CN107040510A - A kind of medical big data processing method based on body area network and cloud computing - Google Patents

A kind of medical big data processing method based on body area network and cloud computing Download PDF

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CN107040510A
CN107040510A CN201611078849.6A CN201611078849A CN107040510A CN 107040510 A CN107040510 A CN 107040510A CN 201611078849 A CN201611078849 A CN 201611078849A CN 107040510 A CN107040510 A CN 107040510A
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data
biosensor
mobile device
key
storage
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CN107040510B (en
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陈永红
朱博文
王忠文
孔新玲
刘怀进
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Huaqiao University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
    • H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
    • H04L63/045—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload wherein the sending and receiving network entities apply hybrid encryption, i.e. combination of symmetric and asymmetric encryption
    • G—PHYSICS
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    • G06F21/60—Protecting data
    • G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
    • G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
    • G06F21/6245—Protecting personal data, e.g. for financial or medical purposes
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    • G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
    • G06F3/0601—Interfaces specially adapted for storage systems
    • G06F3/0602—Interfaces specially adapted for storage systems specifically adapted to achieve a particular effect
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    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
    • G06F3/0601—Interfaces specially adapted for storage systems
    • G06F3/0668—Interfaces specially adapted for storage systems adopting a particular infrastructure
    • G06F3/067—Distributed or networked storage systems, e.g. storage area networks [SAN], network attached storage [NAS]
    • G—PHYSICS
    • G08—SIGNALLING
    • G08C—TRANSMISSION SYSTEMS FOR MEASURED VALUES, CONTROL OR SIMILAR SIGNALS
    • G08C17/00—Arrangements for transmitting signals characterised by the use of a wireless electrical link
    • G08C17/02—Arrangements for transmitting signals characterised by the use of a wireless electrical link using a radio link
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
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    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/12—Applying verification of the received information
    • H04L63/123—Applying verification of the received information received data contents, e.g. message integrity
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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    • 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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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2221/00—Indexing scheme relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F2221/21—Indexing scheme relating to G06F21/00 and subgroups addressing additional information or applications relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
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Abstract

The invention discloses a kind of medical big data processing method based on body area network and cloud computing, including：Biosensor perceives user's physiological data, using APTEEN agreements, generates symmetric key by Diffie Hellman IKEs and data are encrypted, data are signed using Merkle trees, the data after processing are transferred into mobile device；The data that mobile device is uploaded to biosensor are decrypted and the integrality of user identity and transmission data are verified, the user data after checking is sent into Cloud Server carries out data storage and data analysis；Cloud Server is stored the result of data analysis, and is sent to mobile device.The present invention carries out the storage and protection of medical data by transmitting encryption technology, efficiently solves the problem of patients' privacy is revealed；By wireless network by real-time data transmission to Cloud Server, solve the de-redundancy storage of real-time transmission data using big data technology and Real-time Decision is analyzed, realize the safeguard protection and analysis in real time of medical big data.

Description

A kind of medical big data processing method based on body area network and cloud computing
Technical field
The present invention relates to wireless human body local area network, Cloud Server and big data field, including human body physiological data is adopted Collection, protection, storage and analysis are specifically, and in particular to gather medical big data based on wireless human body local area network and protected in storage The method analyzed in real time on the basis of shield data safety and privacy.
Background technology
Wireless human body local area network be it is a kind of can monitored for prolonged periods of time and record human body health signal basic fundamental, in early days application master If for the continuous health parameters for monitoring and recording chronic disease (such as diabetes, asthma and heart disease) patient, there is provided certain The automatic therapy control of the mode of kind.The application of these early stages is all the equipment of some unification, the illness or peace that can be directed to Full decision-making is limited.And traditional wireless sensor network security is not too much adapted to calculating and storage energy with secret protection technology The more limited wireless human body local area network of power, to the solution of the security and privacy sex chromosome mosaicism of the wireless human body local area network in the system Certainly become very challenging property and very necessary.Need to integrate the advantage of cloud computing and big data technology simultaneously.In recent years, with micro- The development of electronic technology, the health care settings for serving people that are wearable, implantable, can invading have occurred, wireless human body LAN is more and more extensive along with the application of Intelligent worn device etc., and the data volume of generation is huge, and growth rate It is surprising, therefore the storage transmission of these medical datas and security privacy protection become a greatly challenge, are also common big Many hot issues of concern.Past medical data secret protection is very weak, in addition have there is no secret protection measure, Information-based today, people increasingly pay attention to the protection of privacy, and the secret protection technology of fusion biological information safety can be brought Safer strategy.
With the development of cloud, cloud storage technology is widely used.And at present not with medical treatment combine compared with Good cloud storage system.General cloud storage conceptual design is on condition that support the stream data operation of Large Copacity, the number being related to According to measure it is larger when, if data block set it is smaller, it is necessary to read data block will become many, due to data block on hard disk it is discontinuous Storage, magnetic head traveling time and tracking time will increase therewith, so file block size typically sets larger (be typically no less than 64M).If also, the too small length that can cause catalogue of single file block increases, and adds the memory cost of catalogue.And in medical treatment In, wall scroll data record is typically all less KB scales, the problem of this just brings storage efficiency.
