CN109003104B - Service trust quantitative calculation method based on grey correlation in cloud calculation - Google Patents

Service trust quantitative calculation method based on grey correlation in cloud calculation Download PDF

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CN109003104B
CN109003104B CN201810801276.8A CN201810801276A CN109003104B CN 109003104 B CN109003104 B CN 109003104B CN 201810801276 A CN201810801276 A CN 201810801276A CN 109003104 B CN109003104 B CN 109003104B
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李晓会
陈潮阳
张兴
孙福明
李波
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Abstract

The invention discloses a gray correlation-based service trust quantification method in cloud computing, which comprises the following steps: step one, reading historical credit data of cloud users i, constructing a decision matrix A, and calculating trust weight TW of each cloud user i for service evaluationiAnd credit CWiWeighting to obtain a weight matrix A' of the evaluation factors; step two, respectively calculating trust gray level TViAnd the gray CV of creditiObtaining a gray level R, and calculating objective preference B of the cloud user to each scheme evaluation factor interval through B ═ A' R; thirdly, calculating the subjective preference theta of the cloud user to each scheme evaluation factor intervali(ii) a Step four, calculating objective preference B and subjective preference theta of each scheme evaluation intervaliThe gray correlation coefficient epsilon; and fifthly, calculating a final trust quantification value T of the cloud user i to the cloud service in the evaluation interval. And performing current trust quantitative calculation according to historical credit data of the cloud user, realizing service selection in a cloud calculation environment, and improving the safety and the resisting capability of a cloud service system.

Description

Service trust quantitative calculation method based on grey correlation in cloud calculation
Technical Field
The invention relates to a gray association-based service trust quantitative calculation method in cloud computing, and belongs to the field of computer network security.
Background
The core technology of the cloud computing platform comprises mass data distributed storage technology, distributed programming, virtualization and the like, and the core technology of the cloud computing application is service-oriented architecture and computing. Service discovery under a service-oriented architecture means that when a service requester generates a certain requirement, firstly, available services are inquired from a public directory of a service registration center, and then the center returns the services meeting the requirements of users to the service requester. When the returned result received by the service requester contains a plurality of candidate services, the service selection module is executed.
Huge service resources in the cloud platform provide a riding opportunity for malicious service providers, great troubles are caused for users, waste is caused for limited resources, a unique service providing mode of cloud computing brings people with unprecedented user experience and unique safety problems, and contradiction between large-scale service resource providing and user controllable personalized service requirements under a cloud computing environment is an important issue. The existing methods for personalized access control, access filtering and the like cannot completely solve the problem of controllable personalized service requirements of cloud users.
Disclosure of Invention
The invention designs and develops a gray association-based service trust quantitative calculation method in cloud calculation, which is used for carrying out current trust quantitative calculation according to historical credit data of cloud users, realizing service selection in a cloud calculation environment and improving the safety and the resisting capability of a cloud service system.
The technical scheme provided by the invention is as follows:
a grey correlation-based service trust quantification calculation method in cloud computing comprises the following steps:
firstly, selecting a cloud user, and determining an evaluation interval;
step two, reading the latest credit data of the cloud user and constructing a decision matrix A
Step three, carrying out standardization processing on the decision matrix A to obtain a standardized decision matrix B;
step four, calculating the subjective preference value theta of the cloud user to each case evaluation factor intervali；
Step five, calculating a gray correlation coefficient epsilon of each scheme evaluation interval;
and step six, calculating a final trust quantification value T of the cloud user i to the cloud service in the evaluation interval.
