CN110069907A - Big data source tracing method and system based on digital watermarking - Google Patents
Big data source tracing method and system based on digital watermarking Download PDFInfo
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- CN110069907A CN110069907A CN201910335999.8A CN201910335999A CN110069907A CN 110069907 A CN110069907 A CN 110069907A CN 201910335999 A CN201910335999 A CN 201910335999A CN 110069907 A CN110069907 A CN 110069907A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/10—Protecting distributed programs or content, e.g. vending or licensing of copyrighted material ; Digital rights management [DRM]
- G06F21/16—Program or content traceability, e.g. by watermarking
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/602—Providing cryptographic facilities or services
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T1/00—General purpose image data processing
- G06T1/0021—Image watermarking
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Abstract
The embodiment of the present invention provides a kind of big data source tracing method and system, which comprises two-value picture watermark is converted to raw bytes flowing water print, encryption is carried out to raw bytes flowing water print and obtains digital watermarking and shared key；By in original big data every record in major key and text data be converted into, record corresponding digital finger-print with every；Based on digital finger-print, all records in original big data are divided into n group, n is the pixel number of the two-value picture watermark；Every records and records corresponding digital finger-print, a pixel, i.e. in digital watermarking a position in the corresponding two-value picture watermark with every；Using a position in digital watermarking as value embedded, it is embedded into the numeric type data in big data in every record.The embodiment of the present invention, which has the advantages that, solves in the prior art the problems such as algorithm robustness is poor, and anti-deletion is aggressive poor, anti-increase attack difference.
Description
Technical field
The present embodiments relate to digital watermark technology fields, and in particular to a kind of big data based on digital watermarking is traced to the source
Method and system.
Background technique
With the major transformation that big data is opened in terms of human lives, work and thinking, personal data have become one
The precious deposits for containing immense value can constantly create value in a manner of multiplicity after its value is exploited.It is dug by depth
Personal data are dug, enterprise, which is able to achieve the market segments more refined and designs production, has more targetedly product, realizes precisely battalion
Pin；The formulation of government policy regulation will be more wise and farsighted, scientific.In big data era, personal data are collected, handle, trade and are answered
With will be unprecedentedly active, meanwhile, frequent transaction will also set personal data privacy in the desperate situation revealed at any time.Therefore,
Personal data secret protection problem under big data background how is solved, the pass between personal privacy protection and data utilization is balanced
System, has caused the extensive concern of industry and academia.
It is one of important directions that data, which are traced to the source,.Non-encrypted method is used using digital watermark technology, realizes logarithm
According to copyright protection and security control, more meet the characteristic and application demand of big data era data.Currently, for the water of data
Print insertion is based primarily upon Numeric Attributes and certain redundancy may be present to realize, passes through the least significant bit of logarithm type attribute
LSB (Least Significant Bit) introduces the insertion and extraction that certain error realizes watermark.
However, existing big data tracing technology has following defect: algorithm robustness is poor, aggressive poor, the anti-increase of anti-deletion
Attack difference etc..
Summary of the invention
For this purpose, the embodiment of the present invention provides a kind of big data source tracing method and system based on digital watermarking, it is existing to solve
There is algorithm robustness in technology poor, anti-deletion is aggressive poor, and the problems such as poor is attacked in anti-increase.
To achieve the goals above, the embodiment of the present invention provides the following technical solutions:
According to embodiments of the present invention, a kind of big data source tracing method is provided, comprising:
Two-value picture watermark is converted to raw bytes flowing water print, encryption is carried out to raw bytes flowing water print and obtains digital water
Print and shared key；
By in original big data every record in major key and text data be converted into, record corresponding number with every
Fingerprint；
Based on digital finger-print, all records in original big data are divided into n group, n is the picture of the two-value picture watermark
Vegetarian refreshments number；Every records and records corresponding digital finger-print with every, corresponds to a pixel in the two-value picture watermark,
A position i.e. in digital watermarking；
Using a position in digital watermarking as value embedded, it is embedded into the numeric type data in big data in every record
In.
Further, the method also includes:
By in big data to be compared in every record major key and text data be converted into, record corresponding the with every
Two digital finger-prints；
Based on the second digital finger-print, all records in big data to be compared are divided into n group, n is the two-value picture water
The pixel number of print；Every records and records corresponding second digital finger-print, the corresponding two-value picture watermark with every
In a pixel, i.e. a position in the second digital watermarking；
The position being embedded in each group in numeric type data in big data to be compared is extracted, as extraction of values.
