CN115834257B - Cloud power data safety protection method and protection system - Google Patents
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Abstract
The invention provides a cloud power data safety protection method and a protection system, which relate to the technical field of cloud power data safety, wherein in a cloud power data system, a processor processes and stores data generated by a terminal device, marks node addresses of each data, and divides the data generated by the terminal device into a private data block and a common data block; classifying the private data blocks, and carrying out key processing of different degrees on the private data blocks of different categories; performing matrix compression on the private data matrix processed by the key by using a matrix compression algorithm to obtain an interference matrix; and data interference mining is carried out on the interference matrix to form an interference attribute data set, and the interference noise value is used for replacing the interference attribute value of the interference attribute data set, so that the safety of cloud power data transmission and storage is improved.
Description
Technical Field
The invention relates to the technical field of power data safety, in particular to a cloud power data safety protection method and system.
Background
In the traditional IT infrastructure of the power system, network management, storage management and calculation management are three split systems, so that all applications and security policy deployment are caused, and the cooperative coordination of multiple systems and a large amount of manual operation are required. Meanwhile, when network faults or attacks occur in the traditional IT infrastructure of the power system, the traditional IT infrastructure of the power system is only handled in a forced mechanical shutdown or restarting mode, the handling mechanism is simpler, mutual influence among all network units in the power system cannot be ensured, and the attacks or faults cannot be limited within a certain range. Therefore, how to migrate the traditional IT resources to the center of the software defined data ensures that the influence on the currently running service and application is minimized, and meanwhile, the mutual influence among all network units in the power system is also ensured, and the attack or the fault is limited within a certain range, so that the method is a technical difficulty to be studied.
The operation and protection of the power grid equipment is critical to ensure the stability and reliability of the power grid. With the expansion of the grid scale and the higher demands on customer service, the task of operating and maintaining the grid is becoming increasingly more difficult. Traditional operation and protection grid modes have failed to meet increasingly complex demands. In the traditional mode, the mutual compatibility between different department data is poor, and technicians cannot protect the power data in real time because of no method for effectively analyzing and diagnosing the power grid state. Therefore, big data, cloud computing, internet of things and mobile internet technologies are pushing smart grids to develop and build, and developing new operation and protection technologies becomes extremely critical, and is one of the directions of smart grid development.
However, the openness of the wireless communication link also causes the communication process to be extremely vulnerable to data security such as interception by a third party and easy tampering in communication. In addition, the security degree of a key processing algorithm for processing the network layer data key in the traditional technology generally depends on the solving difficulty of a mathematical model, and the communication security cannot be ensured. These problems are very detrimental to wireless sensor networks with limited computational power and small memory space.
In order to ensure the security, particularly the privacy, of the power data in the cloud storage system, the current common method is still based on the traditional data key processing technology, namely, the data after the key processing is simply managed into the cloud storage system by using a certain key processing technology. The existing mechanism has the following problems: the content of the data file does not distinguish the data information, and the same key processing is carried out as a whole; the whole storage of the data file, the user access strategy can not be subdivided, namely the access of the corresponding user to the private data can not be controlled through the access strategy; the data hosting party cannot change the unsafe of the data storage existing in the public cloud; cloud storage devices owned by the power industry are not fully utilized to improve storage security.
Disclosure of Invention
In order to solve the technical problems, the invention provides a cloud power data safety protection method, which comprises the following steps:
s1, processing and storing data generated by a terminal device, and dividing the data generated by the terminal device into a private data block and a common data block;
s2, classifying the private data blocks, and performing key processing on the private data blocks of different categories to different degrees to obtain a private data matrix after the key processing;
s3, performing matrix compression on the private data matrix subjected to the key processing by using a matrix compression algorithm to obtain an interference matrix;
s4, data interference mining is conducted on the interference matrix to form an interference attribute data set, and the interference attribute value in the interference attribute data set is replaced by the interference noise value.