Preferably, the credit data comprises a Trust value TrustiAnd Credit value Crediti。
Preferably, the second step includes:
let A be the decision matrix, with m schemes A1,A2,...,AmT factors F1,F2,...,FtN evaluation intervals, protocol AiIn evaluation of factor FjIs evaluated asijWhen A is then
Figure GDA0003331204940000021
Preferably, the third step includes:
calculating trust weight TW of each cloud user for service evaluationiAnd credit weight CWi：
Figure GDA0003331204940000022
CWi＝1-TWi；
Trust gray level TV for calculating evaluation of each cloud user on serviceiAnd the gray CV of crediti：
TVi＝Trusti×(1-Crediti)；
CVi＝Crediti×(1-Trusti)；
The grayscale matrix R { (x, y), μR(x,y),vR(X, Y)) X ∈ X, Y ∈ Y }, where μR(X, Y) is a membership, V, of a given space X ═ X }, Y ═ Y } gray-scale matrix RR(x, y) is grayscale; the weight matrix a 'according to the gradation matrix evaluation factor is expressed as a' ═ a [ ("aij,vA'(aij))]mtWherein a isijNot less than 0, i-1, 2, t, and
Figure GDA0003331204940000023
if the weight assignment of each factor is clearly specified, a ═ aij,0)]mt；
The decision matrix is recorded as
Figure GDA0003331204940000024
Wherein b isijIs the decision maker to the scheme AiAbout evaluation section FjObjective preference of (2).
Preferably, the subjective preference θ in step four isiComprises the following steps:
Figure GDA0003331204940000031
Figure GDA0003331204940000032
preferably, the gray correlation coefficient epsilon is:
Figure GDA0003331204940000033
preferably, the sixth step includes, in the evaluation interval
Figure GDA0003331204940000034
In the method, a final trust quantization value T of a cloud user i to the cloud service is calculated,
Figure GDA0003331204940000035
the invention has the following beneficial effects: the grey correlation-based service trust quantification method is used for quantifying the service trust of cloud users, a preference trust evaluation mechanism is introduced to obtain a comprehensive trust value by combining direct trust and recommendation trust to obtain a service list to be selected to make a decision for service selection, the service trust quantification depends on the privacy information provision of the users, and the service trust quantification is more accurate along with the increase of the privacy information provision.
The cloud user application of the invention is based on gray correlation service trust quantification, has sensitive self-adaption and regulation and control capability on behavior change of the service node, and has resistance capability on fraud behaviors provided by the service.
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Fig. 1 is a schematic diagram of an interaction establishment process between a cloud user and a service agent according to the present invention.
Fig. 2 is a correlation diagram of the service providing record table and the user evaluation table according to the present invention.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
As shown in figures 1-2, the invention provides a gray correlation-based service Trust quantification calculation method in cloud computing, which is characterized in that n cloud users i with historical interaction with a service party are selected, and the latest Trust value Trust stored by the cloud users i on the service party is readiAnd Credit value CreditiAnd performing gray level calculation and preference calculation, and finally calculating a service trust quantification value T in the evaluation interval. The method specifically comprises the following steps:
step 1, selecting n cloud users i with historical interaction with a server, and sequentially reading the Trust value of the cloud users i stored in the server at the latest timeiAnd Credit value CreditiWhen the cloud user node does not have the service history request information, Trusti＝0，Crediti＝0；
Step 2, constructing a decision matrix A according to the data read in the step 1:
let A be the decision matrix, with m schemes A1,A2,...,AmT factors F1,F2,...,FtN evaluation intervals, protocol AiIn evaluation of factor FjIs evaluated asijIf A is:
Figure GDA0003331204940000041
step 3, standardizing the decision matrix,
calculating trust weight TW of each cloud user for service evaluationiAnd credit weight CWi：
Figure GDA0003331204940000042
CWi＝1-TWi；
Trust gray level TV for calculating evaluation of each cloud user on serviceiAnd the gray CV of crediti：
TVi＝Trusti×(1-Crediti)；
CVi＝Crediti×(1-Trusti)；
The grayscale matrix R { (x, y), μR(x,y)，vR(X, Y) | X ∈ X, Y ∈ Y }; in the formula, muR(X, Y) is the degree of membership of the given space X ═ X }, Y ═ Y } gray scale matrix R, v ═ Y } gray scale matrix RR(x, y) is grayscale;
the weight matrix a 'according to the gradation matrix evaluation factor is expressed as a' ═ a [ ("aij,vA'(aij))]mtWherein a isijNot less than 0, i-1, 2, t, and
Figure GDA0003331204940000043
if the weight assignment of each factor is clearly specified, a ═ aij,0)]mt；
Obtaining a normalized decision matrix, and recording the normalized decision matrix as
Figure GDA0003331204940000044
Wherein b isijIs the decision maker to the scheme AiAbout evaluation section FjObjective preference of (2).