Further, the method also includes:
Based on the extraction of values of each group in big data to be compared, according to grouping voting mechanism, acquisition is embedded in big number to be compared
The second digital watermarking in；
The second digital watermarking is decrypted based on shared key, obtains the second byte stream watermark；
It is whether consistent by comparing the second byte stream watermark and raw bytes flowing water print, and then judge that big data to be compared is
It is no counterfeit.
Further, the method also includes:
By comparing the ratio of the second byte stream watermark and the different byte of raw bytes flowing water India and China, original big number is obtained
According to the ratio being tampered.
Further, the method also includes:
By comparing the difference of value embedded and extraction of values in each item record, the note being tampered in big data to be compared is determined
Record.
Further, described to be based on digital finger-print, all records in original big data are divided into n group, comprising:
All records in original big data are divided into n group, n is the pixel number of the two-value picture watermark；
Corresponding i-th group of each digital finger-print determines as follows: digital finger-print is converted into integer again divided by number
After watermark, the remainder taken is i；Wherein, i is integer more than or equal to 0, and n > i.
Further, the method also includes:
Integer is converted by moving decimal point by the real-coded GA in big data in every record；
Using a position in digital watermarking as value embedded, it is embedded into the numeric type data in big data in every record
In.
According to embodiments of the present invention, a kind of big data traceability system is provided, comprising:
First conversion module prints raw bytes flowing water for two-value picture watermark to be converted to raw bytes flowing water print
It carries out encryption and obtains digital watermarking and shared key；
Second conversion module, for by original big data in every record major key and text data be converted into, and it is every
Item records corresponding digital finger-print；
All records in original big data are divided into n group, n is the two-value for being based on digital finger-print by grouping module
The pixel number of picture watermark；Every records and records corresponding digital finger-print, the corresponding two-value picture water with every
One pixel of India and China, i.e. in digital watermarking a position；
It is embedded in module, for using a position in digital watermarking as value embedded, being embedded into big data in every record
Numeric type data in.
According to embodiments of the present invention, a kind of electronic equipment is provided, including memory, processor and storage are on a memory simultaneously
The computer program that can be run on a processor, the processor realize big data described in any of the above-described when executing described program
The step of source tracing method.
According to embodiments of the present invention, a kind of non-transient computer readable storage medium is provided, computer journey is stored thereon with
Sequence, when which is executed by processor the step of big data source tracing method described in realization any of the above-described.
The embodiment of the present invention provides a kind of big data source tracing method and system, which comprises by two-value picture watermark
It is converted to raw bytes flowing water print, encryption is carried out to raw bytes flowing water print and obtains digital watermarking and shared key；It will be original big
Major key and text data in data in every record are converted into, and record corresponding digital finger-print with every；Referred to based on number
All records in original big data are divided into n group by line, and n is the pixel number of the two-value picture watermark；Every record and
Corresponding digital finger-print, a pixel, i.e. one in digital watermarking in the corresponding two-value picture watermark are recorded with every
Position；Using a position in digital watermarking as value embedded, it is embedded into the numeric type data in big data in every record.
The embodiment of the present invention has the advantages that anti-deletion is aggressive strong with optimization algorithm robustness, anti-increase attack
The technical effects such as strong.
Detailed description of the invention
It, below will be to embodiment party in order to illustrate more clearly of embodiments of the present invention or technical solution in the prior art
Formula or attached drawing needed to be used in the description of the prior art are briefly described.It should be evident that the accompanying drawings in the following description is only
It is merely exemplary, it for those of ordinary skill in the art, without creative efforts, can also basis
The attached drawing of offer, which is extended, obtains other implementation attached drawings.
Structure depicted in this specification, ratio, size etc., only to cooperate the revealed content of specification, for
Those skilled in the art understands and reads, and is not intended to limit the invention enforceable qualifications, therefore does not have technical
Essential meaning, the modification of any structure, the change of proportionate relationship or the adjustment of size are not influencing the function of the invention that can be generated
Under effect and the purpose that can reach, should all still it fall in the range of disclosed technology contents obtain and can cover.
Fig. 1 is a kind of big data source tracing method overall flow schematic diagram provided in an embodiment of the present invention；
Fig. 2 is a kind of big data traceability system overall structure diagram provided in an embodiment of the present invention；
Fig. 3 is a kind of electronic equipment structural schematic diagram provided in an embodiment of the present invention.