Further, in step S3:
given a set of m columns by n rows of private data matrices
Private data matrix->
Compression by matrix compression algorithm to obtain left matrix of m columns x t rows->
And t columns by n rows of right matrix +.>
：
Wherein the private data matrix
Maximizing compression to left matrix +.>
And right matrix->
Left matrix->
And right matrix->
Compression into a plurality of interference matrices: interference matrix of m columns x a rows->
And a column n row interference matrix->
。
Further, solving the left matrix using an iterative algorithm
And right matrix->
Defining the interference degree as the interference matrix of (1)
Calculated by the following formula:
wherein ,
is private data matrix->
I column x->
Sub-matrices of rows, so that the interference level +.>
Maximum of
、/>
I.e. the interference matrix.
Further, step S4 includes the steps of:
s41, obtaining an interference matrix
、/>
Is->
The last column in (a) is multiplied by the interference matrix
The first row of (2) gets the original interference data +.>
From the original interference data
K data are randomly selected as interference attribute values to form an interference attribute data set: />
； wherein />
Is the j-th interference attribute value in the interference attribute data set.
S42, for each interference attribute value
An interference interval is defined, expressed as:
wherein 
The lower limit and the upper limit of the interference interval are respectively calculated by the following formulas:
wherein ,
for interference attribute data set->
Is the Chinese angelica of (2)And (5) a value. />
S43, obtaining the interference attribute value
Is +.>
The interference noise value is calculated by the following formula +.>
：
Where N is a random number.
S44, the interference noise value
Replacement interference attribute dataset +.>
Interference attribute value ∈>
。
Further, the step S2 specifically includes the following steps:
s21, the private data block is transmitted into a time convolution block, the private data block firstly passes through a time convolution layer in the time convolution block, then the output of the time convolution block is obtained through a ReLU activation function by using batch normalization, the output is transmitted to the next time convolution block, and the process is repeated for 2 times;
s22, entering data output by 3 stacked time convolution blocks into a global average pooling layer and a time recurrent neural network;
s23, the output results of the global average pooling layer and the time recurrent neural network are connected in series and sent to the classification layer for classification, so that private data blocks of different categories are obtained;
s24, carrying out key processing on the private data blocks of different types to different degrees to obtain a private data matrix after the key processing.
The invention also provides a cloud power data safety protection system, which is used for realizing a cloud power data safety protection method, comprising the following steps: the system comprises a power data system, a processor, a terminal device and a cloud processing layer;
in the power data system, the processor processes and stores data generated by the terminal device, and the data generated by the terminal device is divided into a private data block and a common data block;
the cloud processing layer comprises a level classification unit, a matrix compression unit, a replacement unit and a cloud server; the level classification unit classifies the private data blocks, and performs key processing on the private data blocks of different categories to different degrees to obtain a private data matrix after the key processing; the matrix compression unit performs matrix compression on the private data matrix processed by the key by using a matrix compression algorithm to obtain an interference matrix; the replacing unit performs data interference mining on the interference matrix to form an interference attribute data set, and replaces the interference attribute value in the interference attribute data set with the interference noise value.
Further, the processor includes a marking unit that marks a node address of each data according to the data attribute, and a dividing unit that divides the data generated by the terminal device into a private data block and a normal data block according to the marking.
Further, the level classification unit further comprises a key factor control device, a key factor generation device and a key distribution device; the key factor control device is used for generating key factor dividing parameters; the key factor generating device receives the key factor dividing parameter and divides the key factor dividing parameter into a plurality of dividing sub-parameters as a private key factor; the key distribution device sends a plurality of private key factors to each row of the private data matrix according to the serial numbers of each row of the private data matrix, and performs key processing on the private data.
Compared with the prior art, the invention has the following beneficial technical effects:
processing and storing data generated by the terminal device, and dividing the data generated by the terminal device into a private data block and a common data block; classifying the private data blocks, and carrying out key processing of different degrees on the private data blocks of different categories; performing matrix compression on the private data matrix processed by the key by using a matrix compression algorithm to obtain an interference matrix; and data interference mining is carried out on the interference matrix to form an interference attribute data set, and the interference noise value is used for replacing the interference attribute value in the interference attribute data set, so that the transmission and storage safety is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 is a flowchart of a cloud power data security protection method according to the present invention.
