CN116707803A - Private data crushing method based on data encryption - Google Patents
Private data crushing method based on data encryption Download PDFInfo
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- CN116707803A CN116707803A CN202310980031.7A CN202310980031A CN116707803A CN 116707803 A CN116707803 A CN 116707803A CN 202310980031 A CN202310980031 A CN 202310980031A CN 116707803 A CN116707803 A CN 116707803A
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- 238000000034 method Methods 0.000 title claims abstract description 120
- 238000010298 pulverizing process Methods 0.000 claims description 8
- 238000007781 pre-processing Methods 0.000 abstract description 2
- 238000004904 shortening Methods 0.000 abstract description 2
- 230000008569 process Effects 0.000 description 17
- 230000000694 effects Effects 0.000 description 12
- 230000000739 chaotic effect Effects 0.000 description 9
- 238000004364 calculation method Methods 0.000 description 7
- 230000010354 integration Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
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Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/08—Key distribution or management, e.g. generation, sharing or updating, of cryptographic keys or passwords
- H04L9/0816—Key establishment, i.e. cryptographic processes or cryptographic protocols whereby a shared secret becomes available to two or more parties, for subsequent use
- H04L9/085—Secret sharing or secret splitting, e.g. threshold schemes
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/30—Public key, i.e. encryption algorithm being computationally infeasible to invert or user's encryption keys not requiring secrecy
- H04L9/3006—Public key, i.e. encryption algorithm being computationally infeasible to invert or user's encryption keys not requiring secrecy underlying computational problems or public-key parameters
- H04L9/302—Public key, i.e. encryption algorithm being computationally infeasible to invert or user's encryption keys not requiring secrecy underlying computational problems or public-key parameters involving the integer factorization problem, e.g. RSA or quadratic sieve [QS] schemes
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/50—Reducing energy consumption in communication networks in wire-line communication networks, e.g. low power modes or reduced link rate
Abstract
The invention relates to the technical field of data processing, in particular to a privacy data crushing method based on data encryption, which comprises the following steps: the method comprises the steps of obtaining privacy data to be encrypted, preprocessing the data, dividing the data into modules, calculating the data confusion degree of each module to obtain the data confusion degree of each module, obtaining merging gains of merging of two adjacent modules according to the data confusion degree of the modules, selectively merging the data modules according to the merging gains, calculating a key weight according to the data confusion degree of the merged modules, obtaining a key length according to the key weight, obtaining a public key according to the key length, and encrypting by utilizing an RSA encryption algorithm. According to the invention, through analyzing the data sample, selecting a proper key length, balancing the encryption time and the security, and shortening the encryption time as much as possible under the condition of enough security.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a privacy data crushing method based on data encryption.
Background
With the continuous development of information technology, security and privacy problems are also more and more prominent. In order to protect personal privacy, data encryption techniques have been developed. Data encryption refers to converting plaintext data into ciphertext data through some algorithm to protect confidentiality and integrity of the data. The privacy data in the plain text is hidden and converted into ciphertext data through a data encryption technology, so that other people cannot intuitively obtain any useful information from the ciphertext data, and the aim of crushing the privacy data is fulfilled.
The traditional asymmetric encryption algorithm is an RSA algorithm, the RSA algorithm directly encrypts the whole sample data, and if the sample data is too long, the encryption time is too long, so that the encryption efficiency is affected. Therefore, the data needs to be subjected to grouping processing, and the ciphertext effect of encrypting grouping data with different lengths is different, the longer the data grouping length is, the higher the security of the ciphertext is, but the longer the encryption time is; the shorter the data length, the shorter the encryption time, but the lower the security of the ciphertext.
Disclosure of Invention
The invention provides a privacy data crushing method based on data encryption, which aims to solve the existing problems.
