Noise-based data encryption method, device, equipment and storage medium

Abstract

The present disclosure relates to a data encryption method, device, equipment and storage medium based on noise, the method comprising: obtaining data to be processed, screening out single point data, time series data and type data of target type from the data to be processed; based on the noise constructors corresponding to the single point data, the time series data and the type data, respectively determining the first noise value corresponding to the single point data, the second noise value corresponding to the time series data and the third noise value corresponding to the type data; and performing privacy encryption on the single point data, the time series data and the type data based on the first noise value, the second noise value and the third noise value. The present disclosure sets different noise constructors for different data to be processed to construct different noises, and completes data encryption based on different noises, which can more accurately meet the privacy protection requirements of various types of data, thereby improving the privacy protection effect.

Description

Technical Field

[0001] The present disclosure relates to the technical field of data encryption, and in particular to a noise-based data encryption method, device, equipment and storage medium. Background Art

[0002] With the acceleration of digitalization and the increase in data volume, the importance of data privacy is also increasing. Data privacy refers to protecting the privacy rights of individuals or organizations in the digital age and ensuring that their data will not be accessed, used, disclosed, tampered with or destroyed by unauthorized persons during collection, processing, storage and transmission. Especially for some sensitive data, how to ensure data privacy is a technical problem that needs to be solved. Summary of the invention

[0003] In order to solve the above technical problems, the present disclosure provides a noise-based data encryption method, device, equipment and storage medium.

[0004] A first aspect of an embodiment of the present disclosure provides a noise-based data encryption method, the method comprising:

[0005] Acquire the data to be processed, and filter out single point data, time series data, and type data of a target type from the data to be processed;

[0006] Based on the noise constructors corresponding to the single point data, the time series data and the type data, respectively, determine a first noise value corresponding to the single point data, a second noise value corresponding to the time series data and a third noise value corresponding to the type data;

[0007] The single point data, the time series data and the type data are privacy encrypted based on the first noise value, the second noise value and the third noise value respectively.

[0008] Optionally, the determining, based on the noise constructors corresponding to the single point data, the time series data, and the type data, respectively a first noise value corresponding to the single point data, a second noise value corresponding to the time series data, and a third noise value corresponding to the type data, comprises:

[0009] Generate random numbers based on a preset probability distribution;

[0010] The random number is substituted into the noise constructors corresponding to the single point data, the time series data and the type data respectively to obtain a first noise value corresponding to the single point data, a second noise value corresponding to the time series data and a third noise value corresponding to the type data.

[0011] Optionally, the random number includes a first random number, and the step of substituting the random number into noise constructors corresponding to the single point data, the time series data, and the type data, respectively, to obtain a first noise value corresponding to the single point data, a second noise value corresponding to the time series data, and a third noise value corresponding to the type data includes:

[0012] The first random number, the first statistical sensitivity corresponding to the single point data, and the first privacy protection strength are substituted into a first noise constructor corresponding to the single point data to obtain a first noise value corresponding to the single point data.

[0013] Optionally, the random number includes a second random number, and the step of substituting the random number into noise constructors corresponding to the single point data, the time series data, and the type data, respectively, to obtain a first noise value corresponding to the single point data, a second noise value corresponding to the time series data, and a third noise value corresponding to the type data includes:

[0014] Substitute the second random number, the second statistical sensitivity and the second privacy protection strength corresponding to the time series data into the second noise constructor corresponding to the time series data to obtain a second noise value corresponding to the time series data.

[0015] Optionally, the random number includes a third random number, and the step of substituting the random number into noise constructors corresponding to the single point data, the time series data, and the type data, respectively, to obtain a first noise value corresponding to the single point data, a second noise value corresponding to the time series data, and a third noise value corresponding to the type data includes:

[0016] For each type of data, calculating the importance score corresponding to the type of data;

[0017] The third random number, the importance score, the score sensitivity corresponding to the type data, and the third privacy protection strength are substituted into a third noise constructor corresponding to the type data to obtain a third noise value corresponding to the type data.

[0018] Optionally, calculating the importance score corresponding to the type of data includes:

[0019] Based on the type data, the weight corresponding to the target type associated with the type data, and the pre-acquired bias item, the first adjustment factor, the second adjustment factor, and the third adjustment factor, an importance score corresponding to the type data is calculated.