Specific embodiment
Embodiments of the present invention are illustrated by particular specific embodiment below, those skilled in the art can be by this explanation
Content disclosed by book is understood other advantages and efficacy of the present invention easily, it is clear that described embodiment is the present invention one
Section Example, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not doing
Every other embodiment obtained under the premise of creative work out, shall fall within the protection scope of the present invention.
However, existing big data tracing technology has following defect: algorithm robustness is poor, aggressive poor, the anti-increase of anti-deletion
Attack difference etc..In order to solve the above technical problems, the embodiment of the present invention provides a kind of big data source tracing method.Such as Fig. 1, this hair is shown
The executing subject of the overall flow schematic diagram of bright embodiment big data source tracing method, the big data source tracing method can be service
Device, PC and other smart terminal products with same processing capacity, the embodiment of the present invention are not especially limited.It is described
Method specifically includes following steps.
Two-value picture watermark is converted to raw bytes flowing water print, carries out encryption to raw bytes flowing water print and obtain by step S1
Obtain digital watermarking and shared key.
Wherein, the embodiment of the present invention obtains the source of digital watermarking.Bianry image (Binary Image) refers to image
On each pixel only there are two types of possible value or tonal gradation state, people are through common black and white B&W (Black and
White), monochrome image indicates bianry image.Two-value picture watermark is converted to raw bytes flowing water print first, it is specific to convert
Method can use conversion method in the prior art, and the embodiment of the present invention is not especially limited.Further, it is based on the prior art
In AES-192 (Advanced Encryption Standard 192) advanced enciphering and deciphering algorithm to raw bytes flowing water print into
Row encryption, obtains encrypted digital watermarking and shared code key.Shared code key is shared to enjoy User ID binding together, for number
It is transferred when watermark is decrypted.
Further, digital watermarking (Digital Watermarking) technology is by some identification informations (i.e. digital watermarking)
It is directly embedded into digital carrier (including multimedia, document, software etc.) or secondary indication (structure of modification specific region),
And the use value of original vector is not influenced, be also not easy to be ascertained and is modified again.But it can be identified and be recognized by producer.It is logical
It crosses these and hides information in the carrier, can achieve confirmation creator of content, buyer, transmission secret information or judgement and carry
The purpose of whether body is tampered.Digital watermarking be protection information security, realize it is anti-fake trace to the source, the effective way of copyright protection, be
The important branch and research direction in Investigation of Information Hiding Technology field.
Step S2, by original big data in every record major key and text data be converted into, it is opposite with every record
The digital finger-print answered.
Wherein, original big data is defaulted as by the processed big data of structuring.It include several in original big data
It records, again includes several text entries in every record.The embodiment of the present invention by original big data every record in major key
It is converted into text data, records corresponding digital finger-print with every.The conversion method can use in the prior art
Hash algorithm (SHA), the embodiment of the present invention is not especially limited.
Step S3 is based on digital finger-print, all records in original big data is divided into n group, n is the two-value picture water
The pixel number of print；Every records and records corresponding digital finger-print with every, corresponds to one in the two-value picture watermark
A pixel, i.e. in digital watermarking a position.
Further, all records in original big data are grouped first, grouping number n is the watermark of two-value picture
In pixel number.Such as two-value picture watermark is 40*50, then all records in original big data is divided into 2000
It is very big due to recording data volume in original big data in group, the corresponding at least one set of data of each pixel, each number in picture
Corresponding i-th group of fingerprint determines as follows: digital finger-print is converted into integer again divided by the resolution ratio of digital watermarking, takes
Remainder is i；Wherein, i is integer more than or equal to 0, and n > i.
Step S4 is embedded into the numerical value in big data in every record using a position in digital watermarking as value embedded
In type data.
Further, the method also includes:
By in big data to be compared in every record major key and text data be converted into, record corresponding the with every
Two digital finger-prints；
Based on the second digital finger-print, all records in big data to be compared are divided into n group, n is the two-value picture water
The pixel number of print；Every records and records corresponding second digital finger-print, the corresponding two-value picture watermark with every
In a pixel, i.e. a position in the second digital watermarking；
The position being embedded in each group in numeric type data in big data to be compared is extracted, as extraction of values.
Further, the method also includes extraction of values based on each group in big data to be compared, according to grouping voting machine
System obtains the second digital watermarking being embedded in big data to be compared；
The second digital watermarking is decrypted based on shared key, obtains the second byte stream watermark；
It is whether consistent by comparing the second byte stream watermark and raw bytes flowing water print, and then judge that big data to be compared is
It is no counterfeit.