The privacy data crushing method based on data encryption adopts the following technical scheme:
an embodiment of the present invention provides a method for pulverizing private data based on data encryption, including the steps of:
the method comprises the steps of obtaining data to be encrypted, dividing the data into a plurality of modules, and obtaining the data confusion degree of the modules according to the different character types and the occurrence frequency of different characters in the modules;
obtaining important data modules according to the data confusion degree of the modules, combining all the modules by using two module combining methods, namely a first module combining method and a second module combining method, wherein the first module combining method is used for combining according to the important data modules and the data confusion degree of adjacent modules; the second module merging method is to merge all modules among important data modules;
the method comprises the steps that adjacent important data modules in all important data modules are respectively marked as a first module and a second module, when the first module utilizes a first module merging method, a first minimum value and a first maximum value of module index values in all modules participating in merging are obtained, a section formed by the first minimum value and the first maximum value is used as a merging section of the first module, when the second module utilizes a second module merging method, a second minimum value and a second maximum value of module index values in all modules participating in merging are obtained, and a section formed by the second minimum value and the second maximum value is used as a merging section of the second module;
obtaining merging gains of merging two adjacent important data modules in the important data modules according to the data confusion degree of the modules in the merging interval, and selecting a module merging method to merge all the modules according to the merging gains to obtain a merging module sequence;
marking any one merging module in the merging module sequence as a first merging module, and obtaining the key weight of the first merging module according to the data confusion degree of the sub-modules in the first merging module;
obtaining the key length of the first merging module according to the key weight of the first merging module, obtaining a public key according to the key length, and encrypting the data needing to be encrypted by utilizing the public key.
Further, the obtaining the data confusion degree of the module according to the different character types and the occurrence frequency of different characters in the module comprises the following specific steps:
in the method, in the process of the invention,indicate->Degree of data confusion for individual modules,/>Indicate->Different character types number in each module, +.>Representing the +.sup.th in the number of different character categories>Probability of occurrence of individual characters, < >>As a logarithmic function.
Further, the obtaining the important data module according to the data confusion degree of the module comprises the following specific steps:
presetting a confusion degree threshold, acquiring the data confusion degree of all modules in the module sequence L1, marking any one module in the module sequence L1 as a target module, and taking the target module as an important data module when the data confusion degree of the target module is smaller than the confusion degree threshold.
Further, the specific method of the first module merging method is as follows:
acquiring an important data module sequence L2 formed by all important data modules in the module sequence L1, wherein the important data module sequence L2 is the first oneA critical data module by comparison of +.>The data confusion degree of two modules adjacent to each other on the left and right of each important data module, and the module with small data confusion degree is related to the first->Merging the important data modules; calculating the data confusion degree of the combined modules, if the confusion degree of the combined modules is smaller than the confusion degree threshold value, continuing to combine the modules with small data confusion degree according to the data confusion degree of the two left and right adjacent modules until the confusion degree of the combined modules is larger than or equal to the confusion degree threshold value, and stoppingAnd stopping merging.
Further, the specific method of the second module merging method is as follows:
and acquiring an important data module sequence L2 formed by all important data modules in the module sequence L1, acquiring two adjacent important data modules in the important data module sequence L2, and merging the modules corresponding to the two adjacent important data modules between the module sequences L1, wherein the modules between the two adjacent important data modules and the module sequence L1 are required to be merged together during merging.
Further, the step of obtaining the merging gain of the merging of two adjacent important data modules in the important data modules according to the data confusion degree of the modules in the merging interval comprises the following specific steps:
in the method, in the process of the invention,representing the merging gain of merging two adjacent important data modules in the important data module sequence L2; />Representing a module index value of +.>When the first module merging method is used, the minimum value of the module index values in all the modules participating in merging; />Representing a module index value of +.>When the first module merging method is utilized, the maximum value of module index values in all the modules participating in merging; />Is indicated as +.>Middle->The degree of data confusion of the individual modules; />Representing a module index value of +.>When the second module merging method is used, the minimum value of the module index values in all the modules participating in merging; />Representing a module index value of +.>When the second module merging method is used, the maximum value of module index values in all the modules participating in merging; />Is indicated as +.>Middle->The degree of data confusion of the individual modules;a module index value representing the module sequence L1 of two adjacent important data modules in the important data module sequence L2; />Is indicated as +.>Middle->Data of individual modulesDegree of confusion.
Further, the method for combining all modules according to the combination gain comprises the following specific steps:
presetting a gain thresholdIf->，/>The merging gain of the merging of two adjacent important data modules in the important data module sequence L2 is represented, the data modules are merged by using a first module merging method, and if +_, the data modules are merged>And merging the data modules by using a second module merging method, selecting and merging the modules in the module sequence L1, and recording the sequence of the merged modules to obtain a merged module sequence.