Fig. 2 is a flow chart of a method of data interference mining and replacement according to the present invention.
Fig. 3 is a schematic structural diagram of the cloud power data security system according to the present invention.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
In the drawings of the specific embodiments of the present invention, in order to better and more clearly describe the working principle of each element in the system, the connection relationship of each part in the device is represented, but only the relative positional relationship between each element is clearly distinguished, and the limitations on the signal transmission direction, connection sequence and the structure size, dimension and shape of each part in the element or structure cannot be constructed.
As shown in fig. 1, a flowchart of the cloud power data security protection method of the present invention includes the following steps:
s1, in a cloud power data system, a processor processes and stores data generated by a terminal device, and the data generated by the terminal device is divided into a private data block and a common data block.
S11, carrying out index storage on data generated by the terminal device, and marking node addresses of each data by adopting a keyword identification technology.
The index is stored as an additional index table is built while the data node information is stored. The index table is composed of a plurality of index entries. If each node has an index entry in the index table, the index table is referred to as a dense index. The general form of the index entry is: key + address. The key is a data item that can uniquely mark a node, and the address of the index item in the dense index indicates the storage location where the node is located.
S12, reading the node address of the data generated by the terminal device, and judging the node address as the starting position of the private data block or the starting position of the common data block according to the marking information of the node address.
S13, sequentially reading the next node address until the marking information of the next node address is different from the marking information of the starting position, and dividing the data to generate a private data block and a common data block.
When the marking information of the next node address is different from the marking information of the starting position, the data corresponding to the two positions are described as belonging to the data with different attributes, so that the node address is required to be used as a demarcation point for segmentation.
Preferably, the data file generated by each terminal device can be compressed into a plurality of data blocks, and the original file can be restored only when any at least half of the data blocks are received; the original file can be restored when any half or less of the data blocks are lost or damaged, so that the reliability and usability are improved; meanwhile, when any less than half of data blocks are stolen, the data blocks cannot be restored into the original file, so that the safety is improved. If all the data blocks are stored in the cloud processing layer, the protection system can still acquire the data privacy information.
S2, classifying the private data blocks, and carrying out key processing of different degrees on the private data blocks of different categories.
The processor receives the private data block from the cloud processing layer, and performs level classification processing on the private data block so as to facilitate later key processing.
Preferably, the data is classified using the LSTM-FCN as follows.
S21, the private data block is transmitted into a time convolution block, the private data block passes through a time convolution layer in the time convolution block, then batch normalization is performed, then the output of the time convolution block is obtained through a ReLU activation function, the output is transmitted to the next time convolution block as input, and the process is repeated for 2 times.
S22, private data blocks with 3 stacked time convolution blocks enter a global average pooling layer and a time recurrent neural network.
S23, the global average pooling layer and the output of the time recurrent neural network are connected in series and sent to the classification layer for classification, and then private data blocks of different categories are obtained.
S24, carrying out key processing on the private data blocks of different types to different degrees, obtaining a private data matrix after the key processing, and storing the private data matrix.
Dividing the private data blocks into private data blocks with different levels according to different categories, and performing key processing with different degrees according to the levels of the private data blocks to obtain a private data matrix after the key processing, wherein each row of the private data matrix represents a key processing level.
In a preferred embodiment, key factor dividing parameters are generated by the key factor control device, each key factor dividing parameter corresponding to a key processing level; the key factor dividing parameter is received by the key factor generating device and divided into a plurality of dividing subparameters, the dividing subparameters are used as the private key factors, and the private key factors are sent to each row of the private data matrix by the key distributing device according to the serial numbers of each row of the private data matrix, so that the private data of the row is subjected to key processing.
And S3, performing matrix compression on the private data matrix subjected to the key processing by using a matrix compression algorithm to obtain an interference matrix.