It is also further, the method also includes: it is different by comparing the second byte stream watermark and raw bytes flowing water India and China
The ratio of sample byte obtains the ratio that original big data is tampered.
Further, the method also includes difference by comparing value embedded and extraction of values in each item record, determine to
Compare the record being tampered in big data.
Further, described to be based on digital finger-print, all records in original big data are divided into n group, comprising:
All records in original big data are divided into n group, n is the pixel number of the two-value picture watermark；
Corresponding i-th group of each digital finger-print determines as follows: digital finger-print is converted into integer again divided by number
After watermark, the remainder taken is i；Wherein, i is integer more than or equal to 0, and n > i.
Further, the method also includes:
Integer is converted by moving decimal point by the real-coded GA in big data in every record；
Using a position in digital watermarking as value embedded, it is embedded into the numeric type data in big data in every record
In.It restores after completing watermark insertion as real-coded GA.
The embodiment of the present invention provides a kind of big data source tracing method, which comprises is converted to two-value picture watermark
Raw bytes flowing water print carries out encryption to raw bytes flowing water print and obtains digital watermarking and shared key；It will be in original big data
Major key and text data in every record are converted into, and record corresponding digital finger-print with every；It, will be former based on digital finger-print
All records are divided into n group in beginning big data, and n is the pixel number of the two-value picture watermark；Every record and with every
Record corresponding digital finger-print, a pixel, i.e. in digital watermarking a position in the corresponding two-value picture watermark；It will count
A position in word watermark is embedded into the numeric type data in big data in every record as value embedded.The present invention is implemented
Example, which has the advantages that, solves in the prior art that algorithm robustness is poor, and anti-deletions is aggressive poor, and anti-increase is attacked difference etc. and asked
Topic.
On the basis of the above embodiment of the present invention, the method also includes:
By in big data to be compared in every record major key and text data be converted into, record corresponding the with every
Two digital finger-prints；
Based on the second digital finger-print, all records in big data to be compared are divided into n group, n is the two-value picture water
The pixel number of print；Every records and records corresponding second digital finger-print, the corresponding two-value picture watermark with every
In a pixel, i.e. a position in the second digital watermarking；
The position being embedded in each group in numeric type data in big data to be compared is extracted, as extraction of values.
The embodiment of the present invention provides a kind of big data source tracing method, which comprises is converted to two-value picture watermark
Raw bytes flowing water print carries out encryption to raw bytes flowing water print and obtains digital watermarking and shared key；It will be in original big data
Major key and text data in every record are converted into, and record corresponding digital finger-print with every；It, will be former based on digital finger-print
All records are divided into n group in beginning big data, and n is the pixel number of the two-value picture watermark；Every record and with every
Record corresponding digital finger-print, a pixel, i.e. in digital watermarking a position in the corresponding two-value picture watermark；It will count
A position in word watermark is embedded into the numeric type data in big data in every record as value embedded.The present invention is implemented
Example, which has the advantages that, solves in the prior art that algorithm robustness is poor, and anti-deletions is aggressive poor, and anti-increase is attacked difference etc. and asked
Topic.
On the basis of any above-described embodiment of the invention, the method also includes:
Based on the extraction of values of each group in big data to be compared, according to grouping voting mechanism, acquisition is embedded in big number to be compared
The second digital watermarking in；
The second digital watermarking is decrypted based on shared key, obtains the second byte stream watermark；
It is whether consistent by comparing the second byte stream watermark and raw bytes flowing water print, and then judge that big data to be compared is
It is no counterfeit.
The embodiment of the present invention provides a kind of big data source tracing method, which comprises is converted to two-value picture watermark
Raw bytes flowing water print carries out encryption to raw bytes flowing water print and obtains digital watermarking and shared key；It will be in original big data
Major key and text data in every record are converted into, and record corresponding digital finger-print with every；It, will be former based on digital finger-print
All records are divided into n group in beginning big data, and n is the pixel number of the two-value picture watermark；Every record and with every
Record corresponding digital finger-print, a pixel, i.e. in digital watermarking a position in the corresponding two-value picture watermark；It will count
A position in word watermark is embedded into the numeric type data in big data in every record as value embedded.The present invention is implemented
Example, which has the advantages that, solves in the prior art that algorithm robustness is poor, and anti-deletions is aggressive poor, and anti-increase is attacked difference etc. and asked
Topic.