With the quickening of information data paces, from ecommerce of the industrial manufacturing into life；From enterprise Information management system to government department E-Government；From the media information on social networks to Online Video image document, All produced daily along with substantial amounts of data；Big data (Big data) is the another of the IT industry after cloud computing and Internet of Things Secondary subversiveness technological change, is that the inevitable outcome cloud storage of economic and technical development and cloud computing are handled as large-scale data Emerging product, and currently a popular Hadoop be exactly it is a kind of realize cloud storage and the method for cloud computing, Hadoop has highly reliable The advantages of property, high scalability, high efficiency and high fault tolerance, used by industry.
The content of the invention
It is an object of the invention to overcome the deficiencies in the prior art, a kind of medical big data based on body area network and cloud computing The data acquisition of various health sensors, can be transferred to high in the clouds, based on high in the clouds by processing method by wireless human body local area network Storage access and computing capability, solve the problem of single sensing equipment analysis efficiency is low；Carried out by transmitting encryption technology The storage and secret protection of medical data, the problems such as efficiently solving conventional patient's privacy leakage；In wireless human body local area network, All kinds of wearable devices and the sensor storage of implantation equipment and disposal ability are limited, and high in the clouds is real-time transmitted to by wireless network, The de-redundancy storage and Real-time Decision analysis of the data of these real real-time Transmissions are solved using big data technology so that modern medical service It is more intelligent, by the various health parameters gathered in analyte sensors, the detection discrimination of disease is improved, so as to carry out early stage Prevention, reduce medical treatment cost.
The technical solution adopted for the present invention to solve the technical problems is：
A kind of medical big data processing method based on body area network and cloud computing, including：
It is attached to the embedded or portable type biosensor with user and perceives user's physiological data；
The biosensor utilizes APTEEN agreements, is generated by Diffie-Hellman IKEs symmetrical close The physiological data is encrypted key, and physiological data is signed using Merkle trees, and by the life after encryption and signature Reason data are transferred to mobile device in a multi-hop fashion by Qos (Quality of Service, service quality) Routing Protocol； The mobile device user's physiological data that biosensor is uploaded is decrypted using symmetric cryptographic algorithm and to user's body The integrality of part and transmission data is verified；
User's physiological data after checking is sent to Cloud Server by Internet and carries out data by the mobile device Storage and data analysis；
The Cloud Server is stored the result of data analysis, and is sent to the mobile device.
The biosensor utilizes APTEEN agreements, is generated by Diffie-Hellman IKEs symmetrical close Physiological data is encrypted key, and physiological data is signed using Merkle trees, and by the physiology number after encryption and signature Mobile device is transferred to according to by Qos Routing Protocols；The mobile device is uploaded using symmetric cryptographic algorithm to biosensor User's physiological data be decrypted and to user identity and transmission data integrality verify, specifically include：
Step a, initialization：The mobile device generates a pair of public private keys to { Mpublic,Mprivate, according to reality Situation sets the relevant parameter set P={ H of each biosensorT,ST,CT, when biosensor asks to register, movement is set Standby broadcast public key Mpublic, cryptographic HashAnd it is each related to relevant parameter concentration by No. ID of biosensor Parameter private key MprivateBiosensor is sent to after encryption；Wherein, relevant parameter HTRepresent hard threshold values, STSoft threshold values is represented, CTRepresent the double time interval for being successfully transmitted data to mobile devices of sensor；
Step b, key generation：Mobile device and biosensor utilize Diffie-Hellman key exchange methods, raw Into the key of encrypted transmission data；
Step c, data transfer：Perception data is encrypted using the key of generation for biosensor, and is utilized Merkle Tree calculate the cryptographic Hash of encryption key and perception data, by the perception data after biosensor ID, encryption and The cryptographic Hash of encryption key and perception data is sent to mobile device, and mobile device receives the cryptographic Hash by public key after data Preliminary authentication is carried out to biosensor, data are decrypted by rear for certification, after successful decryption computation key and The cryptographic Hash of data, by comparing the whether equal integrality to verify data of cryptographic Hash；
Step d, the detection of compromise biosensor：Mobile device is transmitted after being encrypted with private key to broadcast message, physiology If sensor can be with public key decryptions success, by biosensor ID, using Merkle Tree to encryption key and public key The cryptographic Hash that progress computing is obtained is sent to mobile device, and mobile device is recognized biosensor by comparing the cryptographic Hash Card, if the calculating of the value and mobile device obtain it is inconsistent, rejected manually；
Step e, parameter updates：Data to collection are analyzed and processed, when relevant parameter needs to reset, and are repeated Step a~d.