The matrix compression algorithm compresses the private data matrix into the product of a left matrix and a right matrix, wherein one column vector in the private data matrix is the weighted sum of all column vectors in the left matrix, and the weight coefficient is an element in a corresponding column vector in the right matrix. The matrix compression algorithm based on iterative computation has the characteristics of high convergence rate and small left and right matrix storage space capacity.
Specifically, a private data matrix of a set of m columns by n rows is given
Private data matrix->
Compressing by a matrix compression algorithm to obtain a left matrix and a right matrix: left matrix of m columns x t rows +.>
And t columns by n rows of right matrix +.>
：
Wherein the value of t is selected to satisfy (m+n) t<mn, then the private data matrix
Can be maximally compressed into a left matrix +.>
And right matrix->
Left matrix->
Comprises a basis vector which constitutes a t-dimensional space, right matrix +.>
Is a private data matrix +.>
Projection of the corresponding column vector in the t-dimensional space. Left matrix->
And right matrix->
And can be compressed into a plurality of interference matrices, i.e. m columns x a rows of interference matrices +.>
And a column x n row interference matrix
。/>
Solving left matrix using iterative algorithm
And right matrix->
Defining the interference degree as the interference matrix of (1)
Calculated by the following formula:
wherein ,
is private data matrix->
I column x->
Sub-matrices of rows, so that the interference level +.>
Maximum of
、/>
I.e. the interference matrix.
S4, interference matrix
、/>
And performing data interference mining to form an interference attribute data set, and replacing the interference attribute value of the interference attribute data set with the interference noise value.
In order to improve the prediction accuracy and the privacy security level, the invention performs data interference mining based on a random interference method, wherein the random interference method comprises four processing steps, and the random interference method is applied to a protection stage of data interference mining, as shown in fig. 2, and specifically comprises the following steps:
s41, obtaining an interference matrix
、/>
Is->
The last column in (a) is multiplied by the interference matrix
The first row of (2) gets the original interference data +.>
From the original interference data
K data are randomly selected as interference attribute values to form an interference attribute data set of +.>
； wherein />
Is the j-th interference attribute value in the interference attribute data set.
S42, defining an interference attribute interference interval.
For each interference attribute value
An interference interval is defined, expressed as:
wherein 
The lower limit and the upper limit of the interference attribute interference interval are respectively calculated by the following formulas:
wherein ,
for interference attribute dataset +.>
Is included in the above formula (c).
S43, generating random interference data.
Based on the two steps, the interference attribute value can be obtained
Is +.>
The interference noise value is calculated by the following formula +.>
：
Where N is a random number.
S44, the interference noise value
Replacement interference attribute dataset +.>
Interference attribute value ∈>
。/>
The random scrambling technology can camouflage the original data through the random noise, so that the privacy of users in the power data system can be protected.
Fig. 3 is a schematic structural diagram of a cloud power data security system according to the present invention, where the security system includes: the system comprises a power data system, a processor, a terminal device and a cloud processing layer.
In the power data system, a processor processes and stores data generated by a terminal device, and the data generated by the terminal device is divided into a private data block and a common data block;
the processor comprises a marking unit and a dividing unit, wherein the marking unit marks the node address of each data according to the data attribute, and the dividing unit is used for dividing the data generated by the terminal device into a private data block and a common data block.
The cloud processing layer comprises a level classification unit, a matrix compression unit, a replacement unit and a cloud server.
The level classification unit classifies the private data blocks, and performs key processing on the private data blocks of different categories to different degrees to obtain a private data matrix after the key processing.
In a preferred embodiment, the level classification unit further comprises key factor control means, key factor generation means and key distribution means. The key factor control device is used for generating key factor dividing parameters, and each key factor dividing parameter corresponds to one key processing level; and receiving the private key factors corresponding to each key processing level sent by the key factor generating device, wherein the private key factors are generated by key factor dividing parameters, and the key distributing device gives the private key factors to each row of the private data matrix.
The key factor generation device receives the key factor dividing parameter, divides the key factor dividing parameter into a plurality of dividing subparameters, and sends a plurality of private key factors to the key distribution device as the private key factors; the key distribution device sends a plurality of private key factors to each row of the private data matrix according to the serial numbers of each row of the private data matrix, so that the private data of the row is subjected to key processing.