[0020] Optionally, the preset probability distribution includes at least one of uniform distribution, normal distribution, and gamma distribution.

[0021] A second aspect of an embodiment of the present disclosure provides a noise-based data encryption device, the device comprising:

[0022] A screening module is used to obtain the data to be processed, and screen out single point data, time series data and type data of target type from the data to be processed;

[0023] a determination module, configured to determine, based on noise constructors corresponding to the single point data, the time series data, and the type data, a first noise value corresponding to the single point data, a second noise value corresponding to the time series data, and a third noise value corresponding to the type data;

[0024] An encryption module is used to perform privacy encryption on the single point data, the time series data and the type data based on the first noise value, the second noise value and the third noise value respectively.

[0025] A third aspect of an embodiment of the present disclosure provides a computer device, comprising a memory and a processor, and a computer program, wherein the computer program is stored in the memory, and when the computer program is executed by the processor, the noise-based data encryption method as described in the first aspect above is implemented.

[0026] A fourth aspect of an embodiment of the present disclosure provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the noise-based data encryption method as described in the first aspect above is implemented.

[0027] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has the following advantages:

[0028] In the noise-based data encryption method, apparatus, device and storage medium provided in the embodiments of the present disclosure, by acquiring the data to be processed, single point data, time series data and type data of the target type are screened out from the data to be processed, and based on the noise constructors corresponding to the single point data, the time series data and the type data respectively, the first noise value corresponding to the single point data, the second noise value corresponding to the time series data and the third noise value corresponding to the type data are respectively determined, and the single point data, the time series data and the type data are privacy encrypted based on the first noise value, the second noise value and the third noise value respectively, and different noise constructors can be set for different data to be processed to construct different noises, and data encryption is completed based on different noises, so as to more accurately meet the privacy protection requirements of various types of data, thereby improving the privacy protection effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0031] Figure 1 is a flow chart of a noise-based data encryption method provided by an embodiment of the present disclosure;

[0032] Figure 2 is a flow chart of a method for determining a noise value provided by an embodiment of the present disclosure;

[0033] Figure 3 is a structural schematic diagram of a noise-based data encryption device provided by an embodiment of the present disclosure;

[0034] Figure 4 It is a structural diagram of a computer device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0035] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0036] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0037] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0038] Figure 1 The flowchart of a noise-based data encryption method provided by an embodiment of the present disclosure is shown in FIG. 1 . The method can be executed by a noise-based data encryption device, which can be implemented in software and / or hardware. The noise-based data encryption device can be configured in an electronic device, such as a server or a terminal, wherein the terminal specifically includes a mobile phone, a computer or a tablet computer, etc. Figure 1 As shown, the noise-based data encryption method provided in this embodiment includes the following steps:

[0039] S101, obtaining data to be processed, and filtering out single point data, time series data, and type data of target type from the data to be processed.

[0040] The data to be processed in the embodiments of the present disclosure can be understood as data that requires privacy protection. For example, the data to be processed can be meteorological data, hydrological data, geological data, etc. collected from the target water system basin, which is not limited here.

[0041] The single-point data in the embodiments of the present disclosure may be understood as independent data collected separately, such as the temperature and pollution level of the target water system, etc., which are not limited here.

[0042] The time series data in the embodiments of the present disclosure can be understood as multiple data collected at different times within a period of time, such as water quality data of a target water system basin within a period of time, etc., which is not limited here.

[0043] The target type in the embodiment of the present disclosure can be understood as multiple data types determined based on preset classification standards, such as environment type, river type, water quality type, etc., which are not limited here.

[0044] In the disclosed embodiment, the noise-based data encryption device can obtain the data to be processed that needs to be privacy protected, and filter and extract single-point data, time series data, and type data of target types from the data to be processed.

[0045] In an exemplary implementation of the disclosed embodiment, the noise-based data encryption device can traverse the data to be processed when screening single-point data and time series data, and analyze the data format, the continuity of the associated timestamps, and the data change trend in the data to be processed that are associated with timestamps, and determine the data to be processed that have the same data format, continuous or fixed time intervals in the associated timestamps, and obvious trends, periodicity, or correlations as a group of time series data, and determine the remaining data to be processed as single-point data.

