CN109299038A - A kind of mass data summarization generation system and method suitable for block chain - Google Patents
A kind of mass data summarization generation system and method suitable for block chain Download PDFInfo
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- CN109299038A CN109299038A CN201810999707.6A CN201810999707A CN109299038A CN 109299038 A CN109299038 A CN 109299038A CN 201810999707 A CN201810999707 A CN 201810999707A CN 109299038 A CN109299038 A CN 109299038A
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Abstract
The invention discloses a kind of mass data summarization generation system and method suitable for block chain, the operation that the system executes includes carrying out the generation of full dose data summarization to raw data set, the abstract for generating single and recording being recorded to single, merges to the abstract of all singles record, judge whether data are tampered.System of the invention can quickly find whether data are tampered, the specific data content being tampered can be quickly navigated in the case where data are tampered simultaneously, and due to the summary info of the initial data or every data that do not have to storage full dose, greatly reduce the data volume of storage, summary info is stored in the anti-tamper data-storage system of this distribution of block chain by the system, greatly improves the safety and confidence level of data.
Description
Technical field
The invention belongs to data summarization technical field, relates in particular to a kind of mass data suitable for block chain and pluck
Generate system and method.
Background technique
With the arriving of big data era, the shared and propagation of data has become various industries in society and pays close attention at present
Important Problems.Big data produces in people live and produce and its important influence, while bringing positive effect,
Also certain risk has been brought.Big data it is shared with propagating during, all suffer from it is certain distort risk and
It traces to the source to state and ask, once shared produce the case where being tampered with data that are propagating, the effect using data of user can be generated
Very big influence.
As internet is deeply applied, cloud computing moves to maturity and smart phone is largely general for the information equipment of representative
And Chinese society has preliminarily formed the blank of information-intensive society, establishes for the industrial upgrading of China, social transformation, reform and innovation
Basis also produces strong demand to the safety of data information and sharing application.
Currently in order to the problem of reply data tampering, common technological means has data file abstract technology, i.e., in data
After generating file, document production technology can be used, and then data is effectively prevent to be tampered.The method usually made a summary has
Cryptographic Hash, MD5 value of file of file etc..It, can be when using data file to file by carrying out abstract production to file
It is verified, guarantees that file was not tampered with.
But when making a summary to data file, since clip Text is for entire file generated, what is used
In the process if it find that can only judge that data are tampered with when abstract is inconsistent with original digest, but which can not be navigated to
One data is tampered.And it when data itself and is not tampered with, only the sequence of data is changed, and this file is plucked
The method wanted can not also be known.
Summary of the invention
In view of the above-mentioned problems in the prior art, The present invention gives one kind to be plucked for each record
The technical method and finally to summarize.The method can quickly navigate to the specific note being tampered after data are tampered
Record, while the sequence variation of data will not influence the result of judgement.Meanwhile the method can be combined with block chain technology, be answered
For the opening and shares field of data, anti-tamper ability is provided for the data that each is propagated, while obtaining and using to user
Each data the ability traced to the source is provided.
Specifically, the invention adopts the following technical scheme:
A kind of mass data summarization generation system suitable for block chain, the system comprises data-storage systems, full dose
Data summarization module, single docket module, abstract merging module, abstract contrast module, data-storage system is for storing
Raw data set, full dose data summarization module are used to generate the whole abstract of full dose data, and single docket module is for giving birth to
At the abstract that single records, abstract merging module for merging the abstract that all singles record, use by abstract contrast module
Whether it is tampered in discovery data, which is characterized in that the system executes following operation: step 1) carries out raw data set
Full dose data summarization generates, wherein making in the data summarization e that full dose data summarization module generates a full dose to raw data set
Entirety for full dose data makes a summary and is stored in data-storage system；Step 2) records the abstract for generating single record to single,
The digital digest Fi for wherein generating its digital digest as single for each record and recording in single docket module is simultaneously
It is stored in data-storage system, wherein i is the number of specific single record in data set；Step 3) records all singles
Abstract merges, wherein the digital digest that each records is passed to abstract merging module, abstract merging module is by single
The digital digest of record is hashed into one or more Bloom filter, ultimately generates one or more Bloom filters simultaneously
It is stored in data-storage system；Step 4) judges whether data are tampered, wherein using first during using data
The summary info that full dose data summarization module calculates used full dose data obtains the data summarization e ' of corresponding full dose, takes out
Front full dose data summarization e generated plucks calculated result e ' and front full dose data generated in abstract contrast module
It wants e to compare, shows that data are not tampered if consistent, can directly use.Further, if plucked in full dose data
It finds inconsistent in the comparison wanted, shows that data are distorted, then further include step 5) and position the data distorted,
In in single docket module generate the digital digest Fi ' that the corresponding single of used data records, taken from storage system
Front Bloom filter generated out arrives Bloom filter using the digital digest Fi ' that abstract contrast module records single
In carry out lookup matching, until finding that unmatched record is the record that is tampered.Preferably, the data-storage system
For distributed system.In addition, in one embodiment, full dose data summarization generating algorithm is SHA-256 algorithm or MD5
Algorithm.Further, single docket generating algorithm is simple hash algorithm or identical with full dose data summarization module
Algorithm, the digital digest of each of them record all pass to the merging that abstract merging module is made a summary.