Further, the key weight of the first merging module is obtained according to the data confusion degree of the sub-modules in the first merging module, and the method comprises the following specific steps:
in the method, in the process of the invention,key weight representing the first merge module, < ->Representing the number of submodules in the first merge module,/-, for example>Representing the +.>The degree of data confusion of the sub-modules, which represent all the modules participating in the merging when merging into the first merging module.
Further, the key length of the first merging module according to the key weight of the first merging module comprises the following specific steps:
in the method, in the process of the invention,key length representing first merge module, +.>Key weight representing the first merge module, < ->Representing the number of sub-modules within the first merge module.
The technical scheme of the invention has the beneficial effects that: when data is encrypted by using an RSA algorithm, the encryption time becomes very long due to overlong key selection length; if the key is too short, the security is excessively reduced and is easily broken by violence. By analyzing the data samples, selecting a proper key length, balancing encryption time and security, and shortening the encryption time as much as possible under the condition of enough security.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of steps of a method for pulverizing private data based on data encryption.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description refers to the specific implementation, structure, characteristics and effects of the privacy data crushing method based on data encryption according to the invention by combining the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the private data crushing method based on data encryption provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart illustrating a method for data encryption-based private data shredding according to an embodiment of the present invention is shown, the method includes the following steps:
and S001, acquiring the privacy data needing to be encrypted, and preprocessing the data.
First, private data that needs to be encrypted is acquired.
The privacy data includes, but is not limited to, the following:
(1) Personal identification information such as name, identification number, telephone number, email, etc.;
(2) Financial information such as bank account numbers, credit card numbers, financial tables, etc.;
(3) Medical health information such as cases, diagnostic reports, drug prescriptions, etc.;
(4) Geographic location information such as GPS position fixes, mobile device location information, and the like.
The above information belongs to data with comparative privacy, and since the data information is mostly character information, and the information needs to be digitized in an encryption algorithm, all the privacy data information needing to be encrypted needs to be converted into unicode code by a unicode coding method to obtain coded data, and the coded data is simply described as data for convenience of expression.
Thus, the privacy data needing to be encrypted is obtained and converted into unicode codes, and the code data is obtained.
It should be noted that, when encrypting a data sample, the length of the key is related to the length of the data sample, and the value of the key needs to be slightly larger than the value of the data sample, so the longer the data sample length, the longer the key length. In the encryption process, the length of the data sample affects the length of the key, so that the encryption time and encryption security of the data are affected. The longer the data packet length, the longer the key length, the longer the encryption time, and the higher the security; the shorter the data packet length, the shorter the key length, the shorter the encryption time, and the lower the security.
According to the embodiment, the security and the encryption time of the data samples in different groups are comprehensively analyzed, the data samples are adaptively grouped, the security and the encryption time during data encryption are weighed, certain security is lost, and the encryption time is greatly shortened.
And step S002, dividing the data into modules, and calculating the data confusion degree of each module to obtain the data confusion degree of each module.
In encrypting data, the privacy data to be encrypted is also important to different degrees. According to the chaotic degree of the data, importance calculation is carried out on the data, the more important data needs to be encrypted by using a safer key in the encryption process, the data with lower importance can be encrypted by using a key with lower security, and the encryption time consumed when the data is encrypted by using the high-security key can be saved.
Specifically, every 100 data are divided into a module, when the last module is less than 100, zero padding operation is performed, a module sequence L1 can be obtained by dividing the data into modules and recording the sequence of the modules, and according to the data confusion degree of each module, the method specifically comprises the following steps:
in the module sequence L1The modules are exemplified by->The degree of data confusion of the individual modules is:
in the method, in the process of the invention,indicate->The data disorder degree of each module is in the range of +.>；/>Indicate->The number of different character types in 100 data in each module; />Representing the +.sup.th in the number of different character categories>Probability of occurrence of individual characters;as a logarithmic function. Wherein (1)>Indicate->The degree of confusion of 100 data in each module, the more regular the data, the +.>The smaller the value of (2); />The more uniform the probability of occurrence of the individual charactersThe larger the value of (2), the more chaotic the data; />The more non-uniform the probability of occurrence of the individual characters, the moreThe smaller the value of (2), the more regular the data; />Is->Normalized result of (2), so->The value range of (2) is +.>The more chaotic the data, the +.>The larger the value of (C) is, the more regular the data isThe smaller the value of (2);
further, calculating the data confusion degree of all modules, wherein the module with small data confusion degree indicates that the more regular the data of the module is, the lower the uncertainty of the data is, and the more important the data is; a module with a large degree of data confusion indicates that the more chaotic the data of the module, the higher the uncertainty of the data and the less important the data.
