CN113055153B - Data encryption method, system and medium based on fully homomorphic encryption algorithm - Google Patents
Data encryption method, system and medium based on fully homomorphic encryption algorithm Download PDFInfo
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- CN113055153B CN113055153B CN202110259889.5A CN202110259889A CN113055153B CN 113055153 B CN113055153 B CN 113055153B CN 202110259889 A CN202110259889 A CN 202110259889A CN 113055153 B CN113055153 B CN 113055153B
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Abstract
The invention belongs to the technical field of data security, and relates to an image data encryption method, system and medium based on a fully homomorphic encryption algorithm, which comprises the following steps: s1, encrypting data by adopting a fully homomorphic encryption algorithm; s2, decomposing the data encryption model to obtain a function which cannot be replaced by addition, multiplication and displacement operations in the data encryption model; s3, simulating the function in the step S2 by using a polynomial to generate a data encryption model represented by the polynomial; s4, training a data encryption model expressed by a polynomial by using the encrypted image data so as to obtain an optimal data encryption model; and S5, identifying the encrypted data by adopting the optimal data encryption model, and decoding the output result of the optimal data encryption model to obtain a data encryption result. The method has higher safety on the premise of ensuring the system efficiency, expandability and image identification accuracy, and can effectively avoid image data leakage.
Description
Technical Field
The invention relates to a data encryption method, a system and a medium based on a fully homomorphic encryption algorithm, belonging to the technical field of data security.
Background
With the rapid development of social informatization and networking, data is growing explosively. Enterprises, financial institutions and even government agencies are required to face exponentially increasing amounts of data each day. Some sensitive information exists in the data, such as personal privacy, business confidentiality and the like, and if the information is leaked and utilized by lawless persons, the harm is immeasurable, which not only can cause property loss, but also can cause serious influence on the life of individuals, the reputation of enterprises and the security of countries. Meanwhile, with the development of cloud computing technology and cloud platforms, many enterprises, financial institutions and government agencies host their data in professional data storage computing platforms, which brings great challenges to data security.
In order to ensure the data security on the cloud platform, a commonly adopted means at present is to encrypt the data and store the encrypted data on the cloud platform, and before the encrypted data is processed, the encrypted data needs to be returned to a user for decryption and then returned, and finally the encrypted data can be processed. In such a mode, storage and calculation are separated, which causes waste of resources and efficiency, and more importantly, although data is stored in a ciphertext form, the data is still in a plaintext during calculation and transmission, and a large potential safety hazard still exists. Especially, the development of the current image recognition technology is very fast, and when a data encryption model is trained, a large amount of unprocessed original image data needs to be introduced, and the image data in a large amount often contains personal privacy, such as face images, identity card images, addresses and the like. When a data owner passes complex operations such as data encryption to a server for computation, it is further necessary to protect the image data from leakage.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a data encryption method, system and medium based on a fully homomorphic encryption algorithm, which have higher security and can effectively avoid data leakage on the premise of ensuring system efficiency, expandability and data encryption accuracy.
In order to achieve the purpose, the invention adopts the following technical scheme: a data encryption method based on a fully homomorphic encryption algorithm comprises the following steps: s1, encrypting data by adopting a fully homomorphic encryption algorithm; s2, decomposing the data encryption model to obtain a function which cannot be replaced by addition, multiplication and displacement operations in the data encryption model; s3, simulating the function in the step S2 by using a polynomial to generate a data encryption model represented by the polynomial; s4, training the data encryption model represented by the polynomial in the step S3 by using the encrypted data in the step S1, so as to obtain an optimal data encryption model; and S5, identifying the encrypted data by adopting the optimal data encryption model, and decoding the output result of the optimal data encryption model to obtain a data encryption result.
Further, the method for encrypting data in step S1 includes the following steps: and inputting a key by a user, and encrypting the data through a homomorphic encryption algorithm according to the key.
Further, the specific method for encrypting the data according to the key comprises the following steps: inputting a key input by a user into an SHA256 algorithm, and converting the key with any length into a hexadecimal character string; mapping the character string to a key space of a fully homomorphic encryption algorithm, and obtaining a public and private key pair of the fully homomorphic encryption algorithm through the key space; and a public key in the public and private key pair is used for image data encryption, and a private key in the public and private key pair is used for decryption.
Further, the fully homomorphic encryption algorithm is a fully homomorphic encryption algorithm based on the LWE (Learning With Errors) problem or a fully homomorphic encryption algorithm based on an integer ring.