Invention also discloses a kind of mass data abstraction generating methods suitable for block chain, which is characterized in that described
Method includes the following steps: that step 1) carries out the generation of full dose data summarization to raw data set, wherein using data summarization algorithm
The data summarization e for generating a full dose to raw data set makes a summary and stores as the entirety of full dose data；Step 2) is right
Single record generates the abstract of single record, wherein generating its digital digest for each record using data summarization algorithm
As single record digital digest Fi and stored, wherein i is the number of specific single record in data set；Step 3)
The abstract of all singles record is merged, wherein the digital digest that single record generated will be recorded for each
It hashes in one or more Bloom filter, ultimately generate one or more Bloom filters and is stored；Step 4)
Judge whether data are tampered, wherein calculating used full dose using data summarization algorithm first during using data
The summary info of data obtains the data summarization e ' of corresponding full dose, takes out front full dose data summarization e generated and carries out
Comparison shows that data are not tampered if consistent, can directly use.Further, if full dose data summarization comparison
Middle discovery is inconsistent, shows that data are distorted, then further includes step 5) and position the data distorted, wherein using number
The digital digest Fi ' that the corresponding single record of used data is generated according to digest algorithm, takes out cloth generated before storage
The digital digest Fi ' that single records is carried out lookup matching into Bloom filter by grand filter, unmatched until finding
Record is the record being tampered.Wherein, the storing process uses distributed system.In one embodiment, full dose number
It is SHA-256 algorithm or MD5 algorithm according to algorithm used in summarization generation.In addition, used in the generation of single docket
Algorithm is simple hash algorithm or algorithm identical with full dose data summarization algorithm, wherein each generated records
The merging all made a summary of digital digest.
The present invention gives a kind of quick anti-tamper data summarizations suitable for block chain to generate system, is using this
System can quickly find whether data are tampered, while can quickly navigate to and be usurped in the case where data are tampered
The specific data content changed, and due to the summary info of the initial data or every data that do not have to storage full dose, subtract significantly
The data volume for having lacked storage, allows the system that summary info is stored in the anti-tamper data of this distribution of block chain
In storage system, the safety and confidence level of data are greatly improved.
Detailed description of the invention
Fig. 1 is the schematic diagram for the method flow that the present invention uses.
Specific embodiment
It, can be fast the present invention is directed to provide a kind of quick anti-tamper data summarization generation method suitable for block chain
Whether the discovery data of speed are tampered, while the specific data being tampered can be quickly navigated to when data are tampered, and nothing
The data of full dose or the independent abstract of every data need to be stored.Nowadays traditional data summarization technology is tampered in data
When, it can not be navigated in the case where no initial data and specifically be tampered content, and the storage of initial data often accounts for
With a large amount of storage resource, can not exist in block chain, and the full dose of initial data is compared to expend and largely calculates money
Source, performance are relatively low.
