CN107491472A - A kind of safe shared system of big data platform sensitive data and method based on life cycle - Google Patents
A kind of safe shared system of big data platform sensitive data and method based on life cycle Download PDFInfo
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- CN107491472A CN107491472A CN201710483185.XA CN201710483185A CN107491472A CN 107491472 A CN107491472 A CN 107491472A CN 201710483185 A CN201710483185 A CN 201710483185A CN 107491472 A CN107491472 A CN 107491472A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/254—Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
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- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1097—Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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- G06F2216/00—Indexing scheme relating to additional aspects of information retrieval not explicitly covered by G06F16/00 and subgroups
- G06F2216/03—Data mining
Abstract
The invention provides a kind of safe shared system of big data platform sensitive data and method based on life cycle.The present invention can merge the big data platform under multiple network service, and unified big data storage shared system is provided for the big data business of these network services.For the sensitive information of relational users safety and privacy, the present invention newly sets a data security layer, realizes that sensitive information data and the separation of general service process data are extracted to business process data in the level.For sensitive information data in each network service platform separate storage, and life cycle is set according to level of security for sensitive information data；On this basis, based on life cycle, special secure exchange shared mechanism is realized between each big data platform for sensitive information data.
Description
Technical field
The invention belongs to big data information excavating and analysis field, and in particular to a kind of big data based on life cycle is put down
The safe shared system of platform sensitive data and method.
Background technology
Big data technology is by the storage computing platform with magnanimity DBMS load-bearing capacity, for various computer networks
Caused business datum performs and collects, handles and analyze among Service Operation, excavates mutually interconnection significant among various information
System and changing rule, and it is subject to practical application.
Currently, the various network service operators towards customers on a large scale are all being greatly developed and put down using big data
Platform.For example, bank is arranged and analyzed to the deposit revenue and expenditure of client, credit card trade, loan documentation by big data platform,
The income level and asset size of client is determined, so as in sides such as facility extent determination, loan risk evaluation, finance and money management customizations
Face is offering customers service.The big data platform of shopping at network analyzes use among the commodity selection of client and transaction record information
The consuming capacity and consumption preferences at family, on the one hand can be from being macroscopically predicted to all types of commodity future sales volumes, the opposing party
Face can also perform the targetedly service such as advertisement pushing, customization of individual character towards specific client.The big data of medical system is put down
Platform can be directed to target it is personal obtain its with regard to medical drugs, serious disease perform the operation, have regular physical checkups, immunoprophylaxis, psychological consultation etc.
Record data and carry out specialized analysis, so as to provide the assessment report related to personal health condition.It can be said that big number
According to the foundation of platform, further improve the intelligence degree of various network services, the effective exploitation value of information data.
However, traditional big data platform independently disposes operation, a kind of network by the operator of various network services respectively
The business datum of the lower generation of service is stored, analyzed and applied in the big data platform interior of its own；Big data platform that
This is scattered and isolated, is not carried out cross-platform integrated layout, thus cannot also support to include the comprehensive of multi-platform big data
Close analysis and application.For example, the user's assets and income information under the big data platform of bank can not be by shopping at network platforms
Obtained, unlikely by shopping at network platform application in the analysis to customer consumption ability.
Big data platform it is mutually isolated, be largely in order at examining in terms of information security and privacy of user protection
Consider.Because user is summarized under big data platform enjoys caused whole in network service procedure or most business numbers
According to, wherein the sensitive information for being related to user identity confidentiality, account safety and individual privacy is inevitably loaded with, such as bank
Account, stored value account number, personal name, identification card number, medical insurance card number, phone, address etc., these information are once revealed can be to use
Family life brings serious harm, can also influence the reputation of service provider.
Therefore, the business datum and analysis meter is performed on the basis of business datum that operator can be directed in big data platform
The process data of gained is calculated, is respectively provided with the limitation of access rights, and apply tighter safety measure.Especially for from
The data access request (including the access request submitted from other big data platforms to this platform) of big data platform exterior, will
By fixed preset interface, according to the rule of strict difinition, to carry out data transmission exchanging, so facilitate and big data is put down
Table top imposes the means such as control of authority, authentication, data encryption, log recording to the data output of platform exterior.
However, Develop Data exchange depends on limited preset interface between above-mentioned measure has also resulted in big data platform
With the transmission rule of complexity, it postpones, and big, speed is slow, efficiency is low；Thus, sporadic, a small amount of data exchange can only be carried out.
In fact, distribution of the valuable information among big data is extremely sparse, only magnanimity business datum is entered
Row is arranged, compared, associating, cluster can extract.The big data to be directed in multiple network services realizes comprehensive analysis
And application, it is that can not realize completely by a small amount of data exchange is carried out between the respective big data platform of these network services
, it is necessary to support to realize the mutually shared of magnanimity DBMS between big data platform.
That is, the data sharing of professional platform independence, it is necessary to make multiple network distinguish caused magnanimity level business in servicing
Data common transport and application between each big data platform, this is that existing data exchange ways can not be supported at all
's.
The content of the invention
The defects of in order to overcome above-mentioned prior art to exist, the present invention provide a kind of big data platform based on life cycle
The safe shared system of sensitive data and method.
