CN104092564B - A kind of cloud storage service credit assessment method - Google Patents
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Abstract
A kind of cloud storage service credit assessment method, it includes two stages, six steps；Stage one：Server node credit rating, including step one：Calculate CiTo SjDirect trust value TD(Ci, Sj)；Step 2：Calculate CiTo SjRecommendation trust TR(Ci, Sj)；Step 3：Calculate CiTo SjFinal trust value T (Ci, Sj)；Step 4：Calculation server node SjSystem trust value T (Sj).Stage two：User node credit rating, including step 5：Calculate user node CiSystem trust value T (Ci)；Step 6：Belief updating.The present invention is by the use of technology evidence as the source of trust evaluation, behavior based on user to user node carries out credit rating, the result of data integrity validation is measured as the trust feature of CSP storage services to CSP storage services, so as to ensure the objective fairness and verifiability of trust metrics.
Description
Technical field
The present invention relates to a kind of cloud storage service credit assessment method, detected by data integrity under cloud storage environment
Credit rating is carried out to server node and user node, belongs to field of cloud calculation.
Background technology
Cloud storage is a kind of online memory module, i.e., user's (client) passes through certain association with server (high in the clouds)
View, by the outsourcing data storage of oneself in high in the clouds, this emerging storage mode, flexibility, low cost with cloud computing,
Scalability.User can whenever and wherever possible access high in the clouds, obtain the data of oneself；User is empty according to oneself actually used storage
Between pay, reduce data maintenance and purchase storage device cost, and can according to storage the need for be extended.With
Continuing to develop for cloud storage, increasing cloud service provider (Cloud Service Provider, CSP) is occurred in that,
Substantial amounts of different types of cloud storage service is provided.Because data are stored in high in the clouds by user, physically lose to data
Control, the data for being stored in high in the clouds may be tampered, and delete etc..Therefore, the prestige of cloud service provider is provided with them
Cloud storage quality turn into user be concerned about emphasis, so need according to CSP provide cloud storage service public affairs are carried out to them
Positive credit rating.
The method of the existing credit appraisal for cloud storage service is divided into two kinds substantially：A kind of is by the appearance of cloud storage
The features such as amount, speed, price are analyzed contrast, propose that corresponding evaluation method is scored to CSP；Another kind is that user makes
Given a mark or voted with after the cloud service of CSP offers, then these subjective assessments are carried out into polymerization analysis.These evaluation methods
There are some defects to a certain extent：1) evaluation of the user to CSP is subjective assessment, carries out polymerization point to subjective assessment merely
Analysis will certainly be present evaluates prejudice；2) only taken into account evaluation of the user to CSP, and mostly have ignored user be likely to it is not sincere
The situation for being is carried out, for example malice is evaluated, falseness ballot, lazy node etc.；3) it is not involved with the core of CSP storage services measurement
The heart：It is stored in the integrality of high in the clouds data.So proposing that a kind of cloud storage service credit assessment method of objective and fair seems particularly
It is important.
In order to realize the fair appraisal to cloud storage service, the present invention is by the use of objective technology evidence as trust evaluation
Source, while the behavior based on user to user node carries out credit rating.We measure using to cloud storage service
Core feature：The result of data integrity validation is as the trust feature of CSP storage services to CSP storage service degree of being subject to
Amount, so as to ensure the objectivity of trust metrics.Simultaneously by analyzing participative behavior pair of the user in trust evaluation interaction
The prestige of user node is evaluated, to prevent malice from evaluating and detect lazy node etc., it is ensured that the fairness of credit rating, visitor
The property seen and verifiability.
The content of the invention
(1) goal of the invention
The purpose of the present invention is to propose to a kind of cloud storage service credit assessment method, according to the core of CSP storage services measurement
Heart feature, the credit appraisal that the result of data integrity validation can verify that as the source of trust evaluation to CSP；Meanwhile,
Behavior based on user carries out credit rating to user node, can be effective against false evaluation, lazy node, it is ensured that prestige is commented
The fairness of valency, objectivity and verifiability.
(2) technical scheme
In order to achieve the above object, the present invention has used result the coming as trust value computing of data integrity validation
Source, its technical scheme is as follows.