And the matrix compression unit performs matrix compression on the private data matrix processed by the key by using a matrix compression algorithm to obtain an interference matrix.
And the replacing unit is used for carrying out data interference mining on the interference matrix to form an interference attribute data set, and replacing the interference attribute value in the interference attribute data set with the interference noise value.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted across a computer-readable storage medium. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (4)
1. The cloud power data safety protection method is characterized by comprising the following steps of:
s1, processing and storing data generated by a terminal device, and dividing the data generated by the terminal device into a private data block and a common data block;
s2, classifying the private data blocks, and performing key processing on the private data blocks of different categories to different degrees to obtain a private data matrix after the key processing;
step S2 includes the steps of:
s21, a private data block is transmitted into a time convolution block, the private data block firstly passes through a time convolution layer in the time convolution block, then the output of the time convolution block is obtained through an activation function by using batch normalization, and the output is transmitted to the next time convolution block;
s22, entering data output by 3 stacked time convolution blocks into a global average pooling layer and a time recurrent neural network;
s23, the output results of the global average pooling layer and the time recurrent neural network are connected in series and sent to the classification layer for classification, so that private data blocks of different categories are obtained;
s24, carrying out key processing on the private data blocks of different types to different degrees to obtain a private data matrix after the key processing;
s3, performing matrix compression on the private data matrix subjected to the key processing by using a matrix compression algorithm to obtain an interference matrix;
s4, carrying out data interference mining on the interference matrix to form an interference attribute data set, and replacing an interference attribute value in the interference attribute data set with an interference noise value;
step S4 includes the steps of:
s41, obtaining an interference matrix
、/>
Is->
The last column of (2) multiplied by the interference matrix +.>
The first row of (2) gets the original interference data +.>
From the original interference data
K data are randomly selected as interference attribute values to form an interference attribute data set: />
； wherein />
A j-th interference attribute value in the interference attribute data set;
s42, regarding the interference attribute value
Define an interference interval +.>
Expressed as:
wherein 
The lower limit and the upper limit of the interference interval are respectively calculated by the following formulas:
wherein ,
for interference attribute data set->
Is a normalized value of (2);
s43, obtaining the interference attribute value
Is +.>
Thereafter, the dry matter is calculated by the following formulaNoise value->
：
Wherein N is a random number;
s44, using interference noise value
Replacement interference attribute dataset +.>
Interference attribute value ∈>
。
2. A cloud power data security protection system for implementing the cloud power data security protection method as set forth in claim 1, comprising: the system comprises a power data system, a processor, a terminal device and a cloud processing layer;
in the power data system, the processor processes and stores data generated by the terminal device, and the data generated by the terminal device is divided into a private data block and a common data block;
the cloud processing layer comprises a level classification unit, a matrix compression unit, a replacement unit and a cloud server; the level classification unit classifies the private data blocks, and performs key processing on the private data blocks of different categories to different degrees to obtain a private data matrix after the key processing; the matrix compression unit performs matrix compression on the private data matrix processed by the key by using a matrix compression algorithm to obtain an interference matrix; the replacing unit performs data interference mining on the interference matrix to form an interference attribute data set, and replaces the interference attribute value in the interference attribute data set with the interference noise value.
3. The cloud power data security protection system according to claim 2, wherein the processor comprises a marking unit and a dividing unit, the marking unit marks a node address of each data, and the dividing unit is used for dividing the data generated by the terminal device into a private data block and a common data block according to the marking.
4. The cloud power data security protection system of claim 2, wherein the level classification unit further comprises a key factor control means, a key factor generation means, and a key distribution means; the key factor control device is used for generating key factor dividing parameters; the key factor generating device receives the key factor dividing parameter and divides the key factor dividing parameter into a plurality of dividing sub-parameters as a private key factor; the key distribution device sends a plurality of private key factors to each row of the private data matrix according to the serial numbers of each row of the private data matrix, and performs key processing on the private data of each row.
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