The present invention can merge the big data platform under multiple network service, be provided for the big data business of these network services
Unified big data storage shared system；These big data platforms can perform to original service data caused by own net service
Big data Treatment Analysis computing, business process data is generated, be put into big data storage shared system；Also, each big data
Platform can utilize big data storage shared system, and the related business of the various network services obtain cross-platformly required for adds
Number evidence, and these data are deployed with the analysis and application of big data rank.
Wherein, for the sensitive information of relational users safety and privacy, the present invention is among big data storage shared system
A data security layer newly is set, realizes sensitive information data and general service process data to business process data in the level
Separation is extracted, and establishes the mapping table of sensitive information data and general service process data；For sensitive information data, in number
Level of security delimited according to safe floor, life cycle is set according to level of security；On this basis, to sensitive information data each
Separate storage under the big data platform of network service；And level of security and life cycle are based on, for sensitive information data each
Special secure exchange shared mechanism, including interim shared storage and conditional special interface are realized between individual big data platform
Exchange.For general service process data, then after further going personal informationization to comb, into data sharing layer, realize
Cross-platform unification stores, shared and application.
The invention provides a kind of safe shared system of big data platform sensitive data, it is characterised in that including：Data are adopted
Collect layer, data analysis layer, data security layer, big data inclusion layer；Wherein described data collection layer, data analysis layer, data peace
Holostrome is deployed among the various network service platforms operation systems of itself；The big data inclusion layer is put down across multiple network services
Platform is disposed, and a unified big data storage inclusion layer is provided for the big data business of each network service platform；
The data collection layer is used to gather caused whole original service data in network service platform running, adds
Enter original service data warehouse；
The data analysis layer is the big data mining analysis platform of network service platform itself, by terms of high parallel
Calculation pattern, towards the data flow formed by original service data, data mining and parser are performed, generation is preliminary by data
Business process data after processing；
Inside the operation system of the data security layer embedded network service platform, for the business process data from magnanimity
The a small amount of sensitive information of central separation, generates the sensitive data containing sensitive information and the general service number without sensitive information respectively
According to；Register mutual corresponding mapping relations of the sensitive data with general service data；To sensitive data inside data security layer
Separate storage and management, wherein setting life cycle for sensitive data, and carried out between network service platform by tight
The exchange of lattice limitation is shared, including the interim shared storage based on life cycle duration and conditional special interface exchange；It is right
Big data inclusion layer is transferred to by data security layer in general service data, for cross-platform shared exchange；
The big data inclusion layer is used for the common industry that will be obtained from the data security layer of the multiple network service platform
Data of being engaged in are unified to be stored, and is provided unified reading and writing data and accessed standard and working specification, so as to each network service platform
The general service data of each network service platform are obtained from the big data inclusion layer and are applied to big data mining analysis.
Preferably, the safe shared system of big data platform sensitive data further comprises：Restricted Fabric Interface,
The call request in short-term that data security layer for from response external network service platform to present networks service platform is sent, it is outer to this
Portion's network service platform provides requested sensitive data；Also, according to the life cycle of sensitive data, limitation is provided
The duration that sensitive data to external network platform can be decrypted and applied.
Preferably, the safe shared system of big data platform sensitive data further comprises：Shared positioned at big data
The interim shared memory of layer, the data security layer of response external network service platform to present networks service platform are sent in short-term
Call request, requested sensitive data is uploaded to the interim shared memory by data security layer, external web services are put down
Platform can obtain sensitive data from the interim shared memory；Also, the interim life of the shared memory based on sensitive data
Cycle is controlled to the shared duration of the sensitive data.
Preferably, the data collection layer includes：
Data adaptation interface, for adapting to the operation system of network service platform data type in itself, form and leading
Go out rule, the output channel as original service data caused by the operation system；
Data acquisition module, for real-time or non real-time from the data adaptation interface original service data；
Data inquiry module, for actively sending inquiry message to the data adaptation interface, and receive the data and fit
The original service data transmitted with interface in response to inquiry message；
Data Verification module, for the original service data for being gathered, enter according to predefined data verification rule
Row checking, empirical tests are rejected or reacquired imperfect or are not inconsistent original service data normally, complete for empirical tests
And the original service data for meeting rule requirement are supplied to data processing module；
Data conversion treatment module, for verifying that qualified original service data carry out ETL and (taken out by Data Verification module
Take, change, load) processing, original service data are converted into standard data format；
Original service data warehouse, for storing the standard data format after data conversion treatment module is changed
Original service data.
Preferably, the big data mining analysis platform of the data analysis layer includes：
Platform inner joint module, the upper strata for receiving network service platform operation system are dispatched, and are transmitted data downwards and are dug
Dig analysis task；And externally export the business process data after being handled through lower floor；
Parallel stream task module, for receiving data mining analysis task, and according to the task, open up at least one
Business stream, generation, maintenance, kill to each task flow are managed；For each task flow from original service data warehouse with number
Original service data are extracted according to the form of stream, there is provided to the different submodules of the data relation analysis module, are realized parallel
Task processing；
Data relation analysis module, including data correlation calculating sub module, data classified calculating submodule and data clusters
Calculating sub module；Above submodule undertakes the task flow to match with itself algorithm types respectively, and it is corresponding to receive task flow
The data flow of original service data, using itself algorithm to the data flow carry out computing, obtain business process data, generate and to
The data flow of platform inner joint module outgoing traffic process data.