Thus, the data confusion degree of any one module is obtained.
And step S003, obtaining merging gains of two adjacent modules according to the data confusion degree of the modules, and selectively merging the data modules according to the merging gains.
In the process of encrypting the private data, since the data importance of each module is different, the data in the module needs to be encrypted by using a safer key for the module with great importance, and the security of the key is related to the length of the key, and the longer the length of the key is, the higher the security of the key is. The length of the key is related to the length of the data sample to be encrypted, and the longer the length of the data sample to be encrypted is, the longer the length of the key is, the higher the encryption security is. Therefore, the data modules with great importance need to be combined, so that the more important data sample length is longer, and the security in encryption is higher. Step S002 obtains the importance of each module, selectively merges the data in the modules according to the importance of the modules, i.e. merges the data with higher importance as much as possible, and after merging the important data into some modules, reduces the discrete distribution of the data with higher importance in each merged module. After important data are integrated into some modules, the important data can be encrypted by utilizing data with higher security in a centralized manner, so that the situation that the key security required by all the modules is higher due to the fact that the important data are discretely distributed into a plurality of integrated modules is avoided, and therefore the calculation amount is large, the encryption time is longer as the calculation amount is large, and therefore the calculation amount can be reduced by selectively integrating the important data, and the encryption time is further reduced.
Specifically, a first module merging method comprises the following steps: a chaotic degree threshold is preset, and in this embodiment, the chaotic degree threshold is taken asThe implementation may be set to other values, and no fixed limitation is made here. Obtaining all modules in the module sequence L1 by using the data confusion degree calculation model of the modules in the step S002The degree of data confusion is that any one module in the module sequence L1 is marked as a target module;
and when the data confusion degree of the target module is smaller than the confusion degree threshold value, the target module is used as an important data module. Acquiring an important data module sequence L2 formed by all important data modules in the module sequence L1, wherein the first important data module sequence L2 isBy way of example, the important data module is provided by comparison +.>The data confusion degree of two modules adjacent to each other on the left and right of each important data module, and the module with small data confusion degree is related to the first->The important data modules are combined, and it is required to specifically explain that the data confusion degree of two modules adjacent to each other on the left and right is not less than a confusion degree threshold value, and the smaller the data confusion degree is, the more important data is explained, so that the more important data is combined;
for the modules with the data confusion degree of the target module being larger than or equal to the confusion degree threshold value, if the data importance of the modules is lower, the data confusion degrees of the two adjacent modules are not combined according to the data confusion degree of the two adjacent modules;
if at firstThe important data modules are the first module or the last module in the module sequence L1, and are respectively judged and combined with the second module or the penultimate model in the module sequence L1;
calculating the data confusion degree of the combined modules, if the confusion degree of the combined modules is smaller than the confusion degree threshold, continuing to combine the modules with small data confusion degree according to the data confusion degree of the two left and right adjacent modules until the confusion degree of the combined modules is larger than or equal to the confusion degree threshold, and stopping combining;
when the i-th important data module is merged, the next important data module of the merged module is subjected to the merging operation.
It should be noted that, in the above first module merging method for merging data modules, in order to obtain an optimal merging manner, another method for merging data modules is provided, and in order to facilitate differentiation, another method for merging data modules is denoted as a second module merging method, where the second module merging method is specifically as follows:
specifically, a chaotic degree threshold is preset, and in this embodiment, the chaotic degree threshold is taken asThe implementation may be set to other values, and no fixed limitation is made here. The method comprises the steps of utilizing a data confusion degree calculation model of modules in step S002 to obtain the data confusion degree of all the modules in a module sequence L1, recording any one of the modules in the module sequence L1 as a target module, taking the target module as an important data module when the data confusion degree of the target module is smaller than a confusion degree threshold value, considering that the data importance of the target module is lower when the data confusion degree of the target module is larger than or equal to the confusion degree threshold value, not performing specific processing, obtaining an important data module sequence L2 formed by all the important data modules in the module sequence L1, obtaining two adjacent important data modules in the important data module sequence L2, merging the modules corresponding to the module sequence L1 between the two adjacent important data modules, and if the two adjacent important data modules are merged, then not participating in merging when the two adjacent important data modules are merged.