On the basis of any above-described embodiment of the invention, the method also includes: by comparing the second byte stream watermark
With the ratio of raw bytes flowing water India and China different bytes, the ratio that original big data is tampered is obtained.
The embodiment of the present invention provides a kind of big data source tracing method, which comprises is converted to two-value picture watermark
Raw bytes flowing water print carries out encryption to raw bytes flowing water print and obtains digital watermarking and shared key；It will be in original big data
Major key and text data in every record are converted into, and record corresponding digital finger-print with every；It, will be former based on digital finger-print
All records are divided into n group in beginning big data, and n is the pixel number of the two-value picture watermark；Every record and with every
Record corresponding digital finger-print, a pixel, i.e. in digital watermarking a position in the corresponding two-value picture watermark；It will count
A position in word watermark is embedded into the numeric type data in big data in every record as value embedded.The present invention is implemented
Example, which has the advantages that, solves in the prior art that algorithm robustness is poor, and anti-deletions is aggressive poor, and anti-increase is attacked difference etc. and asked
Topic.
On the basis of any above-described embodiment of the invention, the method also includes: it is embedded in by comparing in each item record
The difference of value and extraction of values, determines the record being tampered in big data to be compared.
The embodiment of the present invention provides a kind of big data source tracing method, which comprises is converted to two-value picture watermark
Raw bytes flowing water print carries out encryption to raw bytes flowing water print and obtains digital watermarking and shared key；It will be in original big data
Major key and text data in every record are converted into, and record corresponding digital finger-print with every；It, will be former based on digital finger-print
All records are divided into n group in beginning big data, and n is the pixel number of the two-value picture watermark；Every record and with every
Record corresponding digital finger-print, a pixel, i.e. in digital watermarking a position in the corresponding two-value picture watermark；It will count
A position in word watermark is embedded into the numeric type data in big data in every record as value embedded.The present invention is implemented
Example, which has the advantages that, solves in the prior art that algorithm robustness is poor, and anti-deletions is aggressive poor, and anti-increase is attacked difference etc. and asked
Topic.
It is described to be based on digital finger-print on the basis of any above-described embodiment of the invention, by all in original big data
Record is divided into n group, comprising:
All records in original big data are divided into n group, n is the pixel number of the two-value picture watermark；
Corresponding i-th group of each digital finger-print determines as follows: digital finger-print is converted into integer again divided by number
After watermark, the remainder taken is i；Wherein, i is integer more than or equal to 0, and n > i.
The embodiment of the present invention provides a kind of big data source tracing method, which comprises is converted to two-value picture watermark
Raw bytes flowing water print carries out encryption to raw bytes flowing water print and obtains digital watermarking and shared key；It will be in original big data
Major key and text data in every record are converted into, and record corresponding digital finger-print with every；It, will be former based on digital finger-print
All records are divided into n group in beginning big data, and n is the pixel number of the two-value picture watermark；Every record and with every
Record corresponding digital finger-print, a pixel, i.e. in digital watermarking a position in the corresponding two-value picture watermark；It will count
A position in word watermark is embedded into the numeric type data in big data in every record as value embedded.The present invention is implemented
Example, which has the advantages that, solves in the prior art that algorithm robustness is poor, and anti-deletions is aggressive poor, and anti-increase is attacked difference etc. and asked
Topic.
On the basis of any above-described embodiment of the invention, the method also includes:
Integer is converted by the real-coded GA in big data in every record；
Using a position in digital watermarking as value embedded, it is embedded into the numeric type data in big data in every record
In.
The embodiment of the present invention provides a kind of big data source tracing method, which comprises is converted to two-value picture watermark
Raw bytes flowing water print carries out encryption to raw bytes flowing water print and obtains digital watermarking and shared key；It will be in original big data
Major key and text data in every record are converted into, and record corresponding digital finger-print with every；It, will be former based on digital finger-print
All records are divided into n group in beginning big data, and n is the pixel number of the two-value picture watermark；Every record and with every
Record corresponding digital finger-print, a pixel, i.e. in digital watermarking a position in the corresponding two-value picture watermark；It will count
A position in word watermark is embedded into the numeric type data in big data in every record as value embedded.The present invention is implemented
Example, which has the advantages that, solves in the prior art that algorithm robustness is poor, and anti-deletions is aggressive poor, and anti-increase is attacked difference etc. and asked
Topic.