The data security layer includes：
Sensitive data extraction module, for business process data, according to predetermined filtering rule, therefrom filter out containing quick
Feel the business process data unit of information, there is provided give sensitive data separation module；The filtered business processing without sensitive information
Data cell, then it is supplied to the big data inclusion layer as general service data；
Sensitive data separation module, add for the business containing sensitive information filtered out by sensitive data extraction module
Work data cell, sensitive data part and general service data division are separated into, and established, storage and maintenance sensitivity number
According to part and the mapping table of general service data division, register separated by same business process data unit in the table
Corresponding relation between sensitive data part out and general service data division；After mapping registration, by general service number
According to big data inclusion layer is partly supplied to, sensitive data part is provided to sensitive data MMU memory management unit and is stored and managed
Reason；
Sensitive data MMU memory management unit, received from sensitive data separation module and store the sensitive data part, and
The life cycle is set for it according to the level of security of sensitive data part；
Data sharing interface, general service data are uploaded to the big data inclusion layer.
It may further be preferable that the Data Data safe floor also includes：De-personalization data comb module, for this
Body is free of the general service data of sensitive information or the general service of sensitive information has been separated by sensitive data separation module
Data division, the data for performing de-personalization information comb.
Preferably, the big data inclusion layer specifically includes：
Shared thesaurus, for receiving general service data from the data security layer of each network service platform, and uniformly
Stored；
Shared interface is standardized, for accessing standard and working specification using unified reading and writing data, is taken for each network
Business platform obtains the general service data of each platform of unified storage among the shared thesaurus by this interface.
The present invention and then provide a kind of big data platform sensitive data secure sharing method based on life cycle, it is special
Sign is, comprises the following steps：
Original service data caused by various network service platforms are gathered, original service data are performed with big data processing point
Computing is analysed, generates business process data；
The sensitive information data comprising sensitive information and the common industry not comprising sensitive information are carried out to business process data
Separation between business process data；For the sensitive data part separated by same business process data unit and common
Business datum part, establish the mapping table of sensitive information data division and general service process data part；For sensitivity
Information data part, level of security delimited, life cycle is set according to level of security；
The sensitive information data separated are stored in each network service platform internal independence；
Using special secure exchange shared mechanism, based on the life cycle, to sensitive information data in each big number
Shared according to severely limited exchange is carried out between platform, including interim shared storage and conditional special interface exchange；
General service data are delivered to the data sharing layer across multiple network service platforms, cross-platform uniformly deposit
Storage, shared and application.
Preferably, exchanged by special interface according to the life cycle of sensitive data, limitation and be provided to outside
The duration that the sensitive data of the network platform can be decrypted and applied；Or the life cycle according to sensitive data, limitation are interim
The shared duration of shared storage.
In fact, concerning user security and the sensitive information of privacy, come relative to the business datum amount of whole network platform
Say, be very sparse.The present invention separates a small amount of sensitive information among the business datum of magnanimity, by each network
The big data platform separate storage of service and management, and the exchange strictly limited between platforms is shared.Wherein, establish
Exchange shared mechanism based on life cycle, ensure acquisition of the outside platform to this platform sensitive information and using being all interim
, can not be repeatedly.And for general service data, then put down using unified store of the present invention with shared mechanism, each big data
Platform can be obtained and analyzed, and breached the bottleneck that interface data exchanges in the prior art, can be realized magnanimity rank
Cross-platform big data application.
Brief description of the drawings
Fig. 1 is the hierarchy schematic diagram of the safe shared system of big data platform sensitive data of the present invention；
Fig. 2 is the modular structure schematic diagram of data collection layer of the present invention；
Fig. 3 is the big data mining analysis console module structural representation of data analysis layer of the present invention；
Fig. 4 is the module architectures schematic diagram of data security layer of the present invention；
Fig. 5 is the module architectures schematic diagram of big data inclusion layer of the present invention.
Embodiment
Below by embodiment, and with reference to accompanying drawing, technical scheme is described in further detail.
As shown in figure 1, the safe shared system of big data platform sensitive data provided by the invention is divided into data collection layer
1st, data analysis layer 2, data security layer 3, big data inclusion layer 4.
Data collection layer 1 towards each network service, such as Web bank, shopping at network, medical treatment ＆ health information system etc.,
Caused whole original service data in these network service runnings are gathered, add original service data warehouse；For example, net
Bank account revenue and expenditure flowing water list, loan and interest refund record and the signal card to go to bank is swiped the card bill etc., the purchase of shopping online
Thing car record, transaction record and Payment Records etc., registration and medical consultation record, bill of writing a prescription, the physical examination report of medical treatment ＆ health information system
List etc. is accused, can be used as original service data.
Fig. 2 is the modular structure schematic diagram of data collection layer of the present invention；It is each in bank, shopping online, medical information etc.
The data collection layer module architectures for belonging to the network service are independently deployed among the operation system of network service itself.