The step b is specifically included：
Biosensor siChoose the largest prime q less than hard threshold valuesiWith the plain root a of its onei, if there is no then selecting Take the least prime q more than hard threshold valuesiWith the plain root a of its onei, mobile device is according to same method selection prime number qiAnd it Plain root ai；Wherein, i=1,2,3 ..., represent the numbering of biosensor；
Biosensor siSelect a random number ri, then calculateBy YiWith the cryptographic Hash of public key and No. ID of biosensor is sent to mobile device, then carries out Hash operation using conventional MD5 algorithms, and mobile device passes through CompareCome to biosensor siIt is authenticated；
Mobile device selects a random number Ri, calculateMobile device is by Yi' and with private key by physiology Sensor siNo. ID encryption after broadcast, ID is decrypted with public key after biosensor received data packet, if successful decryption explanation Data source is credible, will extract No. ID and is compared with oneself No. ID, by the data packet discarding if differing, is protected if identical Deposit Y 'i；
Mobile device C and biosensor siEncryption key is calculated respectively：
C：
si：
The step c is specifically included：
According to APTEEN agreements, as biosensor siData d >=the H perceived firstTWhen, biosensor siD is sent out Mobile device is given, and d is stored in built-in variable SV, afterwards as biosensor siData d >=H of perceptionTAnd | d-SV | >= STOr current time and interval of delta t >=C of the time of last time transmissionTWhen, biosensor sends perception data；
Biosensor siHash operation is carried out to perception data, using MERKLE trees, passed throughIt is right The cryptographic Hash of perception data and the cryptographic Hash of public key carry out Hash operation；By the perception data after No. ID, Hash operation andIt is sent to mobile device；
Movement is received after data, and corresponding key K is found according to IDiIf using KiEnergy successful decryption is then to the number after decryption According to progressComputing, operation result is compared with the value in packet, if equal, further Authentication data is from the horse's mouth, and data are not tampered with, if KiSuccessful decryption is unable to, the packet is abandoned.
The data storage, which is used, includes the safety of load balancing layer, level cache layer, L2 cache layer and cloud storage layer Storage system is realized；The load balancing layer is tied by Linux virtual server (LVS, Linux Virtual Server) Close OSPF (Open Shortest Path First ospfs) protocol realization；Its one-level cache layer passes through Web server (Web Server) realization, the data high for storing access frequency；Secondly level cache layer passes through medical server (Medical Server) is realized, as the supplement of level cache, is additionally operable to cache part Analysis of Medical Treatment Data result；Its cloud is deposited Reservoir passes through distributed file system (MFS) realization, the initial data and analysis result data all for storing；Data access Shi Youxian solves load balancing by load balancing layer, then accesses level cache layer, if not finding access in level cache layer Data, then access L2 cache layer, if not finding access data in L2 cache layer, accesses cloud storage layer and reads data.
The distributed file system includes proxy module, catalogue module, memory module, monitoring modular, HBase databases Module and ZooKeeper Coordination modules；The module externally provides api interface, shields the structure and details of storage inside, internally Lift access request of data to memory module, and to catalogue module application directory and dispatch service；The catalogue module is used In obtaining User Defined data to the application of HBase database modules, and Receiving Agent module and ZooKeeper Coordination modules Request；The HBase database modules, which are used to store, includes the User Defined metadata of filename, type and directory tree；Institute Stating ZooKeeper Coordination modules is used to propose metadata change request to catalogue module；The monitoring modular is fixed to memory module Phase initiates to check and the state of the memory module checked is sent into ZooKeeper Coordination modules；The memory module is to deposit Core is stored up, for registering data storage to ZooKeeper Coordination modules.
Superblock is devised in the memory module；When memory module is arrived in file storage, by each small documents and file Corresponding unique ID constitutes a piece；Again by piece be sequentially written in or suffix by way of constitute a superblock and stored； Then superblock offset table is set up in a storage module, and corresponding file ID value is recorded.
The data analysis is realized using the streaming big data processing method based on Hadoop MapReduce, specific to wrap Include：
Receive by network transmission come data and the data that are stored in safe storage system, then data are pressed Burst is carried out according to size；
Data after burst carry out the matching of data arrival rate by tactful distribution mechanism, are then passed to Map tasks Node carries out initial analysis；
The processing speed of each Map task node is different, by the data of network transmission to system according to arrival rate The reallocation of task is carried out with the completion speed of Map task nodes, and the intermediate data of generation is cached to each Map tasks section That puts is local, and data are read for Reduce task nodes；
Each Reduce task nodes read the intermediate data that Map task nodes are produced, and are further processed, each The processing speed of individual Reduce task nodes is also different, and it is defeated that fireballing Reduce task nodes continue reading Map task nodes The intermediate buffer data gone out；
Each Reduce task nodes collect the data handled well according to period progress defeated while processing data Go out final result.
A kind of medical big data processing method based on body area network and cloud computing of the present invention, it can protect the complete of data Whole property, privacy, confirmability is detected by node of compromising, and node energy can be saved again, extends the life of wireless human body local area network The life cycle.Biosensor, which is attached to user, gathers user data, user's mobile device such as PDA (Personal Digital Assistan, palm PC) collect the physiological data of all biosensors and be encrypted and sign, Yong Humin Data encryption and signature are felt using simple symmetric cryptography and signature technology is calculated, specifically by using with active sensor The APTEEN agreements of network and passive sensor network feature, Diffie-Hellman key exchange methods and are able to verify that data are complete The Merkle Tree of whole property are realized.