Cloud storage trust model of the present invention includes two network entities：1) store data in high in the clouds individual or
Enterprise institution, i.e. user；2) there is the cloud service provider of special resource and computing capability.
It can be in two stages according to the stage of execution for the present invention：Stage one：Server node credit rating, is CSP storages
Service credit evaluation phase；Stage two：User node credit rating, is the user node credit rating rank based on user behavior
Section.
A kind of cloud storage service credit assessment method of the present invention, the technical scheme of the method is as follows：
Stage one：Server node credit rating：Including step 1~step 4, client node C is calculated respectivelyiTo service
Device node SjDirect trust value (only by CiItself and SjThe trust value that interactive computing is obtained) TD(Ci, Sj) and recommendation trust
(by other nodes and SjThe trust value that interactive computing is obtained) TR(Ci, Sj)；Calculated by direct trust value and recommendation trust
To CiTo SjFinal trust value T (Ci, Sj)；All of client node be polymerized again to SjTrust be worth to server node Sj
System trust value T (Sj)。
Step 1：Calculate CiTo SjDirect trust value TD(Ci, Sj)：To carry out the result of data integrity validation each time
({ 0,1 } for being given is evaluated, and 1 expression is verified, i.e., data are complete, and 0 represents that checking does not pass through, i.e., data are imperfect) is
Input, the desired value characteristic being distributed using beta completes CiTo SjDirect trust value measurement.
Step 2：Calculate CiTo SjRecommendation trust TR(Ci, Sj)：By with server node SjOther clients for interacting
End node Cr∈ID(Sj, Ci) to server node SjThe comprehensive measurement of trusting degree calculate CiTo SjRecommendation trust：
We are with CrTo SjDirect trust value TD(Cr, Sj) as CrTo SjTrusting degree, trust weight is defined as CrAnd CiTo him
The server node that interacted jointly carried out data integrity validation result similarity, the data that they are given are complete
Property evaluation result is more similar, and the trusting degree between them is higher, with root mean square RMS (Cr, Ci) be identified.
Step 3：Calculate CiTo SjFinal trust value T (Ci, Sj)：The direct trust value that step 1 and 2 are obtained and recommendation
Trust value carries out polymerization and is calculated CiTo SiFinal trust value.
Step 4：Calculation server node SjSystem trust value T (Sj)：By all of client node Ci(i=1to m)
To SjTrust value T (Ci, Sj) be polymerized, its weight definition is CiTrust value T (Ci), finally give all Sj(j=1to
N) system trust value.
Stage two：User node credit rating：The credit value of the server node calculated according to the first stage, is set
One decision threshold TthTo judge SjWhether it is honest server node.Then the behavior according to user node is entered to its prestige
Row is accumulative, and accumulative rule is：With honest server node carry out integrity verification result for 1 and with dishonest server section
Point carries out integrity verification result for 0 is accordingly to be regarded as dishonest behavior, and interbehavior trust value adds up；Other are accordingly to be regarded as dishonest row
For interbehavior trust value does not add up.The trust value to server node and user node is iterated renewal simultaneously, reaches it
To stable state.
Step 5：Calculate user node CiSystem trust value T (Ci)：Determine first and CiThe server node for interacting
Set I (Ci), then judge CiWith I (Ci) in each element interactions history, accumulative rule is trusted to trust value according to interaction
Added up, then the degree of closeness P of aggregate-value and server node and utmost good faith/non-honest will be trustedjCarry out product weightings,
User node C can just be calculatediSystem trust value T (Ci)。
Step 6：Belief updating：In step 4, we will use the user node C not calculated alsoiSystem letter
Appoint value T (Ci), still be provided with T (Ci) initial value Tdefault.In this step, it would be desirable to the calculation procedure according to more than
And be calculated the server node of stable state and the trust value of user node by way of iterative calculation, i.e., constantly to service
The system trust value of device node and user node is iterated calculating, its value is reached convergence.
Wherein, " calculating C described in step 1iTo SjDirect trust value TD(Ci, Sj) ", its calculating process is as follows：Wherein nijIt is client node CiWith server node Sj
Carry out the total degree of integrity verification；FPDP(Ci, Sj, k) it is client node CiWith server node SjCarry out kth time integrality
The result of checking；VEF(Ci, Sj, k) it is client node CiWith server node SjCarry out environment during kth time integrity verification
The factor.