The framework of data collection layer 1 in each network service operation system includes：
Data adaptation interface 101, according to the berthing mechanism of network service own service system, set in the operation system
Data adaptation interface 101, the data adaptation interface 101 adapt to data type, form and the derived rule of operation system in itself,
The output channel of caused original service data during being runed as operation system.
Data acquisition module 102, under the exportable original service data ready state of data adaptation interface 101,
The ready notification message sent according to data adaptation interface 101, real-time or non real-time from the interface original service data.
Data inquiry module 103, the module actively sends inquiry message to data adaptation interface 101, and receives the interface
The original service data transmitted in response to inquiry message.Real-time or non real-time reception for data acquisition module 102 occurs
When interrupting or data incompleteness be present, the data inquiry module 103 can send the inquiry message, receive original service number again
According to ensure the integrality of business datum., can also be by this when big data application needs active inquiry target service data
Data inquiry module 103 sends inquiry message, actively extracts original service data.
Data Verification module 104, for original to being obtained by data acquisition module 102 and data inquiry module 103
Business datum, according to adaptation network service operation system concrete condition, predefined data verification is regular, to the original industry
Business data are verified.Judge imperfect for empirical tests or be not inconsistent data normally, such as format error, mess code, numerical value
Malfunctioned beyond limit value, consistency checking etc., send feedback, director data from Data Verification module 104 to data inquiry module 103
Inquiry module 103 regains these defective original service data.If through data inquiry module 103, inquiry is still repeatedly
It can not obtain complete and meet the original service data of proof rule requirement, then it is problematic to reject these for Data Verification module 104
Data.Original service data that are complete for empirical tests and meeting regular requirement, there is provided enter to data conversion treatment module 105
Row further processing.
Data conversion treatment module 105, for verifying qualified original service data by Data Verification module 104, by counting
ETL is carried out according to modular converter 105 and (extracts, change, loading) processing, by the multi-sourcing, isomerization of network service operation system itself
Data format to Uniform data format conversion, generation be suitable to data analysis layer 2 perform big data Treatment Analysis criterion numeral
According to the original service data of form.
Original service data warehouse 106, it is independent to be erected at the inside of network service operation system, store through data conversion
Processing module 105 changed after standard data format original service data.For example, Web bank, shopping at network with
And the original service data warehouse of data collection layer of the present invention is respectively embedded among the operation system of medical treatment ＆ health information service
106。
By the big data mining analysis platform of the network services such as bank, shopping online, medical treatment ＆ health itself as the present invention
Data analysis layer 2.For the original service data stored in the original service data warehouse 106 of data collection layer 1, each net
Network service call is from the big data mining analysis platform in data analysis layer 2, with high parallel computation schema, towards by original
The data flow that business datum is formed, performs data mining and parser, and the business that generation is passed through after data preparatory processing adds
Number evidence.
Fig. 3 shows the big data mining analysis console module structural representation of data analysis layer.As shown in figure 3, this is big
Data mining analysis platform from top to bottom includes successively：
Platform inner joint module 201, for receiving the scheduling of system upper strata, data mining analysis task is transmitted downwards；And
Business process data after externally output is handled through lower floor.The big data mining analysis platform of data analysis layer 2 as bank,
The execution carrier of big data mining analysis function inside the various network service operation systems such as shopping online, health care, lead to
Platform inner joint module 201 is crossed, receives the scheduler task instruction manually or automatically assigned on the network service operation system upper strata,
Data mining analysis task is obtained, and the data mining analysis task is passed down to parallel stream task module 202.In platform
Interface module 201 provides the human-computer interaction interface of close friend, and the operator of big data platform can easily start on interface to dig
Analysis task is dug, selectes the algorithmic rule of mining analysis, the result feedback time requirement of input mining analysis task, selection is excavated
Above Parameter Switch is that the scheduler task manually assigned instructs by the target data ranges and training data scope of analysis, interface.
System upper strata can also assign a task dispatch command automatically, for example, being accumulated not according in the original service data warehouse 106
The magnitude of processed original service data, or according to newly-increased data mining analysis demand, the scheduler task is assigned automatically
Instruction, creates new task.For the business process data obtained by performing data mining analysis task, by platform
Interface module 201 is externally exported, and on the one hand can be supplied to the big data upper strata of network service operation system itself, be imposed
The analysis and application of more depth, on the other hand, as the emphasis of the present invention, business process data is supplied to data security layer 3,
Exchanged to carry out follow-up cross-platform sharing.
Parallel stream task module 202 receives data mining analysis task from platform inner joint module 201, and according to this
Business, opens up at least one task flow, in general opens up the task flow of multiple functioning in parallel；Parallel stream task module 202 is every
Individual task flow extracts original service data from original service data warehouse 106 in the form of data flow, such as according to 200M/s's
Speed is original to extract for unit with predetermined data cell (such as a tables of data, a data block, a data record)
Business datum, the data flow for the original service data extracted is supplied to data relation analysis module 203.Parallel stream task mould
Generation, maintenance, kill of the block 202 to each task flow are managed, and the ancestral task number extracted to each task flow
According to data distribution scope, each task flow take system resource be allocated and reclaim.Wherein, dug for same data
Pick analysis task is related to the situation of the polytype algorithms such as data correlation calculating, data classified calculating and data clusters calculating,
Parallel stream task module 202 responds the task and each algorithm types is opened up with a task flow, gives data relation analysis
The different submodules operation of module, realize parallel task processing.