[0046] In another exemplary implementation of the disclosed embodiment, the noise-based data encryption device can determine one or more data tags corresponding to each target type when screening type data of the target type, and screen the data to be processed that carries these data tags in the data to be processed, determine it as type data, and determine the target type of each type of data based on the correspondence between the carried data tags and the target type.

[0047] S102, based on the noise constructors corresponding to the single point data, the time series data and the type data respectively, determine a first noise value corresponding to the single point data, a second noise value corresponding to the time series data and a third noise value corresponding to the type data.

[0048] In the embodiment of the present disclosure, the noise-based data encryption device can, after filtering out single-point data, time series data and type data from the data to be processed, generate a first noise value corresponding to the single-point data, a second noise value corresponding to the time series data, and a third noise value corresponding to the type data according to the noise constructors corresponding to the three types of data to be processed that are pre-acquired.

[0049] S103. Perform privacy encryption on single-point data, time series data, and type data based on the first noise value, the second noise value, and the third noise value, respectively.

[0050] In the disclosed embodiments, the noise-based data encryption device can add the first noise value to the single-point data, add the second noise value to the time series data, and add the third noise value to the type data after the first noise value, the second noise value, and the third noise value are generated, thereby realizing privacy encryption of the single-point data, the time series data, and the type data, wherein the noise-based data encryption device can use a differential privacy algorithm to add noise, or can use other algorithms to add noise, which is not limited here.

[0051] The disclosed embodiment obtains the data to be processed, filters out single-point data, time series data and type data of the target type from the data to be processed, determines a first noise value corresponding to the single-point data, a second noise value corresponding to the time series data and a third noise value corresponding to the type data based on the noise constructors corresponding to the single-point data, the time series data and the type data respectively, performs privacy encryption on the single-point data, the time series data and the type data based on the first noise value, the second noise value and the third noise value respectively, can set different noise constructors for different data to be processed to construct different noises, completes data encryption based on different noises, more accurately meets the privacy protection requirements of various types of data, and thus improves the privacy protection effect.

[0052] Figure 2 is a flow chart of a method for determining a noise value provided by an embodiment of the present disclosure, such as Figure 2 As shown, based on the above embodiment, the noise value can be determined by the following method.

[0053] S201. Generate a random number based on a preset probability distribution.

[0054] The preset probability distribution in the embodiment of the present disclosure may include at least one of uniform distribution, normal distribution, and gamma distribution.

[0055] In the embodiments of the present disclosure, the noise-based data encryption device can generate multiple random numbers according to a preset probability distribution when it is necessary to construct a noise value. Specifically, the probability distribution parameters of probability distributions such as uniform distribution, normal distribution, and gamma distribution can be obtained, such as the value range of uniform distribution, the mean and variance of normal distribution, the shape parameters and scale parameters of gamma distribution, etc., and the corresponding probability distribution can be constructed based on the obtained probability distribution parameters to generate multiple random numbers.

[0056] S202, respectively substitute random numbers into the noise constructors corresponding to the single point data, the time series data and the type data to obtain a first noise value corresponding to the single point data, a second noise value corresponding to the time series data and a third noise value corresponding to the type data.

[0057] In the embodiment of the present disclosure, the noise-based data encryption device can substitute the random number into the noise constructors corresponding to the single-point data, time series data and type data respectively after the random number is generated, to obtain the first noise value corresponding to the single-point data, the second noise value corresponding to the time series data and the third noise value corresponding to the type data.

[0058] Optionally, the random numbers generated based on a preset probability distribution include a first random number, and the noise-based data encryption device can substitute the first random number, the first statistical sensitivity corresponding to the single-point data, and the first privacy protection strength into a first noise constructor corresponding to the single-point data to obtain a first noise value corresponding to the single-point data.

[0059] Among them, the statistical sensitivity is used to measure the maximum change that may occur in the output result of the statistical function (such as the summation function) when a single data point in the input data of the statistical function changes. The statistical sensitivity can be manually preset or calculated by numerical difference method, gradient algorithm or other methods, which are not limited here. The first statistical sensitivity can be understood as the statistical sensitivity when a single point data is used as the input data of the statistical function.