On the basis of any above-described embodiment of the invention, the embodiment of the present invention provides a kind of big data traceability system.Such as
Fig. 2 shows the overall structure diagram of big data traceability system of the embodiment of the present invention.It comprises the following modules.
First conversion module 210, for two-value picture watermark to be converted to raw bytes flowing water print, to raw bytes flowing water
Print carries out encryption and obtains digital watermarking and shared key.
Wherein, the embodiment of the present invention obtains the source of digital watermarking.Bianry image (Binary Image) refers to image
On each pixel only there are two types of possible value or tonal gradation state, people are through common black and white, B&W, monochrome image table
Show bianry image.Two-value picture watermark is converted to raw bytes flowing water print first, specific conversion method can use existing
Conversion method in technology, the embodiment of the present invention are not especially limited.Further, solution is added based on AES-192 in the prior art
Close algorithm encrypts raw bytes flowing water print, obtains encrypted digital watermarking and shared code key.Shared code key it is shared with
Shared User ID binding, is transferred when for digital watermarking being decrypted.
Further, digital watermarking (Digital Watermarking) technology is by some identification informations (i.e. digital watermarking)
It is directly embedded into digital carrier (including multimedia, document, software etc.) or secondary indication (structure of modification specific region),
And the use value of original vector is not influenced, be also not easy to be ascertained and is modified again.But it can be identified and be recognized by producer.It is logical
It crosses these and hides information in the carrier, can achieve confirmation creator of content, buyer, transmission secret information or judgement and carry
The purpose of whether body is tampered.Digital watermarking be protection information security, realize it is anti-fake trace to the source, the effective way of copyright protection, be
The important branch and research direction in Investigation of Information Hiding Technology field.
First conversion module 220, for by original big data every record in major key and text data be converted into, with
Every records corresponding digital finger-print.
Wherein, original big data is defaulted as by the processed big data of structuring.It include several in original big data
It records, again includes several text entries in every record.The embodiment of the present invention by original big data every record in major key
It is converted into text data, records corresponding digital finger-print with every.The conversion method can use in the prior art
Hash algorithm (SHA), the embodiment of the present invention is not especially limited.
All records in original big data are divided into n group, n is described for being based on digital finger-print by grouping module 230
The pixel number of two-value picture watermark；Every records and records corresponding digital finger-print, the corresponding binary map with every
A pixel in piece watermark, i.e. in digital watermarking a position.
Further, all records in original big data are grouped first, grouping number n is the watermark of two-value picture
In pixel number.Such as two-value picture watermark is 40*50, then all records in original big data is divided into 2000
It is very big due to recording data volume in original big data in group, the corresponding at least one set of data of each pixel, each number in picture
Corresponding i-th group of fingerprint determines as follows: digital finger-print be converted into integer again divided by digital watermarking after, the remainder taken is
i；Wherein, i is integer more than or equal to 0, and n > i.
It is embedded in module 240, for using a position in digital watermarking as value embedded, being embedded into every record in big data
In numeric type data in.
The embodiment of the present invention provides a kind of big data traceability system, the system comprises: the first conversion module is used for two
Value picture watermark is converted to raw bytes flowing water print, carries out encryption acquisition digital watermarking to raw bytes flowing water print and shares close
Key；Second conversion module, for by original big data every record in major key and text data be converted into, with every record
Corresponding digital finger-print；All records in original big data are divided into n group, n for being based on digital finger-print by grouping module
For the pixel number of the two-value picture watermark；Every record and record corresponding digital finger-print with every, it is corresponding described in
A pixel in two-value picture watermark, i.e. in digital watermarking a position；It is embedded in module, for by a position in digital watermarking
As value embedded, it is embedded into the numeric type data in big data in every record.The embodiment of the present invention has the advantages that solution
The problems such as algorithm robustness in the prior art of having determined is poor, and anti-deletion is aggressive poor, anti-increase attack difference.
Fig. 3 illustrates the entity structure schematic diagram of a kind of electronic equipment, as shown in figure 3, the electronic equipment may include: place
Manage device (processor) 301, communication interface (Communications Interface) 302,303 He of memory (memory)
Communication bus 304, wherein processor 301, communication interface 302, memory 303 complete mutual lead to by communication bus 304
Letter.Processor 301 can call the logical order in memory 303, to execute following method: two-value picture watermark is converted to
Raw bytes flowing water print carries out encryption to raw bytes flowing water print and obtains digital watermarking and shared key；It will be in original big data
Major key and text data in every record are converted into, and record corresponding digital finger-print with every；It, will be former based on digital finger-print
All records are divided into n group in beginning big data, and n is the pixel number of the two-value picture watermark；Every record and with every
Record corresponding digital finger-print, a pixel, i.e. in digital watermarking a position in the corresponding two-value picture watermark；It will count
A position in word watermark is embedded into the numeric type data in big data in every record as value embedded.