Wherein, " calculating C described in step 2iTo SjRecommendation trust TR(Ci, Sj) ", its calculating process is as follows：Wherein ID (Sj, Ci) it is I (Sj) remove in set
CiThe set of node；RMS(Cr, Ci) it is root mean square, for being expressed as CiTo CrTrusting degree, its computational methods is as follows：Wherein SS (Cr, Ci) be and visitor
Family end node CrAnd CiAll carried out the server node set of integrity verification.
Wherein, " calculating C described in step 3iTo SjFinal trust value T (Ci, Sj) ", its calculating process is as follows：T
(Ci, Sj)=α TD(Ci, Sj)+β·TR(Ci, Sj), wherein α and β is respectively the weight of direct trust value and recommendation trust.
Wherein, " calculation server node S described in step 4jSystem trust value T (Sj) ", its calculating process is such as
Under：
Wherein, " calculating user node C described in steps of 5iSystem trust value T (Ci) ", its calculating process is as follows：Wherein I (Ci) be and CiThe set of the server node for interacting；
I.e.It is a set, the number of element is in set | I (Ci) |, what is represented respectively is and CiThe volume of interactive server node
Number；aijIt is client node CiWith server node SjCan be to T (C in interaction resultsi) accumulative trust value；PjIt is server
The degree of closeness of node and utmost good faith/non-honest.
(3) advantage and effect
A kind of cloud storage service credit assessment method of the present invention, its advantage and effect are：1) believe with existing Cloud Server
Reputation evaluation model is compared, and this model can not only carry out credit rating to Cloud Server node, it is also possible to which user node is carried out
Credit rating, can avoid false evaluation, the lazy node of resistance；2) it is different from the reputation model based on subjective assessment, the present invention
It is that based on objective technology evidence, i.e. the result of data integrity detection carries out the calculating of trust value so that model has stronger
Objectivity and fairness；3) model can finally reach stable state, that is, the trust value of each node for calculating reaches convergence, makes mould
Type has correctness and stability；4) communication environment factor variable is provided with, for data integrity validation process each time
Significance level difference is weighted to the weight that this communicates, it is possible to prevente effectively from malice is evaluated.
Brief description of the drawings
Fig. 1 is FB(flow block) of the present invention
Fig. 2 is present system Organization Chart
Specific embodiment
The present invention includes two entities, user and cloud server.Described data are held effectively below with reference to accompanying drawing
Verification method is described in detail, and Fig. 1 is FB(flow block) of the present invention；Fig. 2 is present system Organization Chart；
Main symbol and algorithmic translation：
(1)FPDP(Ci, Sj, k)：Client node CiWith server node SjCarry out kth time PDP (Provable Data
Possession, the data property held is proved) the result；
(2)N(Ci, Sj)：Client node CiWith server node SjCommunicated the total degree of (PDP checkings), can be abbreviated
It is nij；
nij1：Client node CiWith server node SjCarry out PDP checkings and provide the number of times that the result is 1
nij2：Client node CiWith server node SjCarry out PDP checkings and provide the number of times that the result is 0
nij3：Client node CiWith server node SjPDP checkings are carried out to provide the result for the number of times of NULL therefore have：
nij=nij1+nij2+nij3；
(3)VEF(Ci, Sj, k)：Verification Environment Factor, client node CiWith server section
Point SjCarry out envirment factor when kth time PDP is verified.Effect：Can be attacked with preventing malice, such as it is very sincere in small transaction,
The prestige of oneself is lifted, and it is non-honest so as to make a profit in big transaction；
(4)I(Sj)：With server node SjCarried out the set of the client node of PDP checkings；
(5)ID(Sj, Ci)：I(Sj) C is removed in setiThe set of node；
(6)RMS(Cr, Ci)：Root-mean-square, root mean square, for comparison node CiAnd CrTo their common interactions
The evaluation difference of the server node crossed, that is, be expressed as CiTo CrTrusting degree, if evaluate situation be similar to, i.e., theirs is equal
Root value is high, then trusting degree is high, otherwise trusting degree is low；
(7)SS(Cr, Ci)：With client node CrAnd CiAll carried out the server node set of PDP checkings；
(8)I(Ci)：With CiThe set of the server node for interacting；
(9)DefineIt is a set, the number of element is in set | I
(Ci) |, what is represented respectively is and CiThe numbering of interactive server node；
(10)：Define aijIt is client node CiWith server node SjInteraction
Can be to T (C in resulti) accumulative trust value；
(11)：PjFor server node and utmost good faith/non-honest connect
Short range degree.