It should be noted that, when important modules are integrated together as much as possible, it is necessary to consider that some modules with lower importance exist in the process of integrating important modules and also need to be integrated together, so that it is necessary to consider that the integration gain of integrating important modules, that is, the integration gain of integrating important modules in the second method is greater when the important data in the integration process is greater than that of integrating important data in the first method, but if many data with lower importance exist in the integration process, the gain effect is lower, even if negative gain occurs, the important data is selectively integrated, so that the calculation amount is reduced, and the encryption time is further reduced.
Specifically, the merging gain of merging two adjacent important data modules in the important data module sequence L2 is specifically as follows:
in the method, in the process of the invention,representing the merging gain of merging two adjacent important data modules in the important data module sequence L2; />Representing a module index value of +.>When the first module merging method is used, the minimum value of the module index values in all the modules participating in merging; />Representing a module index value of +.>When the first module merging method is utilized, the maximum value of module index values in all the modules participating in merging; />Is indicated as +.>Middle->Degree of data confusion for individual modules；/>Representing a module index value of +.>When the second module merging method is used, the minimum value of the module index values in all the modules participating in merging; />Representing a module index value of +.>When the second module merging method is used, the maximum value of module index values in all the modules participating in merging; />Is indicated as +.>Middle->The degree of data confusion of the individual modules;a module index value representing the module sequence L1 of two adjacent important data modules in the important data module sequence L2; />Is indicated as +.>Middle->The degree of data confusion of the individual modules.
Wherein, the liquid crystal display device comprises a liquid crystal display device,representing the merging gain of two adjacent modules in all important data modulesThe value range isThe larger the gain, the higher the necessity to merge the two modules; />Indicating that the module index value is +.>The module of (1) is merged with the merging interval->Average degree of data confusion of (a);indicating that the module index value is +.>Merging intervals when modules are mergedAverage degree of data confusion of (a); />Representing the module index value of +.>And->The average data confusion degree of the merging intervals when the modules are merged; />Indicating +.>Average degree of data confusion of (a); />The lower the value of (2) the more important the description data is, the more data can be merged, and therefore +.>The smaller the value of (2) the better; />The effect of combining the data modules by the first module combining method is compared with the effect of combining the data modules by the second module combining method, and the +.>The larger the value of (c) the better the gain effect of combining with the second module combining method, the +.>The smaller the value of (c) the worse the gain effect of combining with the second module combining method; />Comparison of data length representing two module merging methods, +.>The larger the value of (c) the better the gain effect of combining with the second module combining method, the +.>The smaller the value of (c) the worse the gain effect of combining with the first module combining method.
Further, the data modules are selectively combined according to the combination gain to obtain a combination module sequence, which is specifically as follows:
presetting a gain thresholdIn this embodiment, the gain threshold value is +.>To mention, other values can be set at the time of implementation, if +.>，/>Indicating the merging gain of the merging of two adjacent important data modules in the important data module sequence L2, the first module merging method has better merging effect, and is used for merging the data modules, if ∈two>The second module merging method has better merging effect, the second module merging method is used for merging the data modules, and the special explanation is that the modules in the module sequence L1 cannot be merged by the first module merging method and the second module merging method, and the modules with lower data importance are merged.
And selecting and combining the modules in the module sequence L1, and recording the combined module sequence to obtain a combined module sequence.
And selectively combining the data modules according to the combining gain.
And S004, calculating the key weight according to the data confusion degree of the combined modules.