[0060] Specifically, the first noise constructor can be expressed as follows:

[0061]

[0062] Among them, Noise 1 is the first noise value, ∈ 1 is the first privacy protection strength, Δf 1 is the first statistical sensitivity, U 1 (0,1),U 2 (0,1) is the first random number that follows uniform distribution, N(0,σ 2 ) has a mean of 0 and a variance of σ 2is the first random number that obeys the normal distribution, and Γ(k,θ) is the first random number that obeys the gamma distribution with shape parameter k and scale parameter θ.

[0063] Optionally, the random numbers generated based on a preset probability distribution include a second random number. The noise-based data encryption device can substitute the second random number, the second statistical sensitivity corresponding to the time series data, and the second privacy protection strength into a second noise constructor corresponding to the time series data to obtain a second noise value corresponding to the time series data.

[0064] The second statistical sensitivity can be understood as the statistical sensitivity when the time series data is used as the input data of the statistical function.

[0065] Specifically, the second noise constructor can be expressed as follows:

[0066]

[0067] Among them, Noise 2 is the second noise value, F -1 is the inverse Fourier transform, F(x ′ (t)) is the data point x in the time series data at time t ′ The Fourier transform of (t), ∈ 2 is the second privacy protection strength, Δf 2 is the sensitivity of the second statistic, U 1 (0,1),U 2 (0,1) is a second random number that obeys a uniform distribution, and Γ(k,θ) is a second random number that obeys a gamma distribution with a shape parameter of k and a scale parameter of θ.

[0068] Optionally, the random numbers generated based on a preset probability distribution include a third random number. The noise-based data encryption device can calculate the importance score corresponding to each type of data, and substitute the third random number, the importance score, the score sensitivity corresponding to the type data, and the third privacy protection strength into a third noise constructor corresponding to the type data to obtain a third noise value corresponding to the type data.

[0069] Among them, the score sensitivity is used to measure the maximum change that may occur in the output result of the importance score function when a single data point in the input data of the importance score function changes. The importance score sensitivity can be manually preset or calculated by numerical difference method, gradient algorithm or other methods, which is not limited here. The score sensitivity can be understood as the score sensitivity when the type data is used as the input data of the importance score function.

[0070] Specifically, the third noise constructor can be expressed as follows:

[0071]

[0072] Among them, Noise 3 is the third noise value, X is the set of all types of data, q(x) is the importance score of the type of data, ∈ 3 is the third privacy protection strength, Δq is the score sensitivity, U 1 (0,1),U 2 (0,1) is the third random number that follows uniform distribution, N(0,σ 2 ) has a mean of 0 and a variance of σ 2 is the third random number that obeys the normal distribution, and Γ(k,θ) is the third random number that obeys the gamma distribution with shape parameter k and scale parameter θ.

[0073] Optionally, the noise-based data encryption device can calculate the importance score corresponding to the type data based on the type data, the weight corresponding to the target type associated with the type data, and the pre-acquired bias item, the first adjustment factor, the second adjustment factor and the third adjustment factor when calculating the importance score.

[0074] Specifically, the importance score function can be expressed as follows:

[0075]

[0076] Among them, q(x) is the importance score of type data x, θ ′ is the weight corresponding to the target type associated with the type data x, which is used to perform linear combination with the type data x to measure the influence of each type data x on the importance score prediction result, T is the matrix transpose, b is the bias item corresponding to the target type associated with the type data, α is the first adjustment factor, β is the second adjustment factor, and γ is the third adjustment factor.

[0077] The disclosed embodiment generates random numbers based on a preset probability distribution, and substitutes the random numbers into the noise constructors corresponding to the single-point data, the time series data, and the type data, respectively, to obtain a first noise value corresponding to the single-point data, a second noise value corresponding to the time series data, and a third noise value corresponding to the type data. The privacy protection strength is introduced when constructing the noise, and the protection strength can be flexibly set for different types of data to be processed. The introduction of statistical sensitivity or score sensitivity can more accurately control the impact of noise on the data to be processed, which is convenient for users to set relevant parameters according to actual needs, and further improves the privacy protection effect of the data to be processed.

