CN111431698B - Vector space data encryption method using Haar transformation and Gaussian distribution - Google Patents
Vector space data encryption method using Haar transformation and Gaussian distribution Download PDFInfo
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- CN111431698B CN111431698B CN202010328668.4A CN202010328668A CN111431698B CN 111431698 B CN111431698 B CN 111431698B CN 202010328668 A CN202010328668 A CN 202010328668A CN 111431698 B CN111431698 B CN 111431698B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/08—Key distribution or management, e.g. generation, sharing or updating, of cryptographic keys or passwords
- H04L9/0861—Generation of secret information including derivation or calculation of cryptographic keys or passwords
- H04L9/0869—Generation of secret information including derivation or calculation of cryptographic keys or passwords involving random numbers or seeds
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/06—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols the encryption apparatus using shift registers or memories for block-wise or stream coding, e.g. DES systems or RC4; Hash functions; Pseudorandom sequence generators
- H04L9/0643—Hash functions, e.g. MD5, SHA, HMAC or f9 MAC
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/08—Key distribution or management, e.g. generation, sharing or updating, of cryptographic keys or passwords
- H04L9/0861—Generation of secret information including derivation or calculation of cryptographic keys or passwords
- H04L9/0863—Generation of secret information including derivation or calculation of cryptographic keys or passwords involving passwords or one-time passwords
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/50—Reducing energy consumption in communication networks in wire-line communication networks, e.g. low power modes or reduced link rate
Abstract
Firstly, generating a hash key K for a key input by a user by using an SHA-512 hash algorithm, calculating a Gaussian random number by using the key K through Gaussian distribution, and generating a new key of an encryption element by using the hash key K and the Gaussian random number sequence; then, performing tree Haar transformation on the read vector data vertex sequence to obtain a mean value coefficient and a difference value coefficient; then, encrypting the mean value coefficient and the difference value coefficient after tree Haar transformation by using a multiplication principle; and performing tree-shaped Haar inverse transformation on the encrypted frequency domain coefficient to obtain a coordinate value, and finally performing random encryption on vector data by using Gaussian random numbers through randomization processing to generate encrypted vector geospatial data. The method has stronger security on the encryption of the vector geographic space data, and the structure of the vector map data is reserved under the condition that the coordinate value of the vector geographic space data is completely changed.
Description
Technical Field
The invention relates to the field of geospatial data security, in particular to a vector space data encryption method using Haar transformation and Gaussian distribution.
Background
The vector geographic data is an important foundation for national infrastructure construction and earth information science research, is an important component part of the national basic geographic data, is also an indispensable strategic resource in national economy and national defense construction, occupies a very important position in the national economy and national defense construction, and is widely applied to various industries. The rapid development of networking, informatization and digitalization technologies makes vector space data which are expensive in production cost and have high value easy to be illegally copied and distributed by hackers, pirates and unauthorized users, and therefore it is of great importance to protect vector geographic data from being illegally copied and distributed.
The current research on vector space data security is mainly focused on three aspects, namely copyright protection based on digital watermarking, user channel control, protection against attacks during storage and transportation, and illegal distribution of encrypted map data. The vector space data watermarking algorithm is mainly to embed watermark by modifying airspace coordinate values of geographic elements or to embed watermark information into frequency domain coefficients, but the digital watermarking technology only acts on the aspects of ownership and copyright protection of identification data, and unauthorized users should not see, attack or extract vector space data content. In recent researches, various methods for controlling a user network channel to access vector geographic data and security requirements and privacy strategies of a geographic space database management system are proposed, and a series of research results are obtained, but the user channel control cannot effectively prevent attack and illegal theft of the data. So that research into the encrypted vector space data is necessary.
Aiming at the structure that unauthorized users pirate data and ciphertext encrypted by using a text encryption method cannot effectively retain vector map data, the invention discloses a vector data encryption method based on a Haar transform domain and Gaussian random distribution. The method mainly comprises the steps of extracting all multi-section line elements from vector space data in a Shapefile format for encryption protection, increasing safety through Tree-Structured Haar (TSH) transformation, calculating high-frequency coefficients and low-frequency coefficients, encrypting frequency domain coefficients by a secret key generated by an SHA-512 hash algorithm and an encryption value generated by the secret key, and finally randomizing all vertex sequences by random Gaussian numbers generated by Gaussian distribution to generate encrypted vector data.
