CN115190217B - Data security encryption method and device integrating self-coding network - Google Patents
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
- H04N1/44—Secrecy systems
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- G06F21/602—Providing cryptographic facilities or services
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
- H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
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- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/60—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding
Abstract
The invention discloses a data security encryption method and device fused with a self-coding network, and relates to the technical field of internet data processing. The invention aims to overcome the defects of large expenditure of mass data storage resources, unsafe data transmission and low data transmission efficiency of a large number of pictures when the existing data are encrypted safely, and the method comprises the steps of adopting a text encryption module to encrypt text type data, constructing a picture self-coding network model, and adopting a picture compression module to pre-compress the original picture type data to be encrypted; the picture compression coding is encrypted by adopting a picture encryption module, text ciphertext data or picture ciphertext data which is required to be applied to a downstream task is decrypted by adopting a decryption module, the decrypted picture compression coding is reconstructed and restored by adopting a picture reconstruction module, and the decoder obtains reconstructed picture type data after the codeword is reconstructed. The invention is mainly used for mass data transmission.
Description
Technical Field
The invention relates to the technical field of internet data processing, in particular to a data security encryption method and device.
Background
In recent years, along with rapid development of information technologies such as cloud computing, big data, internet of things and the like and digital transformation of traditional industries, data sources and data quantity are increasing at unprecedented speeds. The data is also an important research topic as a novel production factor, and the safety of the data is also an important research topic. Encryption processing is one of effective means for ensuring data security during data storage and transmission. However, the existing data encryption technology does not distinguish the types of the data to be encrypted, both the picture data and the text data adopt the same data processing mode and encryption mode, and a large amount of encrypted data can be generated during the transmission of the picture encryption, and when the technology is oriented to the mass data containing a large amount of pictures, the problems of high storage resource overhead, low transmission efficiency and the like of encrypted ciphertext data exist.
Therefore, a method and a device for data security encryption integrated with a self-coding network, which can cope with mass data containing a large number of pictures, are safe in data transmission and high in transmission efficiency, are needed.
Disclosure of Invention
The invention aims to solve the defects of high cost, unsafe data transmission and low data transmission efficiency of mass data storage resources containing a large number of pictures in the conventional data security encryption process, and provides a data security encryption method and device which can cope with mass data containing a large number of pictures, safe data transmission and high transmission efficiency and are fused with a self-coding network.
The invention discloses a data security encryption method fused with a self-coding network, which comprises the following steps:
s1, dividing all data to be processed into original text type data and original picture type data;
s2, encrypting the original text type data in the data to be encrypted by adopting a text encryption module to obtain text ciphertext data;
s3, constructing a picture self-coding network model, and pre-compressing original picture type data to be encrypted by adopting a picture compression module to obtain picture compression codes; the picture self-coding network model comprises an encoder and a decoder, wherein the encoder compresses and codes picture type data to form code words;
s4, encrypting the picture compression codes by adopting a picture encryption module to obtain picture ciphertext data;
s5, transmitting or storing the text ciphertext data and the picture ciphertext data according to user requirements;
s6, decrypting the text ciphertext data or the picture ciphertext data which are required to be applied to the downstream task by adopting a decryption module to obtain decrypted text type data and decrypted picture compression codes;
s7, reconstructing and restoring the decrypted picture compression codes by adopting a picture reconstruction module, and obtaining reconstructed picture type data by the decoder after the code words are reconstructed.
Further: in S2 and S4, the encryption processing method adopted by the text encryption module and the picture encryption module includes a symmetric encryption algorithm and/or an asymmetric encryption algorithm.
Further: in S2, in S3, the encoder section includes an input layer, a convolution layer, and a feature extraction layer, and the decoder includes a convolution layer, a feature extraction layer, and an output layer.
Further: in S3, the pre-compression process adopted by the picture compression module includes the following steps:
s31, taking the original picture type data as input of the encoder part, and defining depth, width and height of the original picture type data;
s32, enabling the defined picture type data to pass through a convolution layer, performing linear transformation and coding on a convolved result twice, and splicing the result after the linear transformation twice and the result after the linear transformation once to obtain a convolution splicing result once;
and S33, the primary convolution splicing result is subjected to secondary convolution layer passing to ensure that the depth is unchanged, the secondary convolution result is spliced with the primary convolution result and subjected to linear transformation, and the codeword with the depth unchanged, the width and the height reduced to 1/8 of the original picture type data is obtained.