Technical solution of the present invention is substantially described as follows:
A kind of quick anti-tamper data summarization generation system suitable for block chain, the system comprises data storages
System, full dose data summarization module, single docket module, abstract merging module, abstract contrast module, data storage system
System is used to generate the whole abstract of full dose data, single docket for storing raw data set, full dose data summarization module
Module is used to generate the abstract of single record, and abstract merging module is made a summary for merging the abstract that all singles record
Contrast module is for finding whether data are tampered, which is characterized in that the system executes following operation: step 1) is to original
Data set carries out the generation of full dose data summarization, and wherein full dose data summarization module generates the data summarization e of a full dose, wherein
SHA-256 algorithm can be used or MD5 scheduling algorithm carries out summarization generation, the abstract of generation can recorde this in block chain
In distributed tamper resistant systems；Step 2) generates its digital digest for each record by single docket module again,
Simple hash algorithm or algorithm identical with full dose data summarization module can be used in it, then the number of each record
Word abstract can all pass to the merging that abstract merging module is made a summary；The digital digest that step 3) records each transmits
Abstract merging module is given, the digital digest hash that abstract merging module can record single arrives the grand filtering of one or more cloth
In device, one or more Bloom filters are ultimately generated, these Bloom filters also can recorde in block chain, so far count
According to summarization generation process complete；During step 4) uses data, full dose is calculated using full dose data summarization module first
The summary info of data compares the full dose metadata digest information stored in calculated result and block chain, if consistent
Show that data are not tampered, can directly use.If full dose data summarization is inconsistent in step 5) previous step, show data
It is distorted, each in data is recorded in Bloom filter using abstract contrast module carries out lookup at this time
Match, until finding that unmatched record is the record being tampered.
In order to combine data summarization technology with block chain technology, it is proposed that a set of data summarization technical method, tool
Steps are as follows for body:
We possess an original data set T first, it is assumed that the record number of T is n item, we need to carry out T at this time
Whole digest calculations, algorithm are M (T), and M () is digest algorithm, and optional algorithm has MD5, SHA-256 etc.；
We will first generate the digital digest e of full dose data by digest algorithm at this time, then deposit digital digest e
It is stored in block chain
E=M (T)
Then we pass through data summarization module to each record one digital digest Fi of generation again, and wherein i is i-th
Item record, S () are digest algorithm, and optional algorithm has MD5, SHA-256 etc., and DATAi is i-th record itself, i.e.,
Fi=S (DATAi)
Then the abstract Fi of each record is passed to the merging that abstract merging module is made a summary, abstract by us
Merging module maps the abstract of each record using Bloom filter algorithm Bloom (), ultimately generates the grand mistake of cloth
Filter BF, wherein Fi is the abstract of i-th record, and n is record number, i.e.,
BF=Bloom (F1, F2 ... Fi ... Fn)
We have been completed the generation of data summarization at this time, e and BF can be stored in block chain, in user
Using carrying out verification of data when data.
When user needs using data, the data T ' of full dose is got first, by data summarization module to full dose
Data carry out digest calculations, obtain the full dose abstract e ' of this part of data, i.e.,
E '=M (T ')
The initial data stored in the abstract and block chain abstract e is compared after obtaining full dose abstract e ', if phase
It is same then show that data are not distorted, it can be used, show that data are distorted if different, it is specific to need to continue positioning
Distort content.
The Bloom filter BF of storage is taken out from block chain, each data DATAi ' in data set T ' is logical
It crosses data summarization module and generates abstract Fi ', then lookup matching will be carried out in Fi ' to Bloom filter BF, until not finding not
The record matched, the data record being as tampered.
For the data set being not tampered with only need a full dose data summarization calculating and compare you can get it as a result,
And result is in the case where being tampered, it is only necessary to be searched the summary info that every records into Bloom filter
The content being tampered is navigated to, data storage capacity has not only been reduced but also improves the speed of positioning.
Embodiments thereof are described in detail now with reference to the technical solution of this paper.In order to which its thought is communicated to
Those skilled in the art provide these embodiments hereafter introduced as case.Therefore, these embodiments can
To implement in different forms, to be not limited to these embodiments described here.Moreover, in any possible place,
To make that the same or similar component is presented with like reference characters in the whole instruction and attached drawing.
Fig. 1 is a kind of summarization generation realized according to technical solution proposed in this paper and compares process, in the process,
We, which possess, stores the original data-storage system with data set that is propagating, and there are one summarization generation modules, there is an abstract
Merging module, while there are one contrast modules of making a summary, in addition there are one block chain memory modules.