[0078] Figure 3 Schematic diagram of a noise-based data encryption device provided by an embodiment of the present disclosure. Figure 3As shown, the noise-based data encryption device 300 includes: a screening module 310, a determination module 320, and an encryption module 330, wherein the screening module 310 is used to obtain the data to be processed, and screen out single-point data, time series data, and type data of the target type from the data to be processed; the determination module 320 is used to determine the first noise value corresponding to the single-point data, the second noise value corresponding to the time series data, and the third noise value corresponding to the type data based on the noise constructors corresponding to the single-point data, the time series data, and the type data respectively; the encryption module 330 is used to perform privacy encryption on the single-point data, the time series data, and the type data based on the first noise value, the second noise value, and the third noise value.

[0079] Optionally, the determination module 320 includes: a generation unit for generating random numbers based on a preset probability distribution; and a calculation unit for substituting the random numbers into noise constructors corresponding to the single point data, the time series data and the type data, respectively, to obtain a first noise value corresponding to the single point data, a second noise value corresponding to the time series data and a third noise value corresponding to the type data.

[0080] Optionally, the random number includes a first random number, and the calculation unit is specifically used to substitute the first random number, the first statistical sensitivity corresponding to the single point data, and the first privacy protection strength into a first noise constructor corresponding to the single point data to obtain a first noise value corresponding to the single point data.

[0081] Optionally, the random number includes a second random number, and the calculation unit is specifically used to substitute the second random number, the second statistical sensitivity and the second privacy protection strength corresponding to the time series data into a second noise constructor corresponding to the time series data to obtain a second noise value corresponding to the time series data.

[0082] Optionally, the random number includes a third random number, and the calculation unit includes: a first calculation subunit, used to calculate the importance score corresponding to the type data for each type of data; a second calculation subunit, used to substitute the third random number, the importance score, and the score sensitivity and third privacy protection strength corresponding to the type data into a third noise constructor corresponding to the type data to obtain a third noise value corresponding to the type data.

[0083] Optionally, the first calculation subunit is specifically used to calculate the importance score corresponding to the type data based on the type data, the weight corresponding to the target type associated with the type data, and the pre-acquired bias item, the first adjustment factor, the second adjustment factor and the third adjustment factor.

[0084] Optionally, the preset probability distribution includes at least one of uniform distribution, normal distribution, and gamma distribution.

[0085] The noise-based data encryption device provided in this embodiment can execute the method described in any of the above embodiments. Its execution method and beneficial effects are similar and will not be repeated here.

[0086] Figure 4 It is a structural diagram of a computer device provided in an embodiment of the present disclosure.

[0087] like Figure 4 As shown, the computer device may include a processor 410 and a memory 420 storing computer program instructions.

[0088] Specifically, the processor 410 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0089] The memory 420 may include a large capacity memory for information or instructions. By way of example and not limitation, the memory 420 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a tape, or a universal serial bus (USB) drive or a combination of two or more of these. Where appropriate, the memory 420 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 420 may be inside or outside the integrated gateway device. In a particular embodiment, the memory 420 is a non-volatile solid-state memory. In a particular embodiment, the memory 420 includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (Electrically Erasable Programmable ROM, EEPROM), an electrically rewritable ROM (EAROM) or a flash memory, or a combination of two or more of these.

[0090] The processor 410 reads and executes the computer program instructions stored in the memory 420 to perform the steps of the noise-based data encryption method provided in the embodiment of the present disclosure.

[0091] In one example, the computer device may further include a transceiver 430 and a bus 440. Figure 4 As shown, the processor 410, the memory 420 and the transceiver 430 are connected via a bus 440 and communicate with each other.

[0092] The bus 440 includes hardware, software, or both. For example, but not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an InfiniBand 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 Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, the bus 440 may include one or more buses. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.

[0093] The embodiments of the present disclosure also provide a computer-readable storage medium, which may store a computer program. When the computer program is executed by a processor, the processor implements the noise-based data encryption method provided by the embodiments of the present disclosure.