Disclosure of Invention
In view of this, the present invention proposes a vector geospatial data encryption method based on tree Haar and combined with gaussian distribution, and fig. 1 is a general flow of the vector geospatial data encryption method of the present invention, including three parts of key generation, random encryption and decryption processing.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the vector space data encryption method using Haar transformation and Gaussian distribution comprises the following steps: key generation, random encryption and data decryption;
1. the key generation steps are as follows:
s1: generating a hash key K for a key input by a user through an SHA-512 algorithm, and calculating a Gaussian random number sequence Gn through Gaussian distribution by using the key K i ＝Gp(K)＝{g i,j |j∈[1,|P i |]}；
S2: generating a new key of the encryption element by using the hash key K and the Gaussian random number sequence;
2. the random encryption steps are as follows:
s3: vertex sequence v for reading vector data i,j ，v i,j From the coordinate value (x) i,j ,y i,j ) The composition is that tree Haar transformation is carried out on the vertex sequence;
s4: encrypting the frequency domain coefficient after Haar transformation by using a multiplication principle;
s5: performing Haar inverse transformation on the encrypted frequency domain coefficient to obtain a coordinate value;
s6: using Gaussian random numbers Gn i Carrying out random encryption on vector data through randomization;
s7: modifying corresponding coordinates according to the vertex sequence encrypted randomly to obtain encrypted data;
3. the data decryption steps are as follows:
s8: reading an encrypted object from the encrypted vector geospatial data;
s9: use of key E after re-randomization of vertices by Gaussian random numbers Haar Decrypting in a tree Haar domain by combining a multiplication principle;
s10: performing tree-shaped Haar inverse transformation to obtain decrypted vector data coordinates;
s11: and (5) ending.
The method is advanced and scientific, ensures that the encrypted data cannot be identified and effective information cannot be provided, has good safety, and the structure of the vector map data is completely reserved. Experiments show that the method has good safety, high calculation speed, high efficiency and good use value, and the decryption error is almost zero.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will briefly introduce the drawings required to be used in the embodiments or the prior art descriptions, it is obvious that the drawings in the following description are only schematic views of the present invention, and other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a Haar transform domain and Gaussian random distribution based vector data encryption method provided by the invention;
FIG. 2 is a model of vector geospatial data provided by the present invention;
FIG. 3 is a representation of the composition of vector geospatial data provided by the present invention;
FIG. 4 is initial road vector data for the experiments provided by the present invention;
FIG. 5 shows the result of road data encryption provided by the invention;
FIG. 6 is a diagram showing the result of decrypting road data according to the present invention;
FIG. 7 is a graph showing the encryption result of railway data provided by the invention;
FIG. 8 is a graph showing the result of decrypting railway data using the correct key provided by the present invention;
fig. 9 is a graph showing the result of decrypting railway data using the wrong key according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The following defines the parts for symbols:
the vector geospatial data comprises a number of data layers, each data layer L being made up of geographic elements (line elements or plane elements), i.e. l= { P i |i∈[1,|L|]Each geographic element contains a large number of vertices, i.e., P i ＝{v i,j |j∈[1，|P i |]}. Each vertex contains two coordinate values, i.e. v i，j ＝(x i，j ，y i，j ). Wherein P is i Represents a geographic element (line element or plane element), and |L| and |P| i I represents layer L and element |p, respectively i Cardinality of v i，j Then it is the j-th point on the i-th element in layer L. Other primary symbols are defined as follows: e (E) Haar Is an encryption element P i K is a key generated by a hash function, gn i Is a Gaussian random number sequence, ac and Dc respectively represent a mean coefficient and a difference coefficient, ea and Ed respectively represent an encryption value of the mean coefficient and the difference coefficient, E i Is the vertex sequence encryption value, R i Is a vector data random encryption value, and Cp (), rp (), and Gp (), are encryption, randomization, and random gaussian functions, respectively.