A kind of medical big data processing method based on body area network and cloud computing of the present invention, in data mainly by wireless The sensor of various Intelligent worn devices and implanted equipment in human body local area network, the various health parameters of collection (including blood PH value, body temperature, blood pressure, glucose, breathing etc.), and in server by wireless network transmissions to high in the clouds.Because sensor The data volume of transmission is smaller and there is continuity, the problems such as redundancy, therefore in data storage section, using for small text The cloud storage system of part, is stored to the Various types of data that sensor is gathered.In big data of the data analysis component using optimization Analytical framework carries out cleaning pretreatment to the data of storage beyond the clouds, simplifies data, removes noise, then according to transmission data Arrival rate, realizes a kind of real-time analytical model of streaming.
A kind of medical big data processing method based on body area network and cloud computing of the present invention, proposes the doctor based on cloud storage Small documents system is treated, ensures data access efficiency by designing multilayered structure, number is ensured using superblock scheme According to storage efficiency.
A kind of medical big data processing method based on body area network and cloud computing of the present invention is based on Hadoop there is provided one kind MapReduce streaming big data analysis solution, goes out to the medical data progress analysis mining of storage beyond the clouds and wherein accumulates The information contained, the real time problems of data processing are solved by streaming scheme.
A kind of medical big data processing method based on body area network and cloud computing of the present invention, its hardware realizes platform bag Include：Data collecting system (including various biosensors and mobile device) based on wireless human body local area network, distributed document Storage system (HDFS) realizes the storage of medical big data, and prediction and parser are realized in MapReduce Computational frames, are led to Cross Zookeeper and carry out the configuration of platform and the co-ordination of system process.
The present invention has the advantages that：
(1) the inventive method utilizes APTEEN agreements, and active Sensor Network and passive type Sensor Network are combined, used Family can understand the health of oneself on the whole, can handle emergency case in time again, greatly play human body local area network Function.Key is generated using the shared information of communicating pair, the transmission of data is reduced, it is ensured that the safety of encryption key distribution Property, preferably protect the privacy of data.Realized with reference to digital signature and Merkle Tree certification to node identities and The certification of data integrity, adds the difficulty of attacker's altered data and camouflage validated user, improves authentication efficiency；Utilize Big data is analyzed, and obtained parameter is more accurate, reliably；
(2) safe storage system of the inventive method has four layers of overall architecture, and the bottom is distributed cloud storage layer； First layer is load balancing layer, and the second layer is level cache layer, third layer L2 cache layer, and the 4th layer is data storage layer；Visit Load balancing is solved by first layer when asking, if it is miss to have access to the second layer, third layer is accessed, not ordered yet if third layer is accessed In, then access the 4th layer of MFS and read metadata；By multilayer access mechanism, access efficiency is improved, and visit using multilayer Ask that there is preferable security to external shield built-in system structure；Distributed file system in the 4th layer passes through superblock The problem of scheme solves small documents access efficiency；
(3) Frame Design for handling real-time streams big data is the real-time intensive stream data of processing by the inventive method； There is limitation structurally and functionally in Hadoop MapReduce frameworks, the data for handling static state, the inventive method is used Adaptive MapReduce frameworks, are realized on the basis of traditional Hadoop MapReduce frameworks to needing real-time place The stream data of reason is analyzed and processed.
Brief description of the drawings
The system diagram of Fig. 1 embodiment of the present invention；
Fig. 2 is the Merkle Tree schematic diagrams of the embodiment of the present invention；
Fig. 3 is the flow chart of data interaction between initial phase mobile device of the present invention and biosensor；
Fig. 4 is the flow chart of data interaction between data transfer phase biosensor of the present invention and mobile device；
Fig. 5 is the flow chart of data transfer phase biosensor encryption information of the present invention；
Fig. 6 is the mobile device of the present invention to reception data deciphering and the flow chart of integrity verification；
The mobile device that Fig. 7 is the present invention judges the flow chart whether biosensor is compromised；
Fig. 8 is four layers of integrated stand composition of the safe storage system of the present invention；
Fig. 9 is the block diagram of six big modules of the distributed file system of the present invention；
Figure 10 is the schematic diagram of the super block structure of the present invention；
Figure 11 is the data flowchart of the streaming big data analysis solution of the invention based on Hadoop MapReduce.
Embodiment
Below with reference to drawings and Examples, the present invention is described in further detail.
Referring to shown in Fig. 1 and Fig. 2, a kind of medical big data processing method based on body area network and cloud computing of the present embodiment Apply in the system being made up of three parts, the system includes：Wireless human body local area network 1, cloud storage service device 2 and cloud meter Calculate server 3.By the gathered data of wireless human body local area network 1, store into cloud storage service device 2, and be transferred to cloud computing Server 3 carries out data analysis, and the result after analysis is saved in cloud storage service device 2, and feeds back to wireless human body local area network 1 User；Data in whole system are all two-way flows.