Further: in S7, the reconstruction processing of the picture reconstruction module includes the following steps:
s71, inputting the code word into a convolution layer for one-time convolution after linear transformation and matrix transformation to obtain a one-time convolution result;
s72, performing characteristic extraction on the primary convolution result, and then performing linear transformation and matrix transformation again to obtain a primary transformation result;
s73, performing linear transformation and matrix transformation again after performing twice feature extraction on the primary convolution result to obtain a secondary transformation result;
s74, performing linear transformation and matrix transformation again after performing three-time feature extraction on the primary convolution result to obtain three-time transformation results;
s75, splicing the secondary linear transformation result and the tertiary linear transformation result, and convoluting the spliced result for two times to obtain a secondary convolution result;
and S76, splicing the primary convolution result and the secondary convolution result, and convolving the spliced result to obtain a reconstructed picture with the same dimensionality as the original picture type data.
Further: in S7, the cost function of the picture self-coding network model is a mean square error of the reconstructed picture and the original picture type data.
Further: in S2, the feature extraction layer includes dynamic position coding, a multihop relational aggregator, and a feed forward neural network.
The invention relates to a data security encryption device, which comprises a text encryption module, a picture compression module, a picture encryption module, a decryption module and a picture reconstruction module;
the text encryption module is used for encrypting the original text type data so as to obtain text ciphertext data;
the picture compression module is used for pre-compressing the original picture type data so as to obtain picture compression codes;
the picture encryption module is used for encrypting the picture compression codes so as to obtain picture ciphertext data;
the decryption module is used for decrypting the text ciphertext data or the picture ciphertext data which are required to be applied to the downstream task according to the user requirement, and obtaining decrypted text type data and decrypted picture compression codes;
the picture reconstruction module is used for reconstructing and restoring the picture compression coding, so as to obtain reconstructed picture type data.
The beneficial effects of the invention are as follows:
the data security encryption method integrating the self-coding network introduces the self-coding network in the data security encryption process aiming at the characteristics of mass data scale, various types and the like of the Internet, and reduces the bandwidth resource and hardware equipment requirements in the storage and transmission processes while realizing the data security encryption. A self-coding model UniFormer-based AutoEncoder (self-coding network model) comprising encoder and decoder structures is autonomously built based on the UniFormer (Unified Transformer, unified trans-former) network. In the process of encrypting the picture type data to be encrypted, compression coding preprocessing is added, the picture type data is compressed and coded into a low-dimensional codeword in advance by using a model encoder part, the original picture type data is rebuilt by using a model decoder part in a decryption stage, the picture type ciphertext data is decrypted and rebuilt and restored before being used for a subsequent downstream task, the reconstruction result is nearly lossless, and the storage and transmission resources and bandwidth cost of the data can be reduced while the safety of the data is ensured.
Drawings
FIG. 1 is a schematic block diagram of an Encoder Encoder;
FIG. 2 is a schematic block diagram of a Decoder;
FIG. 3 is a schematic diagram of the structure of a coding unit;
FIG. 4 is a schematic diagram of the structure of an encoder;
fig. 5 is a schematic block diagram of a data security encryption device.
Detailed Description
The following preferred embodiments of the present invention are provided, but the scope of the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present invention should be included in the scope of the present invention. The examples described below are only for the purpose of illustrating the invention and should not be construed as limiting the invention, which is intended to be covered by the claims. The following detailed description of embodiments of the invention is provided for convenience in describing the invention and simplifying the description, and technical terms used in the description of the invention should be construed broadly, including but not limited to conventional alternatives not mentioned in the present application, including both direct implementation and indirect implementation.
Example 1
The embodiment will be described with reference to fig. 1 to 5, and the data security encryption method disclosed in the embodiment includes the following steps:
s1, dividing all data to be processed into original text type data and original picture type data;
s2, encrypting the original text type data in the data to be encrypted by adopting a text encryption module to obtain text ciphertext data;
s3, constructing a picture self-coding network model, and pre-compressing original picture type data to be encrypted by adopting a picture compression module to obtain picture compression codes; the picture self-coding network model comprises an encoder and a decoder, wherein the encoder compresses and codes picture type data to form code words; and encoding the picture type data in the data to be encrypted by utilizing an encoder part of the trained Uniformer-based AutoEncoder model to obtain codeword information after the picture type data is compressed and encoded.