It should be noted that, after the important data modules are combined, the chaotic degree of each module after the combination is made to be greater than the threshold B, in the combining process, the importance degree of each module is different, and for the more important modules, a longer key is required to be used for encryption, so as to ensure that the encryption security is higher. The key length weight is calculated through the data confusion degree of each module in the merging process, so that the key length of the module formed by merging more important keys is longer. Step S003 selectively merges the data blocks to lengthen the data sample length of the important data block, and also lengthens the key length required for encrypting the partial data.
Specifically, the key weight is obtained according to the data confusion degree of the combined modules, and the key weight is specifically as follows:
taking any one merging module in the merging module sequence as an example, calculating the key weight of any one merging module, and marking any one merging module as a first merging module for convenience of explanation;
in the method, in the process of the invention,the key weight value of the first merging module is represented as +.>，/>Representing the number of sub-modules within the first merge module, said sub-modules representing all modules participating in the merge when merging into the first merge module,/for>Representing the +.>The degree of data confusion of the sub-modules. Wherein->The number of submodules representing any one merging module is within the value range of +.>The greater the number of sub-modules required for merging, the +.>The larger the value of (2), the fewer the number of submodules required for merging, then +.>The smaller the value of (2); />Representing the importance weight of the combined module, the higher the importance of the module is +.>The larger the value of (2), the lower the importance of the module, then +.>The smaller the value of (2). In summary, the greater the number of sub-modules of the merging module, the higher the importance, the greater the key weight; the fewer the number of sub-modules of the merge module, the lower the importance, the smaller the weight of the key.
Thus, the key weight is obtained.
Step S005, obtaining the key length according to the key weight, obtaining the public key according to the key length and encrypting by using an RSA encryption algorithm.
Specifically, step S004 obtains the key weight of the first merging module in the merging module sequence, and obtains the key length of the first merging module according to the key weight of the first merging module and the data length in the first merging module, which is specifically as follows:
in the method, in the process of the invention,key length representing first merge module, +.>Key weight representing the first merge module, < ->Representing the number of sub-modules within the first merge module. />Represents the key length weight, the value range is +.>；/>Representing the total length of the data of the first merge module.
Further, the key parameters are calculated according to the key length and encrypted by using an RSA encryption algorithm, and the method specifically comprises the following steps:
in the method, in the process of the invention,key length representing first merge module, +.>For the key parameter of the first merge module, +.>For->And (5) rounding upwards. Further, a minimum key parameter satisfying the above formula is obtained, and the minimum key parameter is noted +.>Utilize->The public key of the first merge module is determined, it being noted that +.>Determining that the public key is the prior art, not specifically described in this embodiment, obtaining the public keys of all the merging modules in the merging module sequence, encrypting the private data by using the RSA encryption algorithm, and completing the encryptionThe data can be hidden after the data, and the aim of crushing the private data can be fulfilled.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (9)
1. The method for crushing the private data based on the data encryption is characterized by comprising the following steps:
the method comprises the steps of obtaining data to be encrypted, dividing the data into a plurality of modules, and obtaining the data confusion degree of the modules according to the different character types and the occurrence frequency of different characters in the modules;
obtaining important data modules according to the data confusion degree of the modules, combining all the modules by using two module combining methods, namely a first module combining method and a second module combining method, wherein the first module combining method is used for combining according to the important data modules and the data confusion degree of adjacent modules; the second module merging method is to merge all modules among important data modules;
the method comprises the steps that adjacent important data modules in all important data modules are respectively marked as a first module and a second module, when the first module utilizes a first module merging method, a first minimum value and a first maximum value of module index values in all modules participating in merging are obtained, a section formed by the first minimum value and the first maximum value is used as a merging section of the first module, when the second module utilizes a second module merging method, a second minimum value and a second maximum value of module index values in all modules participating in merging are obtained, and a section formed by the second minimum value and the second maximum value is used as a merging section of the second module;
obtaining merging gains of merging two adjacent important data modules in the important data modules according to the data confusion degree of the modules in the merging interval, and selecting a module merging method to merge all the modules according to the merging gains to obtain a merging module sequence;
marking any one merging module in the merging module sequence as a first merging module, and obtaining the key weight of the first merging module according to the data confusion degree of the sub-modules in the first merging module;
obtaining the key length of the first merging module according to the key weight of the first merging module, obtaining a public key according to the key length, and encrypting the data needing to be encrypted by utilizing the public key.