Is provided with
Figure GDA0003331204940000045
For two sets, synthesizing relationships
Figure GDA0003331204940000046
Wherein the content of the first and second substances,
Figure GDA0003331204940000047
step 4, calculating subjective preference theta of the cloud user to each scheme evaluation factor intervali，
Figure GDA0003331204940000051
Figure GDA0003331204940000052
Step 5, calculating evaluation interval block of each evaluation schemePolicy matrix B and subjective preference θ of each scenarioiThe gray correlation coefficient e of (a) is,
Figure GDA0003331204940000053
wherein epsilonl j/εr jThe similarity between the objective preference and the subjective preference of a decision maker in the j evaluation interval is reflected as the degree of association, and the greater the similarity is, the closer the objective preference and the subjective preference is, so that the evaluation interval is determined;
Figure GDA0003331204940000054
the larger the value of (A), the more the decision maker agrees with the j evaluation interval, and the final evaluation result is obtained as the interval
Figure GDA0003331204940000055
So that
Figure GDA0003331204940000056
Step 6, according to the evaluation interval
Figure GDA0003331204940000057
The final trust quantization value of the cloud user for the cloud service is T,
Figure GDA0003331204940000058
examples
Assuming that in a cloud computing environment, a cloud user of service interaction is C, cloud service is S, and trust quantization computing steps are as follows:
(1) selecting 4 cloud users having historical interaction with the service by adopting a random selection method, and respectively recording the cloud users as: c1,C2,C3,C4And C of the present service requirement0And jointly forming an evaluation 'expert' for the trust of the service, taking the trust and the credit as evaluation attributes, wherein an evaluation interval W is as follows:
w1＝[0,0.25],w2＝(0.25,0.5],w3＝(0.5,0.75],w4＝(0.75,1]，
namely, the evaluation scheme m is 5, the evaluation factor t is 2, and the number of evaluation intervals n is 4;
(2)C0in order to read the last trust value and credit value of the service between the cloud user and the selected 4 cloud users, the reading result is shown in table 1.
TABLE 1 Trust and Credit values
Figure GDA0003331204940000059
(3) A is a decision matrix with 5 schemes A1，A2，A3，A4，A52 factors FtAnd FεThen the decision matrix A is
Figure GDA0003331204940000061
(4) The decision matrix is normalized and the decision matrix is normalized,
Figure GDA0003331204940000062
1) calculating trust and credit weight of each cloud user to the service evaluation:
Figure GDA0003331204940000063
the calculation results are shown in table 2:
TABLE 2 Trust and Credit weights for cloud users
Figure GDA0003331204940000064
2) Calculating the trust and the credit gray level of each evaluation cloud user:
TV0＝Trust0×(1-Credit0)＝0.81×(1-0.92)＝0.0648
CV0＝Credit0×(1-Trust0)＝0.92×(1-0.81)＝0.1748
the calculation results are shown in table 3:
TABLE 3 Trust Gray Scale and Credit Gray scale
Figure GDA0003331204940000065
Figure GDA0003331204940000071
Figure GDA0003331204940000072
(5) Calculating subjective preferences for each solution
Figure GDA0003331204940000073
The subjective preferences were:
θ0＝[0.224,0],θ1＝[0.195,0],θ2＝[0.161,0],θ3＝[0.215,0],θ4＝[0.205,0]
(6) calculating a gray correlation coefficient
ε＝[(0.224,0),(0.195,0),(0.161,0),(0.215,0),(0.205,0)]。
Figure GDA0003331204940000074
ε1＝0/1,ε2＝0/1,ε3＝0.776/0.00081,ε4＝0.839/0.000007
Then has ε4The maximum value indicates that the objective preference of each evaluation scheme evaluation interval 4 is most similar to the subjective preference of each scheme.