S4, encrypting the picture compression codes by adopting a picture encryption module to obtain picture ciphertext data; encrypting the processed picture compression codes;
s5, transmitting or storing the text ciphertext data and the picture ciphertext data according to user requirements;
s6, decrypting the text ciphertext data or the picture ciphertext data which are required to be applied to the downstream task by adopting a decryption module to obtain decrypted text type data and decrypted picture compression codes; the original text type data and the coded data after the picture type text processing can be obtained through the decryption module. The encryption and decryption methods used are all existing algorithms, and if an asymmetric encryption algorithm RSA is adopted in S2 and S4, the algorithm is also used for decryption. The RSA algorithm generates a pair of collocated public and private keys, uses the public key to encrypt data, and uses the private key to decrypt the data.
S7, reconstructing and restoring the decrypted picture compression codes by adopting a picture reconstruction module, and obtaining reconstructed picture type data by the decoder after the code words are reconstructed.
And reconstructing and restoring the compressed picture by using the constructed picture self-coding network. And calling a decoder part of the trained Uniformer-based AutoEncoder model, and coding the picture type data obtained after decryption as input to obtain the reconstructed picture type data. Thus, the ciphertext data decryption is completed.
Example 2
In S2 and S4, the encryption processing method adopted by the text encryption module and the picture encryption module includes a symmetric encryption algorithm and/or an asymmetric encryption algorithm. The text type data in the data to be encrypted is encrypted, and the encryption algorithm comprises, but is not limited to, symmetric encryption algorithms such as AES and the like or asymmetric encryption algorithms such as RSA and the like.
Example 3
In the data security encryption method disclosed in this embodiment, in S3, the encoder portion includes an input layer, a convolution layer, and a feature extraction layer, and the decoder includes a convolution layer, a feature extraction layer, and an output layer.
Example 4
In the embodiment, with reference to embodiment 1, the data security encryption method disclosed in the embodiment is described, and in S3, the pre-compression process adopted by the picture compression module includes the following steps:
s31, taking the original picture type data as input of the encoder part, and defining depth, width and height of the original picture type data;
s32, enabling the defined picture type data to pass through a convolution layer, performing linear transformation and coding on a convolved result twice, and splicing the result after the linear transformation twice and the result after the linear transformation once to obtain a convolution splicing result once;
and S33, the primary convolution splicing result is subjected to secondary convolution layer passing to ensure that the depth is unchanged, the secondary convolution result is spliced with the primary convolution result and subjected to linear transformation, and the codeword with the depth unchanged, the width and the height reduced to 1/8 of the original picture type data is obtained.
Inputting the picture type data into a convolution layer for one-time convolution after linear transformation and matrix transformation to obtain a one-time convolution result; after extracting features of the primary convolution result, performing linear transformation and matrix transformation again to obtain a primary transformation result;
performing feature extraction twice on the primary convolution result, and performing linear transformation and matrix transformation again to obtain a secondary transformation result; performing three-time feature extraction on the primary convolution result, and performing linear transformation and matrix transformation again to obtain a three-time transformation result;
splicing the secondary linear transformation result and the tertiary linear transformation result, and convoluting the spliced result for two times to obtain a secondary convolution result;
and splicing the primary convolution result and the secondary convolution result, and carrying out convolution and linear transformation on the spliced result to obtain the code word of the picture type data.
Model UniFormer-based AutoEncoder (unified fransformer based self-Encoder) is constructed and trained to include Encoder (Encoder) and Decoder (Decoder) structures. Taking picture type data p as an example, the p size is c×h×w, i.e., the number of picture p channels is c, the height is h, and the width is w.
The picture p is taken as the input of a Uniformer-based AutoEncoder (self-coding network) architecture encoder part, firstly, a convolution layer is passed, the result obtained by the convolution layer is input into a feature extraction layer (a first Uniformer unit), and the output result is denoted as s0. The Linear function (Linear transformation function) is called for s0, and the processing result reshape is converted into c×h×w '(h')<h，w`<Such as) The size, denoted s_0. S0 is input to the feature extraction layer (second UniFormer unit), and the output is denoted as s1. And calling a Linear function for s1, and recording the processing result as s_1, wherein the processing result is the same as the size of c×h '×w'.