The following steps are the key generation section:
step 1: the key input by the user generates a key K through the SHA-512 hash algorithm, and the key K is used for calculating a random Gaussian number sequence Gn through random Gaussian distribution i It is defined as follows:
Gn i ＝Gp(K)＝{g i,j |j∈[1,|P i |]} (1)
where x is the value of the key K;
step 2: generating a new key E of an encryption element using a hash key K and a Gaussian random number sequence Haar The definition is as follows:
wherein x is the value of the key K, and n is the length of the key K;
the following steps are random encryption:
step 3: determining vertex sequence v of read vector data i,j ，v i,j From the coordinate value (x) i，j ，y i，j ) The method comprises the steps of performing tree Haar transformation on a vertex sequence to obtain a mean value coefficient Ac and a difference value coefficient Dc:
(4) Calculating a definition average value coefficient Ac and a difference value coefficient Dc;
step 4: encrypting the frequency domain coefficient after Haar transformation by using a multiplication principle, encrypting the mean coefficient and the difference coefficient after transformation by using the multiplication principle in a tree Haar domain, and obtaining an encrypted vertex sequence E after Haar inverse transformation after encryption is completed i ，
E i ＝{e i,j |j∈[1,|E i |]} (6)
In the formula, |E i |＝|P i |，e i，j Is composed of coordinate values, which are expressed as follows:
e i,j ＝(IEa i,j ,IEd i,j ) (7)
therein, IEa i,j And IEd i,j Coordinate values of Haar inverse transformation;
step 5: using Gaussian random numbers Gn i The random encryption of vector data by randomization is expressed as follows:
R i ＝Rp(E i ,Gn i )＝{r i,j |j∈[1,|R i |]} (8)
in the formula, |R i |＝|P i |，r i，j Defined as formula (9):
step 6: modifying corresponding coordinates according to the vertex sequence encrypted randomly to obtain encrypted data;
the following steps are decrypting the data portion:
step 7: reading an encrypted object from the encrypted vector geospatial data;
step 8: use of key E after re-randomization of vertices by Gaussian random numbers Haar Decrypting in a tree Haar domain by combining a multiplication principle;
step 9: performing tree-shaped Haar inverse transformation to obtain decrypted vector data coordinates;
step 10: and (5) ending.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (3)
1. A vector space data encryption method using tree Haar transformation and Gaussian distribution comprises three parts of key generation, random encryption and data decryption:
the key generation steps are as follows:
s1: generating a hash key K for a key input by a user through an SHA-512 algorithm, and calculating a Gaussian random number Gn through Gaussian distribution by using the key K i ＝Gp(K)＝{g i，j |j∈[1，|P i |]-wherein Gp (K) represents a random gaussian function of the cryptographic hash key K, g i，j Is the value of a gaussian random number, |p i I represents the cardinality of the geographic element, i and j represent the index of the geographic element in the vector space data and the index of the vertex in the vector space data respectively;
s2: usingGenerating new key E of encryption element by hash key K and Gaussian random number sequence Haar ＝(n×x)/Gn i Wherein x is the value of the key K, and n is the length of the key K;
the random encryption steps are as follows:
s3: vertex sequence v for reading vector data i，j ，v i，j From the coordinate value (x) i，j ，y i，j ) The composition is that tree Haar transformation is carried out on the vertex sequence;
s4: encrypting the frequency domain coefficient after Haar transformation by using a multiplication principle;
s5: carrying out Haar inverse transformation on the encrypted frequency domain coefficient to obtain a coordinate value;
s6: using Gaussian random numbers Gn i Encrypting the vector data by randomization;
s7: modifying corresponding coordinates according to the vertex sequence encrypted randomly to obtain encrypted data;
the data decryption steps are as follows:
s8: reading an encrypted object from the encrypted vector geospatial data;
s9: use of key E after re-randomization of vertices by Gaussian random numbers Haar Decrypting in a tree Haar domain by combining a multiplication principle;
s10: performing tree-shaped Haar inverse transformation to obtain decrypted vector data coordinates;
s11: and (5) ending.
2. A method of encrypting vector space data using tree Haar transforms and gaussian distributions according to claim 1, wherein in step S8, the encrypted vector geospatial data is generated using steps S1 to S7.
3. A method of vector space data encryption using tree Haar transforms and gaussian distributions according to claim 1 or claim 2, characterized in that in step S10, data decryption is done.
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| CN104794671A (en) * | 2015-03-28 | 2015-07-22 | 兰州交通大学 | Vector space data blind watermark method resistant to projection attack |
| KR20150125850A (en) * | 2014-04-30 | 2015-11-10 | 부경대학교 산학협력단 | The methode of selesctive encryption scheme for geographic information system vector map data |
| CN110138561A (en) * | 2019-03-22 | 2019-08-16 | 西安电子科技大学 | Efficient cipher text retrieval method, the cloud computing service system automatically corrected based on CP-ABE |
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| JP6019453B2 (en) * | 2012-07-05 | 2016-11-02 | 株式会社クリプト・ベーシック | ENCRYPTION DEVICE, DECRYPTION DEVICE, AND PROGRAM |
| WO2014112182A1 (en) * | 2013-01-18 | 2014-07-24 | 三菱電機株式会社 | Data decoding device, attribute-based encryption system, random number element removing device, randomized secret key generation device, data decoding method, and data decoding program |
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| KR20150125850A (en) * | 2014-04-30 | 2015-11-10 | 부경대학교 산학협력단 | The methode of selesctive encryption scheme for geographic information system vector map data |
| CN104794671A (en) * | 2015-03-28 | 2015-07-22 | 兰州交通大学 | Vector space data blind watermark method resistant to projection attack |
| CN110138561A (en) * | 2019-03-22 | 2019-08-16 | 西安电子科技大学 | Efficient cipher text retrieval method, the cloud computing service system automatically corrected based on CP-ABE |
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