(7) Calculating a service trust quantification value T according to a formula
Figure GDA0003331204940000075
T＝0.75+(1-0.75)×0.839＝0.9598。
The trust and the credit are obtained through a service evaluation table of the cloud user and a record table provided by the cloud service agent, as shown in fig. 2. The cloud user service evaluation table is used for evaluating and recording the information provided by each cloud service, and the table structure mainly comprises: cloud user ID, service type, service agent identification, time, space, historical behavior trust (trust after the trust and feedback), interaction times and the like. The service evaluation table only has the right to read and write the service evaluation table. When a cloud user makes a service request, the user searches a service evaluation table according to the service type, and if the number of searched records is enough, trust quantification is carried out according to the user preference service attribute; otherwise, the last record of the service table is found, the found service is used as a service to be selected to perform trust updating respectively, and the service requirement is submitted through the cloud service interface to obtain a new optional service. The new service is obtained, and the cloud service is comprehensively quantized in the first step through direct trust and recommended trust. And after the whole service interaction is finished, updating the service evaluation table of the user. The service evaluation table only has the writing and modifying authority of the user, but can be read by other users to provide decision support for other users and the trust quantification of the service.
The main functions of establishing the service evaluation table at each cloud user are as follows:
(1) the behavior of the cloud service is recorded, and the trust, credit and other important information data of each service are structurally managed, so that the service provision of the service is tracked and analyzed;
(2) the information in the service table of each user can be shared by other users, and when other users trust and quantify the service, necessary data support such as trust, credit and the like is provided, so that the trust and quantification of the service are more accurate;
(3) the cloud user service evaluation table carries out block centralized storage management according to the cloud service identification, so that the searching efficiency is high, and the service evaluation records are stored sequentially from top to bottom according to the distance of the historical service providing time, so that the latest service providing record is kept at the forefront.
After the cloud user service requirements are issued through a service interface (the service is on the premise of having automatic acquisition and discovery functions), a service discovery module is called, a cloud service dynamic proxy inquires a service provision record table (cloud user identification and user trust) of the cloud service dynamic proxy according to the service type requirements, if the cloud user is trusted, the user initiatively asks for a tassel, and the user is contacted with a trusted service proxy with interaction according to an evaluation table. The automatic service discovery is a precondition of interaction between a cloud user and a service, and firstly (1) the cloud user issues a service request; the cloud service agent inquires a service record table according to the service type, and if the service type is matched and the user has integrity service interaction history, the cloud service agent sends a self service agent identifier to the cloud user and feeds back the self service agent identifier; (3) and (4) the cloud user receives the identifier sent by the service agent, inquires the user evaluation table, and if the integrity service interaction history exists, establishes interaction. The connection interaction establishment procedure is shown in fig. 1.
On the premise of the historical trust of the cloud service and the cloud user, establishing a connection between the two service parties, calculating the comprehensive trust degree of the service by using a grey correlation analysis method, and if the trust condition of the user is met, adding a to-be-selected service list to complete service discovery; when the cloud user service is selected, after trust quantification, if the cloud service is provided, the cloud user is allowed to search a cloud service providing record table, if the own history record is found, the own related information is verified, and corresponding updating is carried out; otherwise, namely the cloud service provides the service for the cloud user for the first time, the cloud user adds important information such as the node identification of the cloud user, the entry address of the user evaluation table and the like to the cloud service providing record, then allows the cloud user to provide the service, and finally inserts evaluation information such as trust, credit and the like of the cloud service into the evaluation table of the cloud user. Applying a grey correlation analysis method, wherein cloud service trust evaluation factors of a cloud user pair comprise: trust and credit records of the cloud user for the service; and (3) evaluating expert set: { cloud user, expert 1...., expert n }.
Wherein, the selection process of the expert is as follows: after the connection between the cloud user and the optional service is established, the cloud user obtains other cloud users with interaction history by inquiring the cloud service provision record, inquires the cloud user service evaluation table, obtains a suitable cloud user as an evaluation expert according to the interaction time, and takes relevant information in the evaluation expert service evaluation table as evaluation elements. And dividing the trust into various evaluation intervals as elements of the evaluation attribute set.
The method comprises the steps that a domain service agent manages intra-domain services under a hierarchical trust management architecture based on agents and trust domains in a cloud, the services are matched according to service attributes of cloud users and serve as recommenders of the services to complete interaction with the cloud services, comprehensive trust evaluation is conducted on the services according to the service agents, and the service agents provided by the cloud service agents are important composition problems of comprehensive quantification of the services.