S1 is input to the feature extraction layer (third UniFormer unit), and the output is denoted as s2. The Linear function is called for s2 and reshape is also the size c×h '×w', denoted s_2.
And (3) performing characteristic splicing on the s_1 and the s_2, and keeping the number of the spliced channels unchanged by a convolution layer, wherein the result is named conv_cat_1. And performing characteristic splicing on the results out_s1 and s_0 after conv_cat_1 convolution, and keeping the number of channels after splicing unchanged through a convolution layer, wherein the result is named as conv_cat_2.
And (3) applying the result out_s2 after conv_cat_2 convolution to 2 convolution layers, and then calling a Linear function to obtain an N multiplied by 1-dimensional vector s, namely a codeword after compression and encoding of the picture type data p. Due to code wordsThe s-size n×1 is much smaller than the original size c×h×w of the picture p, such as:i.e. the codeword s size is only 1/64 of the original size of the picture p, the memory load and transmission bandwidth required for picture type data can be greatly reduced.
Example 5
In the embodiment, with reference to embodiment 1, the data security encryption method disclosed in the embodiment is described, and in S7, the reconstruction process of the picture reconstruction module includes the following steps:
s71, inputting the code word into a convolution layer for one-time convolution after linear transformation and matrix transformation to obtain a one-time convolution result;
s72, performing characteristic extraction on the primary convolution result, and then performing linear transformation and matrix transformation again to obtain a primary transformation result;
s73, performing linear transformation and matrix transformation again after performing twice feature extraction on the primary convolution result to obtain a secondary transformation result;
s74, performing linear transformation and matrix transformation again after performing three-time feature extraction on the primary convolution result to obtain three-time transformation results;
s75, splicing the secondary linear transformation result and the tertiary linear transformation result, and convoluting the spliced result for two times to obtain a secondary convolution result;
and S76, splicing the primary convolution result and the secondary convolution result, and convolving the spliced result to obtain a reconstructed picture with the same dimensionality as the picture type data.
The resulting codeword s is converted by a Linear function (Linear transformation function) and reshape is c×h×w in size, and the output result is recorded as i. And (3) applying i to one convolution layer, inputting the convolution result into a first Uniformer unit, and recording the Uniformer output result as i0.
The Linear function is called for i0, and the processing result is reshape is c×h×w, and is denoted as i_0. And (3) applying i0 to a second Uniformer unit, and circulating the unit twice to obtain a better picture reconstruction effect, wherein an output result is denoted as i1. The Linear function is called for i1, and the processing result is reshape to be c×h×w, and is denoted as i_1.
I1 is applied to the third UniFormer unit, and the unit is cycled twice as well, and the output is designated as i2. The Linear function is called for i2, and the processing result reshape is c×h×w, and is denoted as i_2. And performing characteristic splicing on the i_1 and the i_2, and keeping the number of the spliced channels unchanged by a convolution layer, and recording the result as conv_cat_1'.
And performing characteristic splicing on the result out_i1 after conv_cat_1 'convolution and i_0, and keeping the number of channels after splicing unchanged through a convolution layer, wherein the result is named as conv_cat_2'. The result out_i2 after conv_cat_2' convolution is acted on 2 convolution layers to obtain a reconstructed picture with the same dimension as the original picture p
Example 6
In S7, the cost function of the picture self-coding network model is the mean square error of the reconstructed picture and the original picture type data.
Designing cost function of the whole Uniformer-based AutoEncoder model as picture type data reconstructed by a decoderMean square error with the original picture type data p, i.e. cost function is +.>Wherein C is the total number of training samples, II 2 Is the euclidean norm.
The parameters of the encoder and decoder (mainly including weights, offsets and convolution kernels) can be jointly trained using a range optimization algorithm and an end-to-end learning approach, so that the cost function is minimized. The trained Uniformer-based AutoEncoder model can be used for the compression encoding and decoding reconstruction of the subsequent picture type data.
Example 7
In the embodiment, in S2, the feature extraction layer includes a dynamic position code, a multi-head relational aggregator, and a feedforward neural network.
The feature extraction layer Uniformer unit consists of three parts, namely dynamic position coding (Dynamic Position Embedding, DPE), multi-head relational aggregator (Multi-Head Relation Aggregator, MHRA) and feedforward neural network (Feed Forward Network, FFN). Feedforward neural networks, i.e. input, hidden or output layer neural structures that are parallel to each other, flow information from front to back.