Specifically, wherein wireless human body local area network 1 includes various biosensors and mobile device (such as cell phone, PDA Deng).Each biosensor, which is attached to, is used for the health for monitoring user on the body of user, biosensor utilizes APTEEN Agreement, generates symmetric key by Diffie-Hellman IKEs and physiological data is encrypted, utilize Merkle Tree is signed to physiological data, and the physiological data after encryption and signature is passed through into Qos (Quality of Service, clothes Business quality) Routing Protocol is transferred to PDA in a multi-hop fashion；PDA utilizes the use that symmetric cryptographic algorithm is uploaded to biosensor Family physiological data is decrypted and the integrality of user identity and transmission data is verified；PDA is by the physiology number after checking Cloud server terminal is transferred to according to by Internet.
Further, referring to shown in Fig. 2 to Fig. 7, mainly by using with active sensor network and passive sensor The APTEEN agreements of network characterization, Diffie-Hellman key exchange methods and the Merkle for being able to verify that data integrity Tree realizes that user's physiological data is encrypted and signature verification, comprises the following steps：
Step a, initialization：As shown in figure 3, mobile device generates a pair of public and private key to { Mpublic,Mprivate, according to Actual conditions set the relevant parameter set P={ H of each biosensorT,ST,CT, when biosensor asks to register, move Dynamic device broadcasts public key MpublicAnd its cryptographic HashIt will be sent after No. ID of node and relevant parameter private key encryption To associated physiological sensors.
Step b, key generation：Mobile device and node selection are less than the largest prime q and q of hard threshold values a plain root a, If largest prime and its an element root of the selection more than hard threshold values in the absence of if.Utilize Diffie-Hellman keys exchange side Method, generates the key of encrypted transmission data.
Step c, data transfer：As shown in figure 4, perception data is encrypted using the key of generation for biosensor, And according to the Merkle Tree cryptographic Hash (as shown in Figure 2) for calculating encryption key and perception data, the data after encryption are breathed out Uncommon value, the cryptographic Hash of public key is sent to mobile device, and mobile device is received after data by the cryptographic Hash of public key to physiology biography Sensor carries out preliminary authentication, and data are decrypted by rear for certification, if energy successful decryption illustrates biosensor It is credible, the cryptographic Hash of computation key and data after successful decryption, by comparing, whether cryptographic Hash is equal to be verified the complete of data Property, specific mobile device decryption inspection data integrity procedure is as shown in Figure 6.
Step d, compromise nodal test：Mobile device is sent after being encrypted with private key to broadcast message, if biosensor Public key decryptions success can be used, description messages come from trusted party, and biosensor sends No. ID, adds using Merkle Tree couple The value that key and public key progress computing are obtained is to mobile device, and mobile device is entered by comparing the cryptographic Hash to biosensor Row certification, if the value and mobile device calculate obtain inconsistent, illustrates that the biosensor is compromised, manually by it Reject.
Step e, parameter updates：Comprehensive analysis processing is carried out to the data of collection using big data analysis method, specifically, Data are analyzed using using supervised classification method, when corresponding parameter needs to reset, a-d are repeated.
In the step a, the biosensor with difference in functionality is given to set according to APTEEN agreement combinations actual conditions Different parameter P={ HT,ST,CT, it is as follows when biosensor is registered according to the function distribution ID and P of node：
Wherein C represents mobile device.
It is close using Diffie-Hellman according to biosensor and mobile device all shared parameters in the step b Key exchange method, generates encryption key, and detailed process is as follows：
(1) biosensor siChoose the largest prime q less than hard threshold valuesiWith the plain root a of its onei, if there is no then Choose the least prime q more than hard threshold valuesiWith the plain root a of its onei, mobile device C is according to identical method selection prime number qiWith Its plain root ai, such C is without sending prime number and plain root to node, and node reduces node energy and disappeared without receiving related data Consumption, adds the security of the plain root of prime number.
(2) biosensor siSelect a random number ri, calculateBy YiCryptographic Hash and life with public key No. ID of reason sensor is sent to C, and we carry out Hash operation using conventional MD5 algorithms, and C is by comparingNext pair Biosensor is authenticated：
(3) C selects a random number RiCalculateC is by Yi' and with private key by siNo. ID encryption after it is wide Broadcast, siAfter node received data packet with public key to ID decrypt, if successful decryption illustrates that data source is credible, will extract No. ID with The ID of oneself cans be compared to compared with by the data packet discarding if differing, Y is preserved if identicali'。
C→si(i=1,2 ..., n)=[Yi',E(ID)]。
(4) C and siEncryption key is calculated respectively：
C：
si：
In the step c, data are transmitted using APTEEN agreements, data are encrypted using symmetric cryptographic algorithm, subtracted Energy expenditure when encrypting less, integrity verification and the authentication of node data are carried out using Merkle trees, as shown in figure 5, its Comprise the following steps that：
(1) according to APTEEN agreements, s is worked asiData d >=the H perceived firstTWhen, siD is sent to C, and by d deposits In portion variable SV, s is worked as afterwardsiData d >=H of perceptionTAnd | d-SV | >=STOr between the time of current time and last time transmission Every Δ t >=CTWhen, siNode sends perception data.