In addition, the logical order in above-mentioned memory 303 can be realized by way of SFU software functional unit and conduct
Independent product when selling or using, can store in a computer readable storage medium.Based on this understanding, originally
Substantially the part of the part that contributes to existing technology or the technical solution can be in other words for the technical solution of invention
The form of software product embodies, which is stored in a storage medium, including some instructions to
So that a computer equipment (can be personal computer, server or the network equipment etc.) executes each implementation of the present invention
The all or part of the steps of example the method.And storage medium above-mentioned include: USB flash disk, mobile hard disk, read-only memory (ROM,
Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic or disk etc. it is various
It can store the medium of program code.
The apparatus embodiments described above are merely exemplary, wherein described, unit can as illustrated by the separation member
It is physically separated with being or may not be, component shown as a unit may or may not be physics list
Member, it can it is in one place, or may be distributed over multiple network units.It can be selected according to the actual needs
In some or all of the modules achieve the purpose of the solution of this embodiment.Those of ordinary skill in the art are not paying creativeness
Labour in the case where, it can understand and implement.
Through the above description of the embodiments, those skilled in the art can be understood that each embodiment can
It realizes by means of software and necessary general hardware platform, naturally it is also possible to pass through hardware.Based on this understanding, on
Stating technical solution, substantially the part that contributes to existing technology can be embodied in the form of software products in other words, should
Computer software product may be stored in a computer readable storage medium, such as ROM/RAM, magnetic disk, CD, including several fingers
It enables and using so that a computer equipment (can be personal computer, server or the network equipment etc.) executes each implementation
Method described in certain parts of example or embodiment.
Although above having used general explanation and specific embodiment, the present invention is described in detail, at this
On the basis of invention, it can be made some modifications or improvements, this will be apparent to those skilled in the art.Therefore,
These modifications or improvements without departing from theon the basis of the spirit of the present invention are fallen within the scope of the claimed invention.
Claims (10)
1. a kind of big data source tracing method characterized by comprising
By two-value picture watermark be converted to raw bytes flowing water print, to raw bytes flowing water print carry out encryption obtain digital watermarking and
Shared key；
By in original big data every record in major key and text data be converted into, record corresponding number with every and refer to
Line；
Based on digital finger-print, all records in original big data are divided into n group, n is the pixel of the two-value picture watermark
Number；Every records and records corresponding digital finger-print with every, a pixel in the corresponding two-value picture watermark counts
A position in word watermark；
Using a position in digital watermarking as value embedded, it is embedded into the numeric type data in big data in every record.
2. big data source tracing method according to claim 1, which is characterized in that the method also includes:
By in big data to be compared every record in major key and text data be converted into, with every record it is corresponding second number
Word fingerprint；
Based on the second digital finger-print, all records in big data to be compared are divided into n group, n is the two-value picture watermark
Pixel number；Every records and records corresponding second digital finger-print with every, corresponds to one in the two-value picture watermark
A pixel, i.e. a position in the second digital watermarking；
The position being embedded in each group in numeric type data in big data to be compared is extracted, as extraction of values.
3. big data source tracing method according to claim 2, which is characterized in that the method also includes:
Based on the extraction of values of each group in big data to be compared, according to grouping voting mechanism, acquisition is embedded in big data to be compared
The second digital watermarking；
The second digital watermarking is decrypted based on shared key, obtains the second byte stream watermark；
It is whether consistent by comparing the second byte stream watermark and raw bytes flowing water print, and then judge whether big data to be compared imitates
It emits.
4. big data source tracing method according to claim 3, which is characterized in that the method also includes:
By comparing the ratio of the second byte stream watermark and the different byte of raw bytes flowing water India and China, original big data quilt is obtained
The ratio distorted.
5. big data source tracing method according to claim 3, which is characterized in that the method also includes:
By comparing the difference of value embedded and extraction of values in each item record, the record being tampered in big data to be compared is determined.