The raw data set file that we have that one is made a summary first is stored in data-storage system, should
System can be distributed file system such as HDFS etc., can be common single machine file system, can be telefile system
System is such as FTP.
Operation is distorted in order to quickly judge that entire file whether there is, it would be desirable to which data are carried out to raw data file
Abstract processing, we will generate the digital digest e of an entirety, tool by summarization generation module to raw data file at this time
The summarization generation algorithm of body goes to realize by summarization generation module, and summarization generation algorithm can be used various hash algorithms and be counted
It calculates, such as using CRC algorithm, SHA algorithm, MD5 algorithm etc., is plucked by the number that data summarization algorithm generates entire file
E is wanted, we will be stored in the abstract in block chain after abstract e is generated, and prevent malicious user from distorting to abstract e.
So far we have been completed the digital digest generating process to raw data set file, and next we will be right
Each record in data set carries out digital digest generation processing, can be quickly fixed in the case where follow-up data is tampered
Position is to the data content being tampered.
We need to carry out digest calculations to each data that initial data is concentrated by summarization generation module, generate
The digital digest Fi of each data, specific digest algorithm go to realize by summarization generation module, and summarization generation algorithm can make
It is calculated with various hash algorithms, such as using CRC algorithm, SHA algorithm, MD5 algorithm etc., passes through data summarization algorithm
The digital digest Fi of each record is generated, the conjunction that abstract merging module is made a summary can be passed to after these summarization generations
And reduce the space hold of storage.
Abstract merging module can be subsequently received the digital digest Fi of each record, and merging module of making a summary at this time can basis
The size of data volume generates Bloom filter not of uniform size, the size of Bloom filter according to the of different sizes of data volume and
Difference, in order to guarantee probability of miscarriage of justice dropping to acceptable range, while can generate under normal circumstances multiple
Bloom filter, each Bloom filter go Hash digest using different hash algorithms, and purpose will erroneous judgement also for guarantee
Probability is preferably minimized.After the digital digest of all records is all merged by abstract merging module, we are by all cloth
Grand filter is stored in block chain, prevents malicious user from distorting to Bloom filter.
We have been completed the whole summarization generation of initial data and the summarization generation of each record so far
And merging, and be stored in block chain, the treatment process that data use next is described herein.
When we get a data set that reputation has been crossed in block chain, in order to verify the data got
Collection is not maliciously tampered, we can take out the overall digital abstract e of data set from block chain, and then we can pass through abstract
Generation module carries out whole digest calculations to data set, a new overall digital abstract e ' is obtained, next by e ' and we
The abstract e obtained from block chain is compared, and indicates that record set is not distorted maliciously if equal, can directly use.
If it find that e and e ' be not identical, then show that data set is distorted by malice, it would be desirable to which which note judgement has specifically distorted
Record.
At this moment we take out the corresponding Bloom filter of data set from block chain, and then we pass through summarization generation mould
Block successively in record set each record carry out digest calculations, obtain each record digital digest Fi ', then we
By making a summary, contrast module compares lookup in Bloom filter to Fi ', if can find this in Bloom filter
The digital digest of item record, then show that this record is not distorted maliciously, we will continue the comparison of next record, and such as
We can not find the digital digest of this record to fruit in Bloom filter, then show that this record is usurped by malice
Change, we should get off the record storage being tampered at this time, and subsequent conduct is distorted evidence and given at data set provider
Reason.
So far the data summarization generating process of entire data and the verification process in data use process are fully completed.
Embodiments of the present invention are described in detail above in conjunction with attached drawing, but the present invention is not limited to above-mentioned realities
Apply mode, technical field those of ordinary skill within the scope of knowledge, present inventive concept can also not departed from
Under the premise of make a variety of changes.