[0094] The above-mentioned storage medium may, for example, include a memory 420 of computer program instructions, and the above-mentioned instructions may be executed by the processor 410 of the noise-based data encryption device to complete the noise-based data encryption method provided by the embodiment of the present disclosure. Optionally, the storage medium may be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium may be a ROM, a random access memory (Random Access Memory, RAM), a compact disc read-only memory (Compact Disc ROM, CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device, etc. The above-mentioned computer program may be written in any combination of one or more programming languages ​​to perform the program code of the operation of the embodiment of the present disclosure, and the programming language includes an object-oriented programming language, such as Java, C++, etc., and also includes a conventional procedural programming language, such as "C" language or similar programming language. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device, partially on the remote computing device, or completely on the remote computing device or server.

[0095] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0096] The above description is only a specific embodiment of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be 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 disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A noise-based data encryption method, characterized in that: The method comprises: Acquire the data to be processed, and filter out single point data, time series data, and type data of a target type from the data to be processed; Based on the noise constructors corresponding to the single point data, the time series data and the type data, respectively, determine a first noise value corresponding to the single point data, a second noise value corresponding to the time series data and a third noise value corresponding to the type data; The single point data, the time series data and the type data are privacy encrypted based on the first noise value, the second noise value and the third noise value respectively.

2. The method according to claim 1, characterized in that: The method of determining a first noise value corresponding to the single point data, a second noise value corresponding to the time series data, and a third noise value corresponding to the type data based on the noise constructors corresponding to the single point data, the time series data, and the type data, respectively, includes: Generate random numbers based on a preset probability distribution; The random number is substituted into the noise constructors corresponding to the single point data, the time series data and the type data respectively to obtain a first noise value corresponding to the single point data, a second noise value corresponding to the time series data and a third noise value corresponding to the type data.

3. The method according to claim 2, characterized in that The random number includes a first random number, and the random number is substituted into the noise constructors corresponding to the single point data, the time series data, and the type data, respectively, to obtain a first noise value corresponding to the single point data, a second noise value corresponding to the time series data, and a third noise value corresponding to the type data, including: The first random number, the first statistical sensitivity corresponding to the single point data, and the first privacy protection strength are substituted into a first noise constructor corresponding to the single point data to obtain a first noise value corresponding to the single point data.

4. The method according to claim 2, characterized in that: The random number includes a second random number, and the random number is substituted into the noise constructors corresponding to the single point data, the time series data, and the type data, respectively, to obtain a first noise value corresponding to the single point data, a second noise value corresponding to the time series data, and a third noise value corresponding to the type data, including: Substitute the second random number, the second statistical sensitivity and the second privacy protection strength corresponding to the time series data into the second noise constructor corresponding to the time series data to obtain a second noise value corresponding to the time series data.

5. The method according to claim 2, characterized in that: The random number includes a third random number, and the random number is substituted into the noise constructors corresponding to the single point data, the time series data, and the type data, respectively, to obtain a first noise value corresponding to the single point data, a second noise value corresponding to the time series data, and a third noise value corresponding to the type data, including: For each type of data, calculating the importance score corresponding to the type of data; The third random number, the importance score, the score sensitivity corresponding to the type data, and the third privacy protection strength are substituted into a third noise constructor corresponding to the type data to obtain a third noise value corresponding to the type data.

6. The method according to claim 5, characterized in that The calculating the importance score corresponding to the type of data includes: Based on the type data, the weight corresponding to the target type associated with the type data, and the pre-acquired bias item, the first adjustment factor, the second adjustment factor, and the third adjustment factor, an importance score corresponding to the type data is calculated.

7. The method according to claim 2, characterized in that The preset probability distribution includes at least one of uniform distribution, normal distribution, and gamma distribution.

8. A noise-based data encryption device, characterized in that: include: A screening module is used to obtain the data to be processed, and screen out single point data, time series data and type data of target type from the data to be processed; a determination module, configured to determine, based on noise constructors corresponding to the single point data, the time series data, and the type data, a first noise value corresponding to the single point data, a second noise value corresponding to the time series data, and a third noise value corresponding to the type data; An encryption module is used to perform privacy encryption on the single point data, the time series data and the type data based on the first noise value, the second noise value and the third noise value respectively.

9. A computer device, characterized in that: include: Memory; processor; and a computer program; wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the noise-based data encryption method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the noise-based data encryption method according to any one of claims 1 to 7 is implemented.

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