Example 8
The embodiment is described with reference to embodiment 1, and the data security encryption device disclosed in the embodiment includes a text encryption module, a picture compression module, a picture encryption module, a decryption module and a picture reconstruction module;
the text encryption module is used for encrypting the original text type data so as to obtain text ciphertext data;
the picture compression module is used for pre-compressing the original picture type data so as to obtain picture compression codes;
the picture encryption module is used for encrypting the picture compression codes so as to obtain picture ciphertext data;
the decryption module is used for decrypting the text ciphertext data or the picture ciphertext data which are required to be applied to the downstream task according to the user requirement, and obtaining decrypted text type data and decrypted picture compression codes;
the picture reconstruction module is used for reconstructing and restoring the picture compression coding, so as to obtain reconstructed picture type data.
Claims (6)
1. A data security encryption method, characterized in that it comprises the steps of:
s1, dividing all data to be processed into original text type data and original picture type data;
s2, encrypting the original text type data in the data to be encrypted by adopting a text encryption module to obtain text ciphertext data;
s3, constructing a picture self-coding network model, and pre-compressing original picture type data to be encrypted by adopting a picture compression module to obtain picture compression codes; the picture self-coding network model comprises an encoder and a decoder, wherein the encoder compresses and codes picture type data to form code words; the encoder section includes an input layer, a convolutional layer, and a feature extraction layer, and the decoder includes a convolutional layer, a feature extraction layer, and an output layer;
s4, encrypting the picture compression codes by adopting a picture encryption module to obtain picture ciphertext data;
s5, transmitting or storing the text ciphertext data and the picture ciphertext data according to user requirements;
s6, decrypting the text ciphertext data or the picture ciphertext data which are required to be applied to the downstream task by adopting a decryption module to obtain decrypted text type data and decrypted picture compression codes;
s7, reconstructing and restoring the decrypted picture compression codes by adopting a picture reconstruction module, and obtaining reconstructed picture type data by the decoder after the code words are reconstructed.
2. A data security encryption method according to claim 1, wherein in S2 and S4, the encryption processing method adopted by the text encryption module and the picture encryption module includes a symmetric encryption algorithm and/or an asymmetric encryption algorithm.
3. The method according to claim 1, wherein in S3, the pre-compression process adopted by the picture compression module includes the steps of:
s31, taking the original picture type data as input of the encoder part, and defining depth, width and height of the original picture type data;
s32, enabling the defined picture type data to pass through a convolution layer, performing linear transformation and coding on a convolved result twice, and splicing the result after the linear transformation twice and the result after the linear transformation once to obtain a convolution splicing result once;
and S33, the primary convolution splicing result is subjected to secondary convolution layer passing to ensure that the depth is unchanged, the secondary convolution result is spliced with the primary convolution result and subjected to linear transformation, and the codeword with the depth unchanged, the width and the height reduced to 1/8 of the original picture type data is obtained.
4. The method according to claim 1, wherein in S7, the reconstruction process of the picture reconstruction module comprises the steps of:
s71, inputting the code word into a convolution layer for one-time convolution after linear transformation and matrix transformation to obtain a one-time convolution result;
s72, performing characteristic extraction on the primary convolution result, and then performing linear transformation and matrix transformation again to obtain a primary transformation result;
s73, performing linear transformation and matrix transformation again after performing twice feature extraction on the primary convolution result to obtain a secondary transformation result;
s74, performing linear transformation and matrix transformation again after performing three-time feature extraction on the primary convolution result to obtain a three-time transformation 5-transformation result;
s75, splicing the secondary linear transformation result and the tertiary linear transformation result, and convoluting the spliced result for two times to obtain a secondary convolution result;
and S76, splicing the primary convolution result and the secondary convolution result, and convolving the spliced result to obtain a reconstructed picture with the same dimensionality as the picture type data.
5. A method of data security encryption according to claim 1 or 4, characterized in that in S7 the cost function of the picture self-encoding network model is the mean square error of the reconstructed picture and the original picture type data.
6. A data security encryption method according to claim 1 or 2, characterized in that in S2 the feature extraction layer comprises dynamic position coding, a multihop relational aggregator and a feed forward neural network.
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