Technical scheme can be divided into two stages of server node credit rating and user node credit rating.It is of the invention a kind of
Cloud storage service credit assessment method, the method specific steps (including specific formula for calculation and algorithm) are as follows：
The 1.CSP storage service credit appraisal stages
Step 1：Calculate CiTo SjDirect trust value TD(Ci, Sj)
Calculating direct trust value should be by client node CiWith server node SjInteractive history obtain, due to client
Node CiWith server node SjMay be interactive multiple, so the result that will each time carry out PDP checkings (is commented { 0,1 } for being given
Valency) FPDP(Ci, Sj, k) it is multiplied by verification environment factor Ⅴ EF (C each timei, Sj, k), then it is polymerized and normalized.Such as
Fruit refusal provides PDP the results, i.e., { 0,1 } is evaluated, then this result FPDP(Ci, Sj, k) it is designated as 0, that is to say, that
When being only verified, client node CiTo server node SjDirect trust value just add up, checking not pass through or refuse
The result is provided, then direct trust value does not add up.
In this programme, we measure C using beta distributioniTo SjDirect trust value, beta distribution be binary system text
The posterior probability of part, its expectation describes the probability of result system event appearance after observation, so we are distributed with beta
Desired value as the direct trust value between node.We useRepresent beta point
Parameter alpha in clothij, use nij-αijTo represent the parameter beta in beta distributionij, the beta probability distribution carried out with such parameter
Expectation be to be expressed as client node CiTo server node SjDirect trust value.
Wherein B (αij, βij) it is beta function, its expression formula isSo client node
CiTo server node SjDirect trust value be：
Step 2：Calculate CiTo SjRecommendation trust TR(Ci, Sj)
Calculate client node CiTo server node SjRecommendation trust should by with server node SjInteracted its
He is client node Cr∈ID(Sj, Ci) to server node SjTrusting degree synthesis, so we are with CrTo SjIt is direct
Trust value is used as CrTo SjTrusting degree, then weight should be CiTo CrTrusting degree, and between client node mutually not
Communication, therefore its trusting degree can be by analyzing client node CrAnd CiTo commenting for their common server nodes for interacting
The similarity that valency carried out the result of PDP checkings is compared, if they carry out PDP checkings with these server nodes
Result is much like, then the trust value between them is just high, on the contrary just low (Things of a kind come together).Do so has one
If benefit is exactly a node belongs to malicious node set, and a node belongs to good will node set, then they are given
The similarity of result is just very low, and the trust value between them is just very low, and the two nodes are thus avoided to a certain extent
Between dishonest recommendation.Computing formula is as follows：
Step 3：Calculate CiTo SjRecommendation trust TR(Ci, Sj)
Direct trust value is polymerized with recommendation trust on the basis of step 1 and step 2, its weight is respectively α
And β.Under different network environments, the requirement to subjective evaluation has differences, and α and β also just can be according to corresponding environment
Condition and requirement are suitably adjusted.Computing formula is as follows：
T(Ci, Sj)=α TD(Ci, Sj)+β·TR(Ci, Sj)
Step 4：Calculation server node SjSystem trust value T (Sj)
Final calculation server node SjTrust value T (Sj)：Should be by all of client node Ci(i=1to m) is to Sj
Trust value T (Ci, Sj) be polymerized, it is C by its weight definitioniTrust value T (Ci), then divided byCarry out normalizing
Change is processed, and computing formula is as follows：
2. the user node credit rating stage of user behavior is based on
Step 5：Calculate user node CiSystem trust value T (Ci)
In the stage 1, we can calculate the trust value of all of server node.According to specific network condition,
One decision threshold T is setthAnd judged：If T (Sj)≥Tth, then it is assumed that SjIt is honest server node, if T
(Sj) ＜ Tth, then it is assumed that SjIt is dishonest server node.It is then determined that and CiThe set I of the server node for interacting
(Ci）, then judge CiWith I (Ci) in each element interactions history, in interaction only with honest server node
