CN114282783A - Multi-factor data reliability assessment method in fog calculation - Google Patents
Multi-factor data reliability assessment method in fog calculation Download PDFInfo
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Abstract
The invention relates to a multi-factor data reliability assessment method in fog calculation, and belongs to the technical field of mobile communication. Data sharing enables users to obtain important information nearby in time. However, users do not fully trust each other during data sharing due to their mobility and variability. Devices may propagate error messages due to sensor failure, virus infection, or even selfish. To reduce the impact of these malicious messages on other users, the propagation of spurious messages must be suppressed. The invention designs a method for quantifying the authenticity of data, so that a data requester can obtain more reliable data in the data sharing process. And the fog node carries out pre-judgment on the authenticity of the data through Bayesian model inference to obtain the reliability value of the data. The requester synthesizes the experience-based trust value and the history interaction of the provider on the basis of the fog node pre-judgment result to obtain the satisfaction degree of the data, so that the reliability evaluation of the data is more comprehensive.
Description
Technical Field
The invention belongs to the technical field of mobile communication, and relates to a multi-factor data reliability evaluation method in fog calculation.
Background
At present, "fog computing" is an emerging distributed computing paradigm that extends computing, communication, and caching to the edge of the network, with inherent characteristics of low latency, mobility, and wireless access capability. Fog computing provides a platform for data sharing between mobile devices. The data sharing enables users to obtain important information nearby in time, and great convenience is provided for life of the users. However, due to the mobility and variability of devices, devices do not fully trust each other. When malicious users exist in the network, the malicious users can intentionally spread false data, and the judgment of the data by other users is disturbed. Therefore, how to effectively evaluate the credibility of the data is an important issue in data sharing.
Based on the above problems, the present invention has devised a method to quantify the authenticity of data. Firstly, the fog node carries out pre-judgment on the authenticity of the data through Bayesian inference to obtain the reliability value of the data. Secondly, the requester integrates the experience-based trust value of the provider and historical interaction to obtain the satisfaction degree of the provided data on the basis of the fog node pre-judgment result, so that the reliability evaluation of the data is more comprehensive. Thereby finding the best data for the data requestor.
Disclosure of Invention
In view of the above, the present invention provides a method for evaluating reliability of multi-factor data in fog calculation.
In order to achieve the purpose, the invention provides the following technical scheme:
a multi-factor data reliability assessment method in fog calculation comprises the following steps:
s1: a two-tier data sharing model based on a fog network;
s2: a data pre-judging method based on Bayesian inference;
s3: a trust value updating method based on user historical interaction and experience;
s4: and a data reliability evaluation scheme based on an entropy weight method.
Further, in step S1, a data sharing model is established.
Further, in step S2, the data has uncertainty, and in order to prevent users from sharing false information, the fog node will pre-determine the content uploaded by the data provider, and perform pre-determination on a certain data elThe credibility of (c) is defined as follows:
wherein the