(2) in order to verify siReliability and data integrality, to perception data carry out Hash operation, utilize Merkle Tree, the cryptographic Hash of cryptographic Hash and public key to perception data carries out Hash operation.
(3) C is received after data, and corresponding key K is found according to IDiIf using KiEnergy successful decryption then illustrates data source It is credible, the data after decryption are carried outComputing, operation result is compared with the value in packet Compared with if equal, further authentication data is from the horse's mouth, and data are not tampered with, if KiIt is unable to successful decryption explanation Data source is unreliable, abandons the packet.
In the step d, s is judged by judging whether encryption key and public key changeiWhether compromised, utilized Merkle trees, had both reduced data transfer length, can judge that encryption key and public key are whether change simultaneously again, as shown in fig. 7, Specifically：
With the broadcast message of private key encryption.
If siNode broadcast message, which can be decrypted, with public key illustrates that broadcast message is from the horse's mouth, otherwise abandons this wide Unicast packets, if what C was calculatedIt is equal with the value received, then illustrate siReliably, otherwise, the physiology is passed Sensor is compromised, and the biosensor is rejected manually.
In the step e, comprehensive analysis is carried out to the data of collection using big data method, preferably can be found in data Portion's rule, the parameter that analysis is obtained is more accurate, reliably.
Understand that the design of telehealth service system is had into safety and high efficiency based on above-mentioned, can be by calculating Simple symmetric cryptography, asymmetric signature algorithm are encrypted and signed to user's sensitive data, and data are entered by cloud service Row storage, expands the memory space of remote medical service system.
Medical small documents system based on cloud storage, as shown in figure 8, being divided into four levels.
First layer is made up of LVS combinations OSPF；
The second layer is made up of Web Server；
Third layer is made up of Medical Server；
4th layer is made up of MFS.
Its accessing step includes：
(1) first layer is responsible for load balancing, improves system flexibility and increase throughput of system；
(2) second layer is as level cache, caching part most hot data, the time required to effectively reduction accesses data；
(3) third layer is as L2 cache, and buffer unit gradation dsc data is used as the supplement of level cache；Meanwhile, buffer unit Divide Analysis of Medical Treatment Data result；
(4) the 4th layers are cloud storage layers, preserve all data.
The MFS of cloud storage layer, is divided into six modules as shown in Figure 9, specifically,
Proxy module Proxy can lift access data to memory module Store, can also be to catalogue module Catalogue Shens Please directory and dispatch service；
Catalogue module Catalogue can obtain User Defined data to database module HBase applications；
Database module HBase is responsible for the User Defined metadata such as storage file name, type and directory tree；
Coordination module ZooKeeper can propose metadata change request to catalogue module Catalogue；
Monitoring modular Inspection periodically initiates to check and by the memory module checked to memory module Store Store state is sent to ZooKeeper；
Memory module Store is storage core, can be to ZooKeeper registration storage metadata.
Specifically, described proxy module can externally provide API to the structure and details of external shield storage inside (Application Programming Interface), can so ensure the safety of medical information data, after being also convenient for More functions are provided.
When write request occurs for the catalogue module, it is estimated according to the nodal information that monitoring modular is obtained, then in section Point internal memory completes scheduling, finally returns in memory module.It can be looked into according to demand with filename etc. in HBase during generation read request Ask once, can obtain the specific fileinfo of memory module node set can complete read operation.Catalogue module is responsible for member Information searching and the module of storage scheduling, information are stored in the internal memory of catalogue module.The node content of all catalogue modules is protected Hold consistent, can be in order to extending.
Described data memory module is responsible for all data storages.Due to being the storage for medical data, number of users Long according to the storage cycle, required operation is less, thus only provide increase, delete, read operation, single memory module can also directly work and carry For service independent of other functional modules.In initial phase memory module and Coordination module ZooKeeper to spool and rope Draw and complete bi-directional synchronization.
Described monitoring modular is responsible for monitoring all memory module nodes, and memory module node is carried out at preset timed intervals Check.Monitoring modular can be attempted to read some file, and the path of memory module node in ZooKeeper is just gone to when occurring abnormal More new state.Monitoring modular can monitor the status information of each memory node according to demand, for example delay, remaining time and Write latency etc..
Described database module is self-defining data module, the User Defined such as storage file name, type and directory tree Metadata.
Described Coordination module works with other module cooperatives.
As shown in Figure 10, superblock is devised in Store., will be each small when memory module Store is arrived in file storage File and Key values (the corresponding unique ID of file) constitute a piece, then by piece be sequentially written in or suffix by way of constitute one Individual superblock is stored.Then superblock offset table is set up in Store, corresponding Key values are recorded.Subsequent access When the positions of small documents is just may have access to according to offset table.When Store is arrived in file storage, by each small documents and Key values (text The corresponding unique ID of part) one piece of composition, then by piece be sequentially written in or suffix by way of constitute a superblock and deposited Storage.Then superblock offset table is set up in Store, all Key values are recorded.During subsequent access according to offset table just It may have access to the position of small documents.