PDP interaction results be 1 and can be accumulated to letter with the behavior that the PDP interaction results of dishonest server node are 0
Appoint in value, other behaviors are accordingly to be regarded as dishonest conduct and can not be accumulated in trust value.Then we define server node
SjWith the degree of closeness P of utmost good faith/non-honest nodej, then aggregate-value P will be trustedjCarry out product weightings, it is possible to calculate
Go out user node CiSystem trust value T (Ci).Computing formula is as follows：
Step 6：Belief updating
Calculating more than, it will be seen that the trust value of server node depends on history mutual information and use
The prestige situation of family node, meanwhile, the trust value of user node depends on the prestige feelings of history mutual information and server node
Condition.So we will use the user node C not calculated also in step 4iSystem trust value T (Ci), still set
T (Ci) initial value Tdefault, so in the calculating process that first time carries out step 4, T (Ci)=Tdefault, then by step
Rapid 4,5 credit values for being calculated all of server node and user node.But because calculating once can not reach
The effect that one stable state is trusted, so in this stage, it would be desirable to the clothes of stable state are calculated by way of iterative calculation
The trust value of business device node and user node, the i.e. system trust value constantly to server node and user node is iterated meter
Calculate, its value is reached convergence.Server node belief updating algorithm and user node belief updating algorithm are as follows：
Algorithm 1 is used for the trust value that iteration updates server node, and calculation specifications are carried out for convenience, and we are by server
Trust value T (the S of node1), T (S2) ..., T (Sn) remember into vector TS={ T (S1), T (S2) ..., T (Sn)}T.Algorithm is specifically walked
Suddenly it is：
1. the trust value to user node assigns initial value Tdefault；
2. according to given initial value Tdefault, next round is calculated by step 4 and step 5
3. calculating constantly is iterated, until trust value reaches convergence, i.e.,
Algorithm 2 is used for the trust value that iteration updates user node, and calculation specifications are carried out for convenience, and we are by client's end segment
Trust value T (the C of point1), T (C2) ..., T (Cm) remember into vector TC={ T (C1), T (C2) ..., T (Cm)}T.Algorithm specific steps
For：
1. the trust value to user node assigns initial value Tdefault；
2. according to given initial value Tdefault, next round is calculated by step 4 and step 5
3. calculating constantly is iterated, until trust value reaches convergence, i.e.,
Claims (6)
1. a kind of cloud storage service credit assessment method, it is characterised in that：The method is comprised the following steps that：
Stage one：Server node credit rating：Including step 1~step 4, client node C is calculated respectivelyiTo server section
Point SjDirect trust value TD(Ci,Sj) and recommendation trust TR(Ci,Sj)；It is calculated by direct trust value and recommendation trust
CiTo SjFinal trust value T (Ci,Sj)；All of client node be polymerized again to SjTrust be worth to server node Sj's
System trust value T (Sj)；
Step 1：Calculate CiTo SjDirect trust value TD(Ci,Sj)：To carry out the result of data integrity validation each time as defeated
Enter, that is, { 0,1 } for being given is evaluated, 1 expression is verified, i.e., data are complete, and 0 represents that checking does not pass through, i.e., data are not complete
Whole, the desired value characteristic being distributed using beta completes CiTo SjDirect trust value measurement；
Step 2：Calculate CiTo SjRecommendation trust TR(Ci,Sj)：By with server node SjOther client's end segments for interacting
Point Cr∈ID(Sj,Ci) to server node SjThe comprehensive measurement of trusting degree calculate CiTo SjRecommendation trust：With Cr
To SjDirect trust value TD(Cr,Sj) as CrTo SjTrusting degree, trust weight is defined as CrAnd CiTo their common friendships
The server node mutually crossed carried out the similarity of the result of data integrity validation, and the data integrity evaluation result for being given is got over
Similar, the trusting degree between them is higher, with root mean square RMS (Cr,Ci) be identified；
Step 3：Calculate CiTo SjFinal trust value T (Ci,Sj)：The direct trust value and recommendation trust that step 1 and 2 are obtained