Further, the method for generating the data encryption model represented by the polynomial in step S3 is as follows: extracting functions which cannot be replaced by addition, multiplication and displacement operations in the initial data encryption model; decomposing the function into a single input function, simulating the input-output relation of the single input function, constructing a plurality of polynomials according to the input-output relation, approximating the calculation result of the polynomials with the calculation result of the single input function by adjusting the coefficients of the polynomials, and adding all the polynomials to obtain the data encryption model represented by the polynomials.
Further, the data encryption model is a neural network model.
Further, the method for obtaining the optimal data encryption model in step S4 includes: and (2) inputting the encrypted data obtained in the step (S1) into a data encryption model represented by a polynomial, and continuously modifying parameters in the data encryption model through data encryption to minimize a loss function of the data encryption model so as to obtain the optimal data encryption model.
The invention also discloses a data encryption system based on the fully homomorphic encryption algorithm, which comprises the following steps: the encryption module is used for encrypting the data by adopting a fully homomorphic encryption algorithm; the model input module is used for decomposing the data encryption model to obtain a function which cannot be replaced by addition, multiplication and displacement operations in the data encryption model; the model processing module is used for simulating the function in the model input module by using a polynomial to generate a data encryption model represented by the polynomial; the model training module is used for training the data encryption model expressed by the polynomial through the encrypted data in the encryption module so as to obtain the optimal data encryption model; and the decryption module is used for processing the encrypted data according to the optimal data encryption model and decoding the output result of the optimal data encryption model so as to obtain an image identification result.
Further, the data encryption system also comprises a data storage module, which is used for storing the encrypted data generated in the encryption module; the decryption module decrypts the optimal data encryption model while decrypting the output result of the optimal data encryption model.
The invention also discloses a computer readable storage medium, on which a computer program is stored, the computer program being executed by a processor to implement the data encryption method based on the fully homomorphic encryption algorithm as any one of the above.
Due to the adoption of the technical scheme, the invention has the following advantages: the method has higher safety on the premise of ensuring the image identification accuracy and the cloud computing platform efficiency, ensures the safety of data through a fully homomorphic encryption algorithm, and prevents the image leakage of sensitive information such as personal privacy and the like.
Drawings
FIG. 1 is a flow chart of an image recognition method based on a fully homomorphic encryption algorithm according to an embodiment of the present invention;
fig. 2 is a flowchart of an image recognition system based on a fully homomorphic encryption algorithm according to an embodiment of the present invention.
Detailed Description
The present invention is described in detail by way of specific embodiments in order to better understand the technical direction of the present invention for those skilled in the art. It should be understood, however, that the detailed description is provided for a better understanding of the invention only and that they should not be taken as limiting the invention. In describing the present invention, it is to be understood that the terminology used is for the purpose of description only and is not intended to be indicative or implied of relative importance.
The invention relates to a data encryption method, system and medium based on a fully homomorphic encryption algorithm, which comprises the following steps: preprocessing data input by a user and a data encryption model to generate corresponding encrypted data and a data encryption model formed by fully homomorphic addition, multiplication and displacement operations, and submitting the encrypted data and the model to a data processing end; the data processing end trains the data encryption model by using ciphertext data provided by a user, and returns a processing result and the optimal data encryption model to the user end after the training is finished; and the user side realizes data encryption through the key decryption processing result and the optimal data encryption model. The method adopts a fully homomorphic encryption algorithm and simulates a data encryption model through a polynomial approximation method, so that a user can outsource image recognition training to a cloud service provider with a large amount of computing resources under the condition of ensuring that sensitive training data are not leaked. Compared with the existing cloud computing service, the technical scheme of the invention has higher safety while ensuring the cloud service efficiency.
Example one
The embodiment discloses a data encryption method based on a fully homomorphic encryption algorithm, as shown in fig. 1, comprising the following steps:
s1, encrypting data by adopting a fully homomorphic encryption algorithm.
The method for encrypting data includes the following steps, taking image data as an example for explanation:
s1.1, converting image data into a pixel matrix;
s1.2, a key is input by a user, and the pixel matrix is encrypted through a homomorphic encryption algorithm according to the key.
The specific method for encrypting the pixel matrix according to the key in step S1.2 includes: inputting a key input by a user into a SHA256 (Secure Hash Algorithm 256) Algorithm, and converting the key with any length into a hexadecimal character string with the length of 64 bytes; mapping the character string to a key space of a fully homomorphic encryption algorithm, and obtaining a public and private key pair of the fully homomorphic encryption algorithm through the key space; and a public key in the public and private key pair is used for image data encryption, and a private key in the public and private key pair is used for decryption.
In this embodiment, the fully homomorphic encryption algorithm is a fully homomorphic encryption algorithm based on the LWE (Learning With Errors) problem or a fully homomorphic encryption algorithm based on an integer ring.
S2, decomposing the data encryption model to obtain a function which cannot be replaced by addition, multiplication and displacement operations in the data encryption model;
and S3, simulating the function in the step S2 by using a polynomial, replacing the function by the polynomial function, replacing the addition, multiplication and displacement operations in all functions in the data encryption model at the moment by corresponding addition, multiplication and displacement operations in an all-homomorphic algorithm, and obtaining the data encryption model represented by the polynomial.
The method for generating the data encryption model represented by the polynomial comprises the following steps: extracting functions which cannot be replaced by addition, multiplication and displacement operations in the initial data encryption model; decomposing the function into a single input function, simulating the input-output relation of the single input function, constructing a plurality of polynomials according to the input-output relation, approximating the calculation result of the polynomials with the calculation result of the single input function by adjusting the coefficients of the polynomials, and adding all the polynomials to obtain the data encryption model represented by the polynomials.
The data encryption model in this embodiment is a neural network model.
And S4, training the data encryption model represented by the polynomial in the step S3 by using the encrypted data in the step S1 so as to obtain an optimal data encryption model.
The method for obtaining the optimal data encryption model comprises the following steps: and (2) inputting the encrypted data obtained in the step (S1) into a data encryption model represented by a polynomial, and continuously modifying parameters in the data encryption model through data encryption to minimize a loss function of the data encryption model so as to obtain the optimal data encryption model.
And S5, identifying the encrypted data by adopting the optimal data encryption model, and decoding the output result of the optimal data encryption model to obtain a data encryption result.
And the user generates a decryption key of the fully homomorphic encryption algorithm according to the key selected by the user. And decrypting the optimal data encryption model and the output result thereof by using a decryption key to obtain the image identification result and the optimal data encryption model. In the subsequent data encryption process, the data encryption model does not need to be trained, and the optimal data encryption model is directly adopted.
Example two
Based on the same inventive concept, the embodiment discloses an image recognition system based on a fully homomorphic encryption algorithm, as shown in fig. 2, including: the system comprises a user side and a data processing side, wherein the user side comprises an encryption module, a model input module and a decryption module; the data processing end comprises a model processing module and a model training module.
And the encryption module is used for encrypting the data by adopting a fully homomorphic encryption algorithm.
And the model input module is used for decomposing the data encryption model to obtain a function which cannot be replaced by addition, multiplication and displacement operations in the data encryption model. The data encryption model herein includes not only the model input by the user but also the model selected by the user from the system. Since the neural network model is a relatively conventional data encryption model, the neural network model, such as the CNN model, is used in this embodiment, but the application of other models is not excluded.
And the model processing module is used for simulating the function in the model input module by using a polynomial to generate a data encryption model represented by the polynomial.
And the model training module is used for training the data encryption model expressed by the polynomial through the encrypted data in the encryption module so as to obtain the optimal data encryption model.
And the decryption module is used for identifying the encrypted data by adopting the optimal data encryption model and decoding the output result of the optimal data encryption model so as to obtain an image identification result. The decryption module decrypts the optimal data encryption model while decrypting the output result of the optimal data encryption model.
The identification system further comprises a data storage module for storing the encrypted data generated in the encryption module. The data storage module is arranged at the data processing end.
EXAMPLE III
Based on the same inventive concept, the present embodiment discloses a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the data encryption method based on the homomorphic encryption algorithm as any one of the above.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims. The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application should be defined by the claims.
Claims (9)
1. A data encryption method based on a fully homomorphic encryption algorithm is characterized by comprising the following steps:
s1, encrypting data by adopting a fully homomorphic encryption algorithm;
s2, decomposing the data encryption model to obtain a function which cannot be replaced by addition, multiplication and displacement operations in the data encryption model;
s3, simulating the function in the step S2 by using a polynomial to generate a data encryption model represented by the polynomial;
s4, training the data encryption model represented by the polynomial in the step S3 by using the encrypted data in the step S1, so as to obtain an optimal data encryption model;
s5, identifying the encrypted data by adopting the optimal data encryption model, and decoding the output result of the optimal data encryption model to obtain a data encryption result;
the method for generating the data encryption model represented by the polynomial in the step S3 comprises the following steps: extracting functions which cannot be replaced by addition, multiplication and displacement operations in an initial data encryption model; decomposing the function into a single input function, simulating the input-output relation of the single input function, constructing a plurality of polynomials according to the input-output relation, approximating the calculation result of the polynomials with the calculation result of the single input function by adjusting the coefficients of the polynomials, and adding all the polynomials to obtain the data encryption model represented by the polynomials.
2. The data encryption method based on the fully homomorphic encryption algorithm according to claim 1, wherein the method for encrypting the data in step S1 comprises the steps of: and inputting a key by a user, and encrypting the data through a homomorphic encryption algorithm according to the key.
3. The data encryption method based on fully homomorphic encryption algorithm according to claim 2, wherein the specific method for encrypting the data according to the key comprises:
inputting a key input by a user into an SHA256 algorithm, and converting the key with any length into a hexadecimal character string; mapping the character string to a key space of the homomorphic encryption algorithm, and obtaining a public and private key pair of the homomorphic encryption algorithm through the key space; and using a public key in the public and private key pair for image data encryption and using a private key in the public and private key pair for decryption.
4. The fully homomorphic encryption algorithm-based data encryption method of claim 3, wherein the fully homomorphic encryption algorithm is a fully homomorphic encryption algorithm based on the LWE problem or a fully homomorphic encryption algorithm based on an integer ring.
5. The fully homomorphic encryption algorithm-based data encryption method of claim 1, wherein said data encryption model is a neural network model.
6. The data encryption method based on fully homomorphic encryption algorithm according to claim 1, wherein the method for obtaining the optimal data encryption model in step S4 is:
and (2) inputting the encrypted data obtained in the step (S1) into a data encryption model represented by a polynomial, and continuously modifying parameters in the data encryption model through data encryption to minimize a loss function of the data encryption model so as to obtain the optimal data encryption model.
7. A data encryption system based on a fully homomorphic encryption algorithm, comprising:
the encryption module is used for encrypting the data by adopting a fully homomorphic encryption algorithm;
the model input module is used for decomposing the data encryption model to obtain a function which cannot be replaced by addition, multiplication and displacement operations in the data encryption model;
the model processing module is used for simulating the function in the model input module by using a polynomial and generating a data encryption model represented by the polynomial;
the model training module is used for training the data encryption model expressed by the polynomial through the encrypted data in the encryption module so as to obtain the optimal data encryption model;
the decryption module is used for processing the encrypted data according to the optimal data encryption model and decoding the output result of the optimal data encryption model so as to obtain an image identification result;
the method for generating the data encryption model represented by the polynomial in the model processing module comprises the following steps: extracting functions which cannot be replaced by addition, multiplication and displacement operations in an initial data encryption model; decomposing the function into a single input function, simulating the input-output relation of the single input function, constructing a plurality of polynomials according to the input-output relation, approximating the calculation result of the polynomials with the calculation result of the single input function by adjusting the coefficients of the polynomials, and adding all the polynomials to obtain the data encryption model represented by the polynomials.
8. The data encryption system based on the fully homomorphic encryption algorithm according to claim 7, wherein the data encryption system further comprises a data storage module for storing the encrypted data generated in the encryption module; the decryption module decrypts the optimal data encryption model while decrypting an output result of the optimal data encryption model.
9. A computer-readable storage medium, having stored thereon a computer program for execution by a processor to implement the method for data encryption based on an all homomorphic encryption algorithm according to any one of claims 1-6.
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| CN112328699A (en) * | 2020-11-20 | 2021-02-05 | 中山大学 | Security outsourcing method and system based on block chain fully homomorphic encryption algorithm |
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| US9350543B2 (en) * | 2012-07-26 | 2016-05-24 | Cisco Technology, Inc. | Method and system for homomorphicly randomizing an input |
| US10541805B2 (en) * | 2017-06-26 | 2020-01-21 | Microsoft Technology Licensing, Llc | Variable relinearization in homomorphic encryption |
| US11087223B2 (en) * | 2018-07-11 | 2021-08-10 | International Business Machines Corporation | Learning and inferring insights from encrypted data |
| US11575500B2 (en) * | 2018-07-25 | 2023-02-07 | Sap Se | Encrypted protection system for a trained neural network |
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| WO2016118206A2 (en) * | 2014-11-07 | 2016-07-28 | Microsoft Technology Licensing, Llc | Neural networks for encrypted data |
| CN112328699A (en) * | 2020-11-20 | 2021-02-05 | 中山大学 | Security outsourcing method and system based on block chain fully homomorphic encryption algorithm |
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