Core of the data relation analysis module 203 as data analysis layer 2, as shown in figure 3, calculating son including data correlation
Module 203A, data classified calculating submodule 203B and data cluster calculation submodule 203C.Above submodule undertake respectively with
The task flow that itself algorithm types matches, and the data flow of the corresponding original service data of task flow is received, calculated using itself
Method carries out computing to the data flow, obtains business process data, generates and processes number to the outgoing traffic of platform inner joint module 201
According to data flow.For example, data correlation calculating sub module 203A can be based on Probabilistic Data Association Algorithm, joint probability association is calculated
At least one of method, Data Association Algorithm for Multi-target, neutral net association algorithm scheduling algorithm, for original service data flow point
The relevance between original service data is analysed, and establishes the associated record table of record relevance, associated record table is added with business
The form output of work data flow.Data classified calculating submodule 203B can be based on Bayes classifier algorithm, decision tree of dividing and ruling
At least one of algorithm, Bagging algorithms, LinearRegression algorithms, data point are performed for original service data
Class calculates, and fills classification type label to original service data according to result of calculation, so as to the number after being labeled
According to as business process data, exported in the form of business processed data stream.Data clusters calculating sub module 203C utilizes cluster
Training dataset is learnt, and performing data clusters to original service data using Data Clustering Algorithms such as K-Means calculates, raw
Exported into cluster set record sheet in the form of business processed data stream.
As the ring of key one of the safe shared mechanism of sensitive data of the present invention based on life cycle, in data analysis layer 2
On the basis of offer business process data, in the operation system of each network service such as bank, shopping online, medical treatment ＆ health
Portion embedded in data security layer 3.Data security layer 3 isolates a small amount of sensitive information among the business process data of magnanimity
Come, generate sensitive data and the general service data without sensitive information；Registration sensitive data is mutual with general service data
The mapping relations of corresponding application.Data security layer 3 is only by the big data platform interior of each network service all the time to sensitive data
Vertical storage and management, and carry out severely limited exchange between platforms and share, including interim shared storage and limited
The special interface of system exchanges.Then further de-personalization data are imposed for general service data by data security layer 3 to comb,
Then big data inclusion layer 4 is given, for cross-platform shared exchange.Wherein, data security layer 3 is established for sensitive information
Exchange shared mechanism based on life cycle, there is provided the sensitive data to outside platform then fails once exceeding life cycle
Or no longer support to share, thus, ensure acquisition of the outside other network service platforms to sensitive information in this platform and profit
Be all it is interim, can not be repeatedly.
Fig. 4 shows the module architectures schematic diagram of data security layer 3 of the present invention.In bank, shopping online, medical treatment ＆ health etc.
Each the data security layer 3 inside operation system is included with lower module network service：
Sensitive data extraction module 301, for the business process data obtained from the platform inner joint module 201, press
According to predetermined filtering rule, the business process data unit containing sensitive information is therefrom filtered out, there is provided separated to sensitive data
Module 302；The filtered business process data unit without sensitive information, then as general service data, there is provided to going individual
Change data and comb module 304.As it was noted above, business process data is original after big data mining analysis algorithm process
The data cell formed after associated record table, cluster set record sheet and tag along sort is added on business datum.Will be pre-
Fixed information type is set as filter condition, if certain type of information is directly connected to the identity confidentiality of user, account peace
Complete and privacy, then using the information type as filter condition in filtering rule, such as by Bank Account Number, stored value account number, identity
The information type such as card number, medical insurance card number, phone, address is set as filter condition.Sensitive data extraction module 301 is processed to business
Data are filtered according to filtering rule, namely judge whether include filter condition among any one business process data unit
Specified in information type, if the business process data unit filtered out comprising if, there is provided to the sensitive data point
From module 302.
Sensitive data separation module 302, for by sensitive data extraction module 301 filter out containing sensitive information
Business process data unit, is separated into sensitive data part and general service data division, and establishes, storage and maintenance
Sensitive data part and the mapping table of general service data division, are registered by same business process data list in the table
Corresponding relation between sensitive data part that member is separated and general service data division.For example, for online-banking
In business system by account open an account information generation business process data unit, wherein some savings users containing a certain cluster are each
From Bank Account Number, identification card number, telephone number, address, Stored Value remaining sum, draw record, then by the Bank Account Number of each user,
Identification card number, telephone number, address are separated into sensitive data part, and by Stored Value remaining sum, draw record and be classified as general service number
According to part, and registered in mapping table between the sensitive data part of each user and general service data division
Corresponding relation.A sequence number can be set up respectively for sensitive data part and general service data division, and closed in mapping
It is the corresponding relation of the two sequence number of registration among table.After mapping registration has been carried out, general service data division is as one
The individual business process data unit gone after sensitive information, there is provided comb module 304 to de-personalization data；Sensitive data part
It is provided to sensitive data MMU memory management unit 303 and carries out storage and management.