Carry out polymerization and be calculated CiTo SjFinal trust value；
Step 4：Calculation server node SjSystem trust value T (Sj)：By all of client node CiTo SjTrust value T
(Ci,Sj) be polymerized, its weight definition is CiTrust value T (Ci), finally give all SjSystem trust value；
Wherein, i=1,2 ..., m；J=1,2 ..., n；
Stage two:User node credit rating：The credit value of the server node calculated according to the first stage, sets one
Decision threshold TthTo judge SjWhether it is honest server node；Then the behavior according to user node is tired out to its prestige
Count, accumulative rule is：Integrity verification result is carried out with honest server node to enter for 1 and with dishonest server node
Row integrity verification result is accordingly to be regarded as dishonest behavior for 0, and interbehavior trust value adds up；Other are accordingly to be regarded as dishonest conduct, hand over
Mutual Behavior trustworthiness value does not add up, while being iterated renewal to the trust value of server node and user node, reaches steady
State；
Step 5：Calculate user node CiSystem trust value T (Ci)：Determine first and CiThe set of the server node for interacting
I(Ci), then judge CiWith I (Ci) in each element interactions history, trust accumulative rule according to interaction is carried out to trust value
It is accumulative, then the degree of closeness P that aggregate-value and server node and utmost good faith/non-honest will be trustedjProduct weightings are carried out, is just counted
Calculation draws user node CiSystem trust value T (Ci)；
Step 6：Belief updating：In step 4, the user node C not calculated also is usediSystem trust value T (Ci),
Therefore it is provided with T (Ci) initial value Tdefault；In this step, it is necessary to the calculation procedure according to more than and the side by iterating to calculate
Formula is calculated the server node of stable state and the trust value of user node, i.e., constantly to server node and user node
System trust value is iterated calculating, its value is reached convergence.
2. a kind of cloud storage service credit assessment method according to claim 1, it is characterised in that：It is described in step 1
" calculate CiTo SjDirect trust value TD(Ci,Sj) ", its calculating process is as follows：
Wherein nijIt is client node CiWith server node SjCarry out the total degree of integrity verification；FPDP(Ci,Sj, k) it is client
Node CiWith server node SjCarry out the result of kth time integrity verification；VEF(Ci,Sj, k) it is client node CiAnd service
Device node SjCarry out envirment factor during kth time integrity verification.
3. a kind of cloud storage service credit assessment method according to claim 2, it is characterised in that：It is described in step 2
" calculate CiTo SjRecommendation trust TR(Ci,Sj) ", its calculating process is as follows： Wherein ID (Sj,Ci) it is I (Sj) C is removed in setiThe set of node；RMS(Cr,Ci) it is root mean square,
For being expressed as CiTo CrTrusting degree, its computational methods is as follows：
Wherein SS (Cr,Ci) be with
Client node CrAnd CiAll carried out the server node set of integrity verification.
4. a kind of cloud storage service credit assessment method according to claim 1, it is characterised in that：It is described in step 3
" calculate CiTo SjFinal trust value T (Ci,Sj) ", its calculating process is as follows：T(Ci,Sj)=α TD(Ci,Sj)+β·TR
(Ci,Sj), wherein α and β is respectively the weight of direct trust value and recommendation trust.
5. a kind of cloud storage service credit assessment method according to claim 1, it is characterised in that：It is described in step 4
" calculation server node SjSystem trust value T (Sj) ", its calculating process is as follows：
6. a kind of cloud storage service credit assessment method according to claim 1, it is characterised in that：It is described in steps of 5
" calculate user node CiSystem trust value T (Ci) ", its calculating process is as follows：
Wherein I (Ci) be and CiThe set of the server node for interacting；I.e.It is a set,
The number of element is in set | I (Ci) |, what is represented respectively is and CiThe numbering of interactive server node；aijIt is client's end segment
Point CiWith server node SjCan be to T (C in interaction resultsi) accumulative trust value；PjFor server node and utmost good faith/
Non-honest degree of closeness.
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