As shown in figure 11, in the streaming big data analysis solution based on Hadoop MapReduce, generally collection Data (breathing, cardiovascular, insulin, blood, glucose and body temperature) be transmitted by network, sample frequency is by sensor Capacity and mobile device processing speed determine.According to the arrival rate of data in solution as described above, carry out Task is distributed, and realizes each Map and Reduce task based on finger daemon mechanism, rather than as traditional MapReduce handles the static data being stored on HDFS like that, Map and Reduce tasks just terminate after the completion of data analysis .New Map tasks repeatedly read the stream data being buffered in HDFS, and Map tasks processing data simultaneously produces middle key assignments To sending Reduce tasks to.Stream data process step is as follows：
(1) receive by network transmission come data and the data that are stored in medical small documents storage system, so Data are carried out burst according to size afterwards；
(2) data after burst carry out the matching of data arrival rate by tactful distribution mechanism, are then passed to Map and appoint Business node carries out initial analysis；
(3) processing speed of each Map task node is different, and the data of system are endlessly reached more by network The completion speed for having arrival rate and Map task nodes carries out the reallocation of task, and the intermediate data of generation is cached to often Individual node it is local, for Reduce task nodes read data；
(4) each Reduce task nodes read the intermediate data that Map task nodes are produced, and are further processed, The processing speed of each Reduce task node is also different, and fireballing node continues to read in the output of Map task nodes Between it is data cached；
(5) each Reduce task nodes collect the data handled well according to the period while processing data The final result of output.
Above-described embodiment is intended merely to the explanation present invention, and is not used as limitation of the invention.It should be pointed out that not taking off From some improvements and modifications under the premise of the principle of the invention, protection scope of the present invention should be regarded as.

Claims (8)

1. a kind of medical big data processing method based on body area network and cloud computing, it is characterised in that including：
It is attached to the biosensor with user and perceives user's physiological data；
The biosensor utilizes APTEEN agreements, and symmetric key pair is generated by Diffie-Hellman IKEs The physiological data is encrypted, and physiological data is signed using Merkle trees, and by the physiology number after encryption and signature Mobile device is transferred to according to by Qos Routing Protocols；The mobile device is uploaded using symmetric cryptographic algorithm to biosensor User's physiological data be decrypted and to user identity and transmission data integrality verify；
User's physiological data after checking is sent to Cloud Server by Internet and carries out data storage by the mobile device And data analysis；
The Cloud Server is stored the result of data analysis, and is sent to the mobile device.
2. the medical big data processing method according to claim 1 based on body area network and cloud computing, it is characterised in that institute Biosensor is stated using APTEEN agreements, symmetric key is generated to physiology number by Diffie-Hellman IKEs According to being encrypted, physiological data is signed using Merkle trees, and the physiological data after encryption and signature is passed through into Qos roads Mobile device is transferred to by agreement；The mobile device utilizes user's physiology number that symmetric cryptographic algorithm is uploaded to biosensor According to being decrypted and the integrality of user identity and transmission data being verified, specifically include：
Step a, initialization：The mobile device generates a pair of public and private key to { Mpublic,Mprivate, set according to actual conditions Relevant parameter set P={ the H of each biosensorT,ST,CT, when biosensor asks to register, mobile device broadcast is public Key Mpublic, cryptographic HashAnd each relevant parameter for concentrating No. ID of biosensor and the relevant parameter is private Key MprivateBiosensor is sent to after encryption；Wherein, relevant parameter HTRepresent hard threshold values, STRepresent soft threshold values, CTRepresent to pass The double time interval for being successfully transmitted data to mobile devices of sensor；
Step b, key generation：Mobile device and biosensor utilize Diffie-Hellman key exchange methods, and generation adds The key of close transmission data；
Step c, data transfer：Perception data is encrypted using the key of generation for biosensor, and utilizes Merkle Tree calculates the cryptographic Hash of encryption key and perception data, and the perception data after biosensor ID, encryption and encryption is close The cryptographic Hash of key and perception data is sent to mobile device, and mobile device is received after data by the cryptographic Hash of public key to physiology Sensor carries out preliminary authentication, and data are decrypted by rear for certification, computation key and data after successful decryption Cryptographic Hash, by comparing the whether equal integrality to verify data of cryptographic Hash；
Step d, the detection of compromise biosensor：Mobile device after broadcast message encryption with private key to being transmitted, and physiology is sensed If device can use public key decryptions success, encryption key and public key are carried out by biosensor ID, using Merkle Tree The cryptographic Hash that computing is obtained is sent to mobile device, and mobile device is authenticated by comparing the cryptographic Hash to biosensor, If the value and mobile device calculate obtain inconsistent, rejected manually；
Step e, parameter updates：Data to collection are analyzed and processed, when relevant parameter needs to reset, repeat step A~d.