As shown in figure 4, sensitive data MMU memory management unit 303 includes sensitive data warehouse 303A, level of security judges mould
Block 303B and life cycle setting module 303C.Sensitive data warehouse 303A is received and deposited from sensitive data separation module 302
The sensitive data part is stored up, so as to which sensitive data part is all the time in the data security layer 3 of each network service operation system
Portion is stored and managed.Level of security determination module 303B is according to the info class of sensitive information in the sensitive data part
Type, judge the level of security of each sensitive data part；For example, if Bank Account Number or identity card are included in sensitive data part
Number, then the level of security of the sensitive data part is set as highest level；If sensitive data part includes telephone number,
Its level of security is set as medium rank；If sensitive data part, can be by its safe level containing having plenty of address information
Lowest level is not set as it；Level of security determination module 303B is corresponding sensitive data portion according to the level of security judged
Divide addition level of security label.And then life cycle setting module 303C is judged according to level of security determination module 303B
Level of security, it is each sensitive data section sets Life Cycle according to the corresponding relation of predetermined level of security and life cycle
Phase；Wherein, for the sensitive data part of highest level, the life cycle duration set for it is most short, and rank is lower, then life
The duration in cycle is longer.Life cycle represents that in cross-platform exchange shared procedure is subsequently carried out sensitive data is available for this every time
Other platforms beyond platform obtain or the time of application, and beyond the duration of the life cycle, then the sensitive data is no longer able to
Obtained or applied by other platforms beyond this platform.Life cycle setting module 303C is that Life Cycle is added in sensitive data part
Phase label, the life cycle duration of this sensitive data part is have recorded in the label.
On the one hand, sensitive data part is stored and managed by sensitive data MMU memory management unit 303；On the other hand, it is right
The general service data of sensitive information are free of in itself or the general of sensitive information has been separated by sensitive data separation module 302
Logical business datum part, it is delivered to de-personalization data combing module 304 and performs further de-personalization data combing.It is described
De-personalization data, which comb, to be referred to embodying the original of userspersonal information among general service data with substitute symbol to substitute
Beginning data.For example, it is possible in general service data comprising the personal name of the user, age, the date of birth, medical card number, excellent
Although favour card number etc., these information are not belonging to the information type of foregoing sensitive information, but also embody some of user
Effective information, it is inappropriate to be supplied directly to other platforms.Therefore, module 304 is combed for common by de-personalization data
Personal information in business datum, which performs, substitutes operation.Specifically, retrieved first against general service data, search it
In userspersonal information；And then for all or part initial data of the userspersonal information, replaced with predetermined
Substituted for symbol；For example, for address name Zhang Peng, can be replaced with substitute symbol " ZP ".
Data security layer 3 also has data sharing interface 305.For general service data, combed by de-personalization data
After reason, the big data inclusion layer 4 is uploaded to by data sharing interface 305, unified by the big data inclusion layer 4
Storage, and unified access approach is provided.
Big data inclusion layer 4 is a cross-platform level in the present invention, towards Web bank, shopping at network, medical treatment letter
The various network services such as breath system, a unified big data storage is provided for the big data business of these network service platforms
And shared system.For being handled and being exported by data sharing interface 305 general in data security layer 3 by various network services
Logical business datum, it is placed on the big data inclusion layer 4 and carries out unified storage and management, using unified reading and writing data standard and behaviour
Make specification；The big data platform of each network service is set to utilize the big data inclusion layer 4, obtain cross-platformly required for
The related general service data of various network services, and these data are deployed with the analysis and application of big data rank.
Fig. 5 is the modular structure schematic diagram of big data inclusion layer.Cross-platform unified big data inclusion layer 4 has in the present invention
Body includes：Shared thesaurus 401 and standardization shared interface 402.Shared thesaurus 401 is used for from bank, shopping at network, medical treatment
The data sharing interface 305 of each network service platform such as health receives general service data, and is uniformly stored.It is shared to deposit
Bank 401 also allows the big data mining analysis platform of each network service to be obtained by standardizing shared interface 402 in the storehouse
The general service data of each platform of unified storage, by big number of these general service data applications in each network service in itself
According to business, so as to bring cross-platform big data to carry out the facility of comprehensive analysis.Shared interface 402 is standardized using unification
Reading and writing data accesses standard and working specification, is capable of simple and stable so as to the big data mining analysis platform of each network service
Obtain the support of general service data.
For example, the big data mining analysis platform of bank can be transferred by shopping online platform from shared thesaurus 401
Comprehensive client's wholesale consumer record carries out client's consuming capacity analyze data that big data excavation is generated, and by medical treatment ＆ health
The user health status analysis data that service system integrates client's physical examination data over the years and obtained, are deposited with reference to customer banking account
Withdrawl deposit record carries out client's totality balance between revenue and expenditure situation that classification analysis is obtained, and assesses the medium-term and longterm credit ability of client.