And the method can conveniently select experts when the cloud user trusts the service to be quantized, construct a cloud service agent providing record table and establish the relation with a cloud user evaluation table. The service providing table is stored on each service agent, but the service agents do not have writing and modifying authority for the service providing record table, and the user provided by the cloud service has authority for reading all records, writing or modifying the records related to the user for the service providing record table. Each service providing record table is used for recording all cloud user information provided by the service agent, and the cloud service providing record table structure mainly comprises: the service agent identification, the cloud user trust level, the service type, the service interaction time, the address of the evaluation table of the cloud user and the like. Through the association between the service providing table and the user evaluation table, the historical service providing behavior information of the cloud service can be fully shared in the cloud environment, and the risk of joint fraud is greatly reduced.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.

Claims (4)

1. A gray association-based service trust quantification calculation method in cloud computing is characterized by comprising the following steps:
firstly, selecting a cloud user, and determining an evaluation interval;
reading the latest credit data of the cloud user, and constructing a decision matrix A;
thirdly, aiming at the decision matrix A, obtaining a normalized decision matrix B by adopting a gray matrix R and a weight matrix A';
the decision matrix is recorded as
Figure FDA0003470572700000011
Wherein b isijIs the decision maker to the scheme AiAbout evaluation section FjObjective preference of bij lAnd bij rFor decision maker to plan AiAbout evaluation section FjInterval value interval of the objective preference;
step four, calculating the subjective preference theta of the cloud user to each evaluation intervali；
Subjective preference θiComprises the following steps:
Figure FDA0003470572700000012
in the formula, CreditiIn order to be the value of the credit, the credit is,
Figure FDA0003470572700000013
i＝1，2...，m，
Figure FDA0003470572700000014
step five, calculating objective preference and subjective preference theta of each evaluation intervaliThe gray correlation coefficient epsilon;
Figure FDA0003470572700000017
in the formula, epsilonlFor objective preference of the gray correlation coefficient, epsilonrThe subjective preference gray correlation coefficient;
calculating a final trust quantization value T of the cloud user i to the cloud service in an evaluation interval;
in the evaluation interval
Figure FDA0003470572700000018
In the method, the final trust quantification value of the cloud user i on the cloud service is calculated
Figure FDA0003470572700000015
In the formula (I), the compound is shown in the specification,
Figure FDA0003470572700000016
trust is a cloud service comprehensive Trust quantitative value for an evaluation interval, ajJ upper limit of evaluation interval, parameter bjJ is the lower limit of the evaluation interval, εj iAnd evaluating the objective preference degree of the interval for j by the decision maker.
2. The grey association-based service Trust quantification calculation method in cloud computing according to claim 1, wherein the credit data comprises a Trust value TrustiAnd Credit value Crediti。
3. The grey association-based service trust quantification calculation method in cloud computing according to claim 2, wherein the second step comprises:
let A be the decision matrix, with m schemes A1，A2，...，AmT factors F1，F2，...，FtN evaluation intervals, protocol AiIn the evaluation interval FjIs evaluated asijIf A is:
Figure FDA0003470572700000021
4. the grey association-based service trust quantification calculation method in cloud computing according to claim 3,
calculating trust weight TW of each cloud user on service evaluationiAnd credit weight CWi：
Figure FDA0003470572700000022
CWi＝1-TWi；
Trust gray level TV for calculating service evaluation of each cloud useriAnd the gray CV of crediti：
TVi＝Trusti×(1-Crediti)；
CVi＝Crediti×(1-Trusti)；
Application TVi＝Trusti×(1-Crediti) Computing confidence levels, applying CVi＝Crediti×(1-Trusti) Calculating the credit gray scale to obtain a gray scale matrix R, then
Figure FDA0003470572700000024
Wherein [ bij l，bij r]Reflecting the objective preference of each scheme to the evaluation interval;
the grayscale matrix R { (x, y), μR(x，y)，vR(x，y)|x∈X，y∈Y}；
In the formula, muR(X, Y) is the degree of membership of the given space X ═ X }, Y ═ Y } gray scale matrix R, v ═ Y } gray scale matrix RR(x, y) is grayscale;
the weight matrix a 'in the evaluation interval according to the gradation matrix R is represented as a' ═ a [ [ (a)ij，vA′(aij))]mtIn the formula aijIs not less than 0, i is not less than 1,2, and
Figure FDA0003470572700000023
in the formula, vA′(aij) Represents the gray level of the weight matrix, [ 2 ]]mtRepresenting m rows and t columns of the matrix.
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