6. big data source tracing method according to claim 1, which is characterized in that it is described to be based on digital finger-print, it will be original big
All records are divided into n group in data, comprising:
All records in original big data are divided into n group, n is the pixel number of the two-value picture watermark；
Corresponding i-th group of each digital finger-print determines as follows: digital finger-print is converted into integer again divided by digital watermarking
Resolution ratio, the remainder taken be i；Wherein, i is integer more than or equal to 0, and n > i.
7. big data source tracing method according to claim 1, which is characterized in that the method also includes:
Integer is converted by moving decimal point by the real-coded GA in big data in every record；
Using a position in digital watermarking as value embedded, it is embedded into the numeric type data in big data in every record.
8. a kind of big data traceability system characterized by comprising
First conversion module prints raw bytes flowing water and carries out for two-value picture watermark to be converted to raw bytes flowing water print
Encryption obtains digital watermarking and shared key；
Second conversion module, for by original big data every record in major key and text data be converted into, with every remember
It uses videotape to record corresponding digital finger-print；
All records in original big data are divided into n group, n is the two-value picture for being based on digital finger-print by grouping module
The pixel number of watermark；Every records and records corresponding digital finger-print with every, corresponds in the two-value picture watermark
One pixel, i.e. in digital watermarking a position；
It is embedded in module, for using a position in digital watermarking as value embedded, being embedded into the number in big data in every record
In value type data.
9. a kind of electronic equipment including memory, processor and stores the calculating that can be run on a memory and on a processor
Machine program, which is characterized in that the processor realizes the big data as described in any one of claim 1 to 7 when executing described program
The step of source tracing method.
10. a kind of non-transient computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer
It is realized when program is executed by processor as described in any one of claim 1 to 7 the step of big data source tracing method.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111385102A (en) * | 2020-03-20 | 2020-07-07 | 浙江传媒学院 | Video copyright transaction traceability method based on parent-child chain |
| CN113032744A (en) * | 2021-03-29 | 2021-06-25 | 吉林省容汇科技服务中心(有限合伙) | Digital watermark all-in-one system |
| CN117473470A (en) * | 2023-12-28 | 2024-01-30 | 深圳昂楷科技有限公司 | Dynamic watermark generation method and system and watermark tracing method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102184243A (en) * | 2011-05-17 | 2011-09-14 | 沈阳化工大学 | Text-type attribute-based relational database watermark embedding method |
| CN104866735A (en) * | 2015-05-22 | 2015-08-26 | 电子科技大学 | Embedding and extraction and verification method of value-type relational database watermark |
| CN109360143A (en) * | 2018-09-21 | 2019-02-19 | 北京仁信证科技有限公司 | A kind of digital watermark embedding and extracting method and system for big data |
-
2019
- 2019-04-24 CN CN201910335999.8A patent/CN110069907A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102184243A (en) * | 2011-05-17 | 2011-09-14 | 沈阳化工大学 | Text-type attribute-based relational database watermark embedding method |
| CN104866735A (en) * | 2015-05-22 | 2015-08-26 | 电子科技大学 | Embedding and extraction and verification method of value-type relational database watermark |
| CN109360143A (en) * | 2018-09-21 | 2019-02-19 | 北京仁信证科技有限公司 | A kind of digital watermark embedding and extracting method and system for big data |
Non-Patent Citations (3)
| Title |
|---|
| 崔海亭: "基于公钥密码学的关系数据库水印算法研究", 《中国优秀硕士学位论文全文数据库》 * |
| 曹再辉: "基于图像的数据库水印算法研究", 《中国优秀硕士学位论文全文数据库》 * |
| 马瑞敏等: "一种基于混沌加密的关系数据库水印算法", 《南通大学学报(自然科学版)》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111385102A (en) * | 2020-03-20 | 2020-07-07 | 浙江传媒学院 | Video copyright transaction traceability method based on parent-child chain |
| CN111385102B (en) * | 2020-03-20 | 2021-05-11 | 浙江传媒学院 | Video copyright transaction traceability method based on parent-child chain |
| CN113032744A (en) * | 2021-03-29 | 2021-06-25 | 吉林省容汇科技服务中心(有限合伙) | Digital watermark all-in-one system |
| CN117473470A (en) * | 2023-12-28 | 2024-01-30 | 深圳昂楷科技有限公司 | Dynamic watermark generation method and system and watermark tracing method |
| CN117473470B (en) * | 2023-12-28 | 2024-05-07 | 深圳昂楷科技有限公司 | Dynamic watermark generation method and system and watermark tracing method |
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