For some network service, the sensitive data that it is generated in running is all the time in the quick of the platform data safe floor 3
Sense data storage management unit 303 is stored.In some cases, exchanged as the cross-platform sharing that the present invention subsequently introduces
Demand, other network services are in order to realize big data function and application, it is possible to need to call the quick of present networks service in short-term
Feel data.For example, bank by above-mentioned big data analysis determine client credit capacity can undertake without mortgage individual disappear
After taking loan, in addition to depositor's advertisement, it is also possible to need to push this without mortgage to customers
The advertisement that personal consumption is borrowed, at this time just need to know the accounts information and telephone number of user from shopping at network platform, this is just
It is related to the acquisition and application cross-platform to shopping at network platform sensitive information.In order to adapt to this demand, present invention additionally comprises
Across the restricted Fabric Interface 5 and interim shared memory 6 of data security layer 3 and big data inclusion layer 4.Limitation sexual intercourse above
Alias 5 and interim shared memory 6 are based on setting of the data security layer 3 to sensitive data life cycle, it is allowed in short-term
Property cross-platform sensitive data exchange it is shared.
Specifically, can be with when the big data business of bank's platform needs to call the sensitive data of shopping at network platform
The call request in short-term of sensitive data is sent to the sensitive data MMU memory management unit 303 of shopping at network platform data safe floor.
Sensitive data MMU memory management unit 303 responds the call request in short-term, and one of following two modes can be taken to carry out sensitive number
According to exchange share.
A kind of mode is the sensitive data MMU memory management unit 303 by shopping at network platform data safe floor 3 through restricted
Fabric Interface 5 provides requested sensitive data to bank's platform；The sensitive data is by encryption, and provides and has
The decruption key of duration limitation is imitated, the effective time is no more than the life cycle of the sensitive data；Banking system can utilize
Decruption key obtains sensitive data by decrypting；After more than the effective time, because password fails, sensitive data can not
It is decrypted and applies again.
Another way is the sensitive data MMU memory management unit 303 by shopping at network platform data safe floor 3 by sensitivity
Data are uploaded to the interim shared memory 6 positioned at big data inclusion layer 4, and banking system can be with normalized shared interface 402
Sensitive data is obtained from the interim shared memory 6；Interim shared memory 6 is based on the life cycle of sensitive data to the sensitivity
The shared duration of data is controlled, once shared duration reaches the upper limit of its life cycle, then interim shared memory 6 is eventually
Only the sensitive data is shared, and banking system is no longer able to continue to obtain sensitive data from the interim shared memory 6；And then
Interim shared memory 6 can also delete the sensitive data for reaching the life cycle upper limit.
It can be seen that the present invention separates a small amount of sensitive information among the business datum of magnanimity, taken by each network
The big data platform separate storage of business and management, and the exchange strictly limited between platforms is shared.Wherein, establish
Exchange shared mechanism based on life cycle, ensure that acquisition and utilization of the outside platform to this platform sensitive information are all interim
, can not be repeatedly.And for general service data, then put down using unified store of the present invention with shared mechanism, each big data
Platform can be obtained and analyzed, and breached the bottleneck that interface data exchanges in the prior art, can be realized magnanimity rank
Cross-platform big data application.
Above example is merely to illustrate the present invention, and not limitation of the present invention, the common skill about technical field
Art personnel, without departing from the spirit and scope of the present invention, it can also make a variety of changes and modification, thus it is all etc.
Same technical scheme falls within scope of the invention, and scope of patent protection of the invention should be defined by the claims.
Claims (10)
- A kind of 1. safe shared system of big data platform sensitive data, it is characterised in that including：Data collection layer, data processing Layer, data security layer, big data inclusion layer；Wherein described data collection layer, data analysis layer, data security layer are deployed in various Among the operation system of network service platform itself；The big data inclusion layer is disposed across multiple network service platforms, is each net The big data business of network service platform provides a unified big data storage inclusion layer；The data collection layer is used to gather caused whole original service data in network service platform running, adds former Beginning of the school year business data warehouse；The data analysis layer is the big data mining analysis platform of network service platform itself, for high parallel calculating mould Formula, towards the data flow formed by original service data, data mining and parser are performed, data preparatory processing is passed through in generation Business process data afterwards；Inside the operation system of the data security layer embedded network service platform, among the business process data of magnanimity A small amount of sensitive information is separated, generates the sensitive data containing sensitive information and the general service data without sensitive information respectively； Register mutual corresponding mapping relations of the sensitive data with general service data；To sensitive data in data security layer internal independence Storage and management, wherein setting life cycle for sensitive data, and between network service platform strictly limited The exchange of system is shared, including the interim shared storage based on life cycle duration and conditional special interface exchange；For general Logical business datum is transferred to big data inclusion layer by data security layer, for cross-platform shared exchange；The big data inclusion layer is used for the general service number that will be obtained from the data security layer of the multiple network service platform Stored according to unified, and unified reading and writing data is provided and accesses standard and working specification, so that each network service platform is from institute Big data inclusion layer is stated to obtain the general service data of each network service platform and be applied to big data mining analysis.
- 2. the safe shared system of big data platform sensitive data according to claim 1, it is characterised in that the big data The safe shared system of platform sensitive data further comprises：Restricted Fabric Interface, for response external network service platform to The call request in short-term that the data security layer of present networks service platform is sent, provided to the external web services platform requested Sensitive data；Also, according to the life cycle of sensitive data, the sensitive data that limitation is provided to external network platform can Decryption and the duration of application.
