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CN-122027119-A - Data management method and device based on homomorphic encryption

CN122027119ACN 122027119 ACN122027119 ACN 122027119ACN-122027119-A

Abstract

The invention discloses a data management method and a device based on isomorphic encryption, wherein the method comprises the steps of firstly mapping original data into a frequency domain; and then processing the original data in the frequency domain based on a preset homomorphic encryption scheme to obtain encrypted data, and then carrying out feature extraction and compression on the encrypted data to obtain compressed data. The consumption of computational resources in the use of isomorphic encryption can be reduced, and the data volume can be reduced.

Inventors

  • Long Shenghai
  • LIAO XIN

Assignees

  • 国信中健数字科技有限公司

Dates

Publication Date
20260512
Application Date
20260402

Claims (8)

  1. 1. A method of data management based on isomorphic encryption, the method comprising: Mapping the original data into a frequency domain; processing the original data in the frequency domain based on a preset homomorphic encryption scheme to obtain encrypted data; and extracting the characteristics of the encrypted data and compressing the encrypted data to obtain compressed data.
  2. 2. The isomorphic encryption-based data management method of claim 1, characterized in that the original data is mapped into the frequency domain by specifically: ; In the formula, For the mapped frequency domain quantum state vector, the frequency domain distribution of the original data in the multidimensional Hilbert space is represented, For a qubit encoding length or signal sampling number, Twiddle factors for quantum Fourier transformation for shaping time domain information Projected onto the basis vectors of the frequency domain, The value range is 0 to N-1 for the frequency domain index.
  3. 3. The method for managing data based on homomorphic encryption according to claim 2, characterized in that it further comprises generating a private key, said private key being encapsulated in the context of the encrypted data, so that the server builds a homomorphic decryption circuit based on the private key, comprising: ; In the formula, In order to represent the instantaneous energy state of the system in the quantum annealing process for the system Hamiltonian quantity evolving with time, And To evolve the scheduling function, the representation controls the rate of transition from an initial simple system like a target complex problem system, For the initial hamiltonian amount, Hamiltonian for the target problem, the The final ground state is the private key.
  4. 4. The isomorphic encryption-based data management method of claim 3, wherein the system hamiltonian is calculated based on a quantum annealing algorithm.
  5. 5. The method for managing data based on homomorphic encryption according to claim 2, wherein the processing of the original data in the frequency domain based on the preset homomorphic encryption scheme to obtain the encrypted data specifically comprises: a random floating point number sequence is obtained based on a pseudo-random number generator, and is mapped to a designated interval to obtain a phase rotation sequence, wherein the phase rotation sequence is specifically a sequence formed by phase confusion angles corresponding to frequency domain coefficients; Acquiring a j-th frequency domain coefficient and an original module length and an original phase of the j-th frequency domain coefficient based on the frequency domain quantum state vector; Performing rotation operation on the original phase of the j-th frequency domain coefficient based on the phase rotation sequence and the original module length to obtain an encrypted new phase; And reconstructing the jth frequency domain coefficient based on the new phase to obtain a reconstructed frequency domain coefficient, and then placing the reconstructed frequency domain coefficient at the jth position of the complex vector.
  6. 6. The isomorphic encryption-based data management method of claim 5, characterized in that the new phase after encryption is obtained by specifically: ; In the formula, For the new phase after encryption, As the original phase is to be taken, Is the phase confusion angle.
  7. 7. The isomorphic encryption-based data management method of claim 1, characterized in that feature extraction and compression is performed specifically by the following formula: ; ; In the formula, In order to preserve the number of feature components, Is the number of total dimensions of the frequency domain, For the eigenvalue of the i-th eigenvector, To encrypt the information energy values of the data in n dimensions i.e. the eigenvalues, To take dictionary D and sparse coefficient As an objective function of the variables, The frequency domain ciphertext to be compressed after the feature extraction, As the compressed sparse coefficient vector, Is the weight of the reconstruction precision and sparsity.
  8. 8. A data management apparatus based on homomorphic encryption, the apparatus comprising: the mapping module is used for mapping the original data into a frequency domain; the encryption module is used for processing the original data in the frequency domain based on a preset homomorphic encryption scheme to obtain encrypted data; and the compression module is used for extracting the characteristics of the encrypted data and compressing the encrypted data to obtain compressed data.