Claims (10)
1. a kind of mass data summarization generation system suitable for block chain, the system comprises data-storage systems, full dose number
According to summarization module, single docket module, abstract merging module, abstract contrast module, data-storage system is for storing original
Beginning data set, full dose data summarization module are used to generate the whole abstract of full dose data, and single docket module is for generating
The abstract of single record, for merging the abstract that all singles record, abstract contrast module is used for abstract merging module
It was found that whether data are tampered, which is characterized in that the system executes following operation: step 1) carries out full dose to raw data set
Data summarization generates, wherein generating the data summarization e an of full dose as complete to raw data set in full dose data summarization module
The whole of amount data makes a summary and is stored in data-storage system；Step 2 records the abstract for generating single record to single, wherein
The digital digest Fi that generates its digital digest as single for each record and record in single docket module is simultaneously stored
In data-storage system, wherein i is the number of specific single record in data set；The abstract that step 3) records all singles into
Row merges, wherein the digital digest that each records is passed to abstract merging module, abstract merging module records single
Digital digest is hashed into one or more Bloom filter, is ultimately generated one or more Bloom filters and is stored in number
According to storage system；Step 4) judges whether data are tampered, wherein being plucked first using full dose data during using data
It wants the summary info of the used full dose data of module calculating to obtain the data summarization e ' of corresponding full dose, it is generated to take out front
Full dose data summarization e compares calculated result e ' and front full dose data summarization e generated in abstract contrast module,
Show that data are not tampered if consistent, can directly use.
2. being suitable for the mass data summarization generation system of block chain as described in claim 1, which is characterized in that if complete
It measures and finds inconsistent in the comparison of data summarization, show that data are distorted, then further include what step 5) positioning was distorted
Data, wherein the digital digest Fi ' of the corresponding single record of used data is generated in single docket module, from storage system
System takes out front Bloom filter generated, arrives the grand mistake of cloth using the digital digest Fi ' that abstract contrast module records single
Lookup matching is carried out in filter, until finding that unmatched record is the record being tampered.
3. being suitable for the mass data summarization generation system of block chain as claimed in claim 1 or 2, which is characterized in that described
Data-storage system is distributed system.
4. being suitable for the mass data summarization generation system of block chain as claimed in claim 1 or 2, which is characterized in that full dose
Data summarization generating algorithm is SHA-256 algorithm or MD5 algorithm.
5. being suitable for the mass data summarization generation system of block chain as claimed in claim 1 or 2, which is characterized in that single
Docket generating algorithm is simple hash algorithm or algorithm identical with full dose data summarization module, each of them note
The digital digest of record all passes to the merging that abstract merging module is made a summary.
6. a kind of mass data abstraction generating method suitable for block chain, which is characterized in that described method includes following steps:
Step 1) carries out the generation of full dose data summarization to raw data set, wherein generating one to raw data set using data summarization algorithm
The data summarization e of a full dose makes a summary and is stored as the entirety of full dose data；Step 2, which records single, generates single note
The abstract of record is plucked wherein data summarization algorithm is used to generate its digital digest for each record as the number that single records
It wants Fi and is stored, wherein i is the number of specific single record in data set；The abstract that step 3) records all singles into
Row merges, wherein hashing the digital digest for recording single record generated for each to the grand mistake of one or more cloth
In filter, ultimately generates one or more Bloom filters and stored；Step 4) judges whether data are tampered, wherein
During using data, obtained first using the summary info that data summarization algorithm calculates used full dose data corresponding complete
The data summarization e ' of amount takes out front full dose data summarization e generated and compares, shows data without usurping if consistent
Change, can directly use.
7. being suitable for the mass data abstraction generating method of block chain as claimed in claim 6, which is characterized in that if complete
It measures and finds inconsistent in the comparison of data summarization, show that data are distorted, then further include what step 5) positioning was distorted
Data take out storage wherein generating the digital digest Fi ' of the corresponding single record of used data using data summarization algorithm
The digital digest Fi ' that single records is carried out lookup matching into Bloom filter by front Bloom filter generated, until
It was found that unmatched record is the record being tampered.
8. the mass data abstraction generating method suitable for block chain as claimed in claims 6 or 7, which is characterized in that described
Storing process uses distributed system.
9. the mass data abstraction generating method suitable for block chain as claimed in claims 6 or 7, which is characterized in that full dose
It is SHA-256 algorithm or MD5 algorithm that data summarization, which generates used algorithm,.
10. the mass data abstraction generating method suitable for block chain as claimed in claims 6 or 7, which is characterized in that single
Docket generate used in algorithm be simple hash algorithm or algorithm identical with full dose data summarization algorithm, wherein
The merging that the digital digest of each record generated is all made a summary.
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