- 3. the safe shared system of big data platform sensitive data according to claim 1, it is characterised in that the big data The safe shared system of platform sensitive data further comprises：Positioned at the interim shared memory of big data inclusion layer, response external The call request in short-term that the data security layer of network service platform to present networks service platform is sent, will be asked by data security layer The sensitive data asked is uploaded to the interim shared memory, and external web services platform can obtain quick from the interim shared memory Feel data；Also, the interim shared memory based on the life cycle of sensitive data to the sensitive data it is shared when progress Row control.
- 4. the safe shared system of big data platform sensitive data according to claim 1, it is characterised in that the data are adopted Collection layer includes：Data adaptation interface, for adapting to data type, form and the export rule of the operation system of network service platform in itself Then, the output channel as original service data caused by the operation system；Data acquisition module, for real-time or non real-time from the data adaptation interface original service data；Data inquiry module, for actively sending inquiry message to the data adaptation interface, and receive the data adaptation and connect The original service data that mouth transmits in response to inquiry message；Data Verification module, for the original service data for being gathered, tested according to predefined data verification rule Card, empirical tests are rejected or reacquired imperfect or are not inconsistent original service data normally, complete for empirical tests and accord with The original service data normally required are supplied to data processing module；Data conversion treatment module, for verifying that qualified original service data carry out ETL and (extract, turn by Data Verification module Change, load) processing, original service data are converted into standard data format；Original service data warehouse, for storing the original of the standard data format after data conversion treatment module is changed Business datum.
- 5. the safe shared system of big data platform sensitive data according to claim 1, it is characterised in that at the data The big data mining analysis platform of reason layer includes：Platform inner joint module, the upper strata for receiving network service platform operation system are dispatched, and transmit data mining point downwards Analysis task；And externally export the business process data after being handled through lower floor；Parallel stream task module, for receiving data mining analysis task, and according to the task, open up at least one task Stream, generation, maintenance, kill to each task flow are managed；For each task flow from original service data warehouse with data The form extraction original service data of stream, there is provided to the different submodules of the data relation analysis module, realize parallel appoint Business is handled；Data relation analysis module, including data correlation calculating sub module, data classified calculating submodule and data cluster calculation Submodule；Above submodule undertakes the task flow to match with itself algorithm types respectively, and it is original accordingly to receive task flow The data flow of business datum, computing is carried out to the data flow using itself algorithm, business process data is obtained, generates and to platform The data flow of inner joint module outgoing traffic process data.
- 6. the safe shared system of big data platform sensitive data according to claim 1, it is characterised in that the data peace Holostrome includes：Sensitive data extraction module, for business process data, according to predetermined filtering rule, therefrom filter out and believe containing sensitivity The business process data unit of breath, there is provided give sensitive data separation module；The filtered business process data without sensitive information Unit, then it is supplied to the big data inclusion layer as general service data；Sensitive data separation module, number is processed for the business containing sensitive information filtered out by sensitive data extraction module According to unit, sensitive data part and general service data division are separated into, and is established, storage and maintenance sensitive data portion Divide the mapping table with general service data division, register separated by same business process data unit in the table Sensitive data part and general service data division between corresponding relation；After mapping registration, by general service data portion Divide and be supplied to big data inclusion layer, sensitive data part is provided to sensitive data MMU memory management unit and carries out storage and management；Sensitive data MMU memory management unit, received from sensitive data separation module and store the sensitive data part, and according to The level of security of sensitive data part sets the life cycle for it；Data sharing interface, general service data are uploaded to the big data inclusion layer.
- 7. the safe shared system of big data platform sensitive data according to claim 6, it is characterised in that the data number Also include according to safe floor：De-personalization data comb module, for itself without sensitive information general service data or The general service data division of sensitive information has been separated by sensitive data separation module, has performed the data comb of de-personalization information Reason.
- 8. the safe shared system of big data platform sensitive data according to claim 1, it is characterised in that the big data Inclusion layer specifically includes：Shared thesaurus, for receiving general service data, and unified progress from the data security layer of each network service platform Storage；Shared interface is standardized, for accessing standard and working specification using unified reading and writing data, is put down for each network service Platform obtains the general service data of each platform of unified storage among the shared thesaurus by this interface.
- 9. a kind of big data platform sensitive data secure sharing method based on life cycle, it is characterised in that including following step Suddenly：Original service data caused by various network service platforms are gathered, original service data are performed with big data Treatment Analysis fortune Calculate, generate business process data；The sensitive information data comprising sensitive information are carried out to business process data with the general service not comprising sensitive information to add Separation of the number between；For the sensitive data part separated by same business process data unit and general service Data division, establish the mapping table of sensitive information data division and general service process data part；For sensitive information Data division, level of security delimited, life cycle is set according to level of security；The sensitive information data separated are stored in each network service platform internal independence；Using special secure exchange shared mechanism, based on the life cycle, sensitive information data are put down in each big data Carry out severely limited exchange between platform to share, including interim shared storage and conditional special interface exchange；General service data are delivered to the data sharing layer across multiple network service platforms, carry out cross-platform unified storage, Shared and application.
- 10. big data platform sensitive data secure sharing method according to claim 9, it is characterised in that according to sensitivity The life cycle of data, limitation by special interface exchange be provided to external network platform sensitive data can decrypt with The duration of application；Or the life cycle according to sensitive data, limit the interim shared shared duration stored.
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