2. The method for pulverizing private data based on data encryption according to claim 1, wherein the obtaining the data confusion degree of the module according to the number of different character types and the occurrence frequency of different characters in the module comprises the following specific steps:
in (1) the->Indicate->Degree of data confusion for individual modules,/>Indicate->Different character types number in each module, +.>Representing the +.sup.th in the number of different character categories>Probability of occurrence of individual characters, < >>As a logarithmic function.
3. The method for pulverizing private data based on data encryption according to claim 1, wherein the obtaining the important data module according to the data confusion degree of the module comprises the following specific steps:
presetting a confusion degree threshold, acquiring the data confusion degree of all modules in the module sequence L1, marking any one module in the module sequence L1 as a target module, and taking the target module as an important data module when the data confusion degree of the target module is smaller than the confusion degree threshold.
4. The private data shredding method based on data encryption according to claim 1, wherein the specific method of the first module merging method is as follows:
acquiring an important data module sequence L2 formed by all important data modules in the module sequence L1, wherein the important data module sequence L2 is the first oneA critical data module by comparison of +.>The data confusion degree of two modules adjacent to each other on the left and right of each important data module, and the module with small data confusion degree is related to the first->Merging the important data modules; and calculating the data confusion degree of the combined modules, if the confusion degree of the combined modules is smaller than the confusion degree threshold, continuing to combine the modules with small data confusion degree according to the data confusion degree of the two left and right adjacent modules until the confusion degree of the combined modules is larger than or equal to the confusion degree threshold, and stopping combining.
5. The private data crushing method based on data encryption according to claim 1, wherein the specific method of the second module merging method is as follows:
and acquiring an important data module sequence L2 formed by all important data modules in the module sequence L1, acquiring two adjacent important data modules in the important data module sequence L2, and merging the modules corresponding to the two adjacent important data modules between the module sequences L1, wherein the modules between the two adjacent important data modules and the module sequence L1 are required to be merged together during merging.
6. The method for pulverizing private data based on data encryption according to claim 1, wherein the step of obtaining the combining gain of combining two adjacent important data modules in the important data modules according to the data confusion degree of the modules in the combining interval comprises the following specific steps:
in (1) the->Representing the merging gain of merging two adjacent important data modules in the important data module sequence L2; />Representing a module index value of +.>When the first module merging method is used, the minimum value of the module index values in all the modules participating in merging; />Representing a module index value of +.>When the first module merging method is utilized, the maximum value of module index values in all the modules participating in merging; />Is expressed in the merging sectionMiddle->The degree of data confusion of the individual modules; />Representing a module index value of +.>When the second module merging method is used, the minimum value of the module index values in all the modules participating in merging; />Representing a module index value of +.>When the second module merging method is used, the maximum value of module index values in all the modules participating in merging; />Is expressed in the merging sectionMiddle->The degree of data confusion of the individual modules; />A module index value representing the module sequence L1 of two adjacent important data modules in the important data module sequence L2; />Is indicated as +.>Middle->The degree of data confusion of the individual modules.
7. The method for pulverizing private data based on data encryption according to claim 1, wherein the step of selecting a module combining method according to the combining gain to combine all modules to obtain a combined module sequence comprises the following specific steps:
presetting a gain thresholdIf->，/>The merging gain of the merging of two adjacent important data modules in the important data module sequence L2 is represented, the data modules are merged by using a first module merging method, and if +_, the data modules are merged>And merging the data modules by using a second module merging method, selecting and merging the modules in the module sequence L1, and recording the sequence of the merged modules to obtain a merged module sequence.
8. The method for pulverizing private data based on data encryption according to claim 1, wherein the step of obtaining the key weight of the first merging module according to the data confusion degree of the sub-modules in the first merging module comprises the following specific steps:
in (1) the->Key weight representing the first merge module, < ->Representing the number of submodules in the first merge module,/-, for example>Representing the +.>The degree of data confusion of the sub-modules, which represent all the modules participating in the merging when merging into the first merging module.
9. The method for pulverizing private data based on data encryption according to claim 1, wherein the key length of the first merging module according to the key weight of the first merging module comprises the following specific steps:
in (1) the->Key length representing first merge module, +.>Key weight representing the first merge module, < ->Representing the number of sub-modules within the first merge module.
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