3. the medical big data processing method according to claim 2 based on body area network and cloud computing, it is characterised in that institute Step b is stated to specifically include：
Biosensor siChoose the largest prime q less than hard threshold valuesiWith the plain root a of its onei, it is big if there is no then selection In the least prime q of hard threshold valuesiWith the plain root a of its onei, mobile device is according to same method selection prime number qiWith its element Root ai；Wherein, i=1,2,3 ..., represent the numbering of biosensor；
Biosensor siSelect a random number ri, then calculateBy YiWith the cryptographic Hash and physiology of public key No. ID of sensor is sent to mobile device, then carries out Hash operation using conventional MD5 algorithms, mobile device is by comparingCome to biosensor siIt is authenticated；
Mobile device selects a random number Ri, calculateMobile device is by Yi' and with private key sensed physiology Device siNo. ID encryption after broadcast, ID is decrypted with public key after biosensor received data packet, if successful decryption illustrates data Source is credible, will extract No. ID and is compared with oneself No. ID, by the data packet discarding if differing, is preserved if identical Yi'；
Mobile device C and biosensor siEncryption key is calculated respectively：
C：
si：
4. the medical big data processing method according to claim 3 based on body area network and cloud computing, it is characterised in that institute Step c is stated to specifically include：
According to APTEEN agreements, as biosensor siData d >=the H perceived firstTWhen, biosensor siD is sent to Mobile device, and d is stored in built-in variable SV, afterwards as biosensor siData d >=H of perceptionTAnd | d-SV | >=STOr Interval of delta t >=the C for the time that person's current time was sent with last timeTWhen, biosensor sends perception data；
Biosensor siHash operation is carried out to perception data, using MERKLE trees, passed throughTo perceiving The cryptographic Hash of data and the cryptographic Hash of public key carry out Hash operation；By the perception data after No. ID, Hash operation andIt is sent to mobile device；
Mobile device is received after data, and corresponding key K is found according to IDiIf using KiEnergy successful decryption is then to the number after decryption According to progressComputing, operation result is compared with the value in packet, if equal, further Authentication data is from the horse's mouth, and data are not tampered with, if KiSuccessful decryption is unable to, the packet is abandoned.
5. the medical big data processing method according to claim 1 based on body area network and cloud computing, it is characterised in that institute State data storage using include load balancing layer, level cache layer, L2 cache layer and cloud storage layer safe storage system come Realize；The load balancing layer is realized by Linux virtual server combination ospf protocol；Its one-level cache layer is taken by Web Business device realization, the data high for storing access frequency；Secondly level cache layer is realized by medical server, level cache is used as Supplement, be additionally operable to cache part Analysis of Medical Treatment Data result；Its cloud storage layer is realized by distributed file system, for depositing Storage all initial data and analysis result data；Load balancing, Ran Houfang are solved by load balancing layer by elder generation during data access Level cache layer is asked, if not finding access data in level cache layer, L2 cache layer is accessed, if not in L2 cache layer In find access data, then access cloud storage layer read data.
6. the medical big data processing method according to claim 5 based on body area network and cloud computing, it is characterised in that institute State distributed file system including proxy module, catalogue module, memory module, monitoring modular, HBase database modules and ZooKeeper Coordination modules；The module externally provides api interface, the structure and details of storage inside is shielded, to interior to storage Module lifts access request of data, and to catalogue module application directory and dispatch service；The catalogue module be used for The application of HBase database modules obtains User Defined data, and Receiving Agent module and ZooKeeper Coordination modules please Ask；The HBase database modules, which are used to store, includes the User Defined metadata of filename, type and directory tree；It is described ZooKeeper Coordination modules are used to propose metadata change request to catalogue module；The monitoring modular is regular to memory module Initiate to check and the state of the memory module checked is sent to ZooKeeper Coordination modules；The memory module is storage Core, for registering data storage to ZooKeeper Coordination modules.
7. the medical big data processing method according to claim 6 based on body area network and cloud computing, it is characterised in that institute State and superblock is devised in memory module；It is when memory module is arrived in file storage, each small documents and file are corresponding unique ID constitutes a piece；Again by piece be sequentially written in or suffix by way of constitute a superblock and stored；Then in storage Superblock offset table is set up in module, corresponding file ID value is recorded.
8. the medical big data processing method according to claim 1 based on body area network and cloud computing, it is characterised in that institute State data analysis to realize using the streaming big data processing method based on Hadoop MapReduce, specifically include：
Receive by network transmission come data and the data that are stored in safe storage system, then data according to big Small carry out burst；
Data after burst carry out the matching of data arrival rate by tactful distribution mechanism, are then passed to Map task nodes Carry out initial analysis；
The processing speed of each Map task node is different, by the data of network transmission to system according to arrival rate and Map The completion speed of task node carries out the reallocation of task, and the intermediate data of generation is cached to each Map task nodes, supplies Reduce task nodes read data；
Each Reduce task nodes read the intermediate data that Map task nodes are produced, and are further processed, each The processing speed of Reduce task nodes is also different, and fireballing Reduce task nodes continue to read the output of Map task nodes Intermediate buffer data；
Each Reduce task nodes carry out the data handled well according to the period collecting output most while processing data Whole result.
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