storing the noisy data on a blockchain node includes:

[0177] The hash value of the noisy data is stored in the blockchain node.

[0178] In some implementations, obtaining the noise-adding key based on user authorization, and denoising the noisy data based on the noise-adding key to obtain the sensitive data, includes:

[0179] Determine the authorization status based on the following authorization function:

[0180] ;

[0181] in, The output value of the authorization function is represented by θ, which represents the system state vector. Indicates the user's authorization status. This represents the permission weight coefficient, where t represents the current timestamp. Indicates the timestamp of the authorization operation. This represents the time tolerance parameter.

[0182] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the computer device described above can be referred to the corresponding process in the aforementioned implementation of the blockchain-based data privacy protection system, and will not be repeated here.

[0183] This application also provides a computer-readable storage medium storing a computer program, the computer program including program instructions, and the method implemented when the program instructions are executed can be referred to various embodiments of the blockchain-based data privacy protection system of this application.

[0184] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0185] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0186] It should also be understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. It should be noted that, herein, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system 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 system. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0187] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. The above descriptions are merely specific implementations of this application, but the scope of protection 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 scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A blockchain-based data privacy protection system, characterized in that, The blockchain-based data privacy protection system includes: The sensor module is used to acquire detection data and use preset data in the detection data as sensitive data; A data noise-adding module is used to add noise to the sensitive data based on a preset noise-adding key, resulting in noisy data. The noise-adding key K = {α, β, ...} , , , W}, where α is the phase noise intensity coefficient and β is the amplitude noise intensity coefficient, both of which are scalars; The standard deviation of phase noise, expressed in radians; The standard deviation of amplitude noise is a relative amplitude. The noise center frequency is represented by Hz, and W represents the noise bandwidth. A blockchain storage module is used to store the noise-infused data to a blockchain node; The data authorization module is used to obtain the noise-adding key according to the user's authorization, and to denoise the noise-adding data based on the noise-adding key to obtain the sensitive data; The step of adding noise to the sensitive data based on a preset noise-adding key to obtain noisy data includes: The sensitive data is converted into frequency domain data using Fourier transform; The noise parameters are determined based on the noise key and the preset noise seed, and the frequency domain data is noise-added based on the noise parameters to obtain the noise-added data. The step of determining noise parameters based on the noise-adding key and a preset noise seed, and then performing noise-adding processing on the frequency domain data based on the noise parameters to obtain the noise-added data includes: The phase noise parameters and amplitude noise parameters are determined based on the noise-adding key; The pseudo-random parameters are determined based on the noise seed; The frequency domain data is noise-added based on the phase noise parameter, the amplitude noise parameter, and the pseudo-random parameter to obtain the noise-added data. Determining the phase noise parameters and amplitude noise parameters based on the noise-adding key includes: The phase noise parameters and amplitude noise parameters are determined based on the noise-adding key using the following formulas: in, Represents phase noise parameters. This represents the amplitude noise parameter; the noise-adding key is K={α, β, ... , , , W}, Describes a random process with a white noise basis and a complex Gaussian distribution. ~CN(0, ); Describes a random process with a white noise basis and a complex Gaussian distribution. ~CN(0, ); The step of adding noise to the frequency domain data based on the phase noise parameter, the amplitude noise parameter, and the pseudo-random parameter to obtain the noisy data includes: The frequency domain data is noise-added using the following formula to obtain noisy frequency domain data: in, This represents the original phase of the frequency domain data; The noise-added data is determined based on the pseudo-random parameters of the noise-added frequency domain data.

2. The blockchain-based data privacy protection system according to claim 1, characterized in that, The step of determining the noisy data based on the pseudo-random parameters of the noisy frequency domain data includes: The noisy frequency domain data is converted into noisy time domain data, and the pseudo-random parameters are added to the noisy time domain data to obtain the noisy data.

3. The blockchain-based data privacy protection system according to claim 1, characterized in that, The process of obtaining the noise-adding key based on user authorization, and then denoising the noisy data based on the noise-adding key to obtain the sensitive data includes: Based on the user's authorization, the noise-adding key and noise seed are sent to the data authorization module in different ways. The noise-adding data is then denoised based on the noise-adding key and the noise seed to obtain the sensitive data.

4. The blockchain-based data privacy protection system according to claim 1, characterized in that, The process of adding noise to the sensitive data based on a preset noise-adding key to obtain noisy data includes: After the authorization period expires, a new noise-adding key is generated, and the sensitive data is noise-adding processing is performed again.

5. The blockchain-based data privacy protection system according to claim 1, characterized in that, The step of storing the noisy data to a blockchain node includes: The hash value of the noisy data is stored in the blockchain node.

6. The blockchain-based data privacy protection system according to any one of claims 1-5, characterized in that, The process of obtaining the noise-adding key based on user authorization, and then denoising the noisy data based on the noise-adding key to obtain the sensitive data includes: Determine the authorization status based on the following authorization function: in, The output value of the authorization function is represented by θ, which represents the system state vector. Indicates the user's authorization status. This represents the permission weight coefficient, where t represents the current timestamp. Indicates the timestamp of the authorization operation. This represents the time tolerance parameter.

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