Description

Data management method and device based on homomorphic encryption Technical Field The invention belongs to the technical field of data encryption, and particularly relates to a data management method and device based on homomorphic encryption. Background In the current background of rapid development of the internet, data is already a core asset of each industry, but in the current background, more data needs to be migrated to a cloud end or other places where the data is stored for processing, so that elastic computing power is obtained, but the cloud end and other places face security threat due to openness and multi-tenant characteristics, in order to solve the problem, the prior art has an identical encryption technology, the identical encryption technology is a special encryption technology, the data can be directly calculated in an encrypted state, the cloud end cannot be contacted with plaintext data, and the risk of data leakage is reduced. However, when the full homomorphic encryption technology is used, because huge noise is introduced, the technical problems that the consumption of computing resources is large, and the consumption of memory is serious because ciphertext data is expanded to hundreds of times of original data exist. Therefore, how to reduce the consumption of computing resources and reduce the data volume when using isomorphic encryption is a technical problem to be solved by those skilled in the art. Disclosure of Invention The invention aims to solve the technical problems that in the prior art, the calculation resource consumption is overlarge and the data volume is larger when an isomorphic encryption scheme is used. To achieve the above technical object, in one aspect, the present invention provides a data management method based on isomorphic encryption, the method comprising: Mapping the original data into a frequency domain; processing the original data in the frequency domain based on a preset homomorphic encryption scheme to obtain encrypted data; and extracting the characteristics of the encrypted data and compressing the encrypted data to obtain compressed data. Further, the raw data is mapped into the frequency domain, specifically by the following formula: ; In the formula, For the mapped frequency domain quantum state vector, the frequency domain distribution of the original data in the multidimensional Hilbert space is represented,For a qubit encoding length or signal sampling number,Twiddle factors for quantum Fourier transformation for shaping time domain informationProjected onto the basis vectors of the frequency domain,The value range is 0 to N-1 for the frequency domain index. Further, the method further includes generating a private key, the private key being encapsulated into a context of the encrypted data, so that the server builds a homomorphic decryption circuit based on the private key, specifically including: ; In the formula, In order to represent the instantaneous energy state of the system in the quantum annealing process for the system Hamiltonian quantity evolving with time,AndTo evolve the scheduling function, the representation controls the rate of transition from an initial simple system like a target complex problem system,For the initial hamiltonian amount,Hamiltonian for the target problem, theThe final ground state is the private key. Further, the Hamiltonian amount of the system is calculated based on a quantum annealing algorithm. Further, the processing the original data in the frequency domain based on the preset homomorphic encryption scheme to obtain encrypted data specifically includes: a random floating point number sequence is obtained based on a pseudo-random number generator, and is mapped to a designated interval to obtain a phase rotation sequence, wherein the phase rotation sequence is specifically a sequence formed by phase confusion angles corresponding to frequency domain coefficients; Acquiring a j-th frequency domain coefficient and an original module length and an original phase of the j-th frequency domain coefficient based on the frequency domain quantum state vector; Performing rotation operation on the original phase of the j-th frequency domain coefficient based on the phase rotation sequence and the original module length to obtain an encrypted new phase; And reconstructing the jth frequency domain coefficient based on the new phase to obtain a reconstructed frequency domain coefficient, and then placing the reconstructed frequency domain coefficient at the jth position of the complex vector. Further, the new phase after encryption is obtained specifically by the following formula: ; In the formula, For the new phase after encryption,As the original phase is to be taken,Is the phase confusion angle. Further, feature extraction and compression are specifically performed by the following formula: ; ; In the formula, In order to preserve the number of feature components,Is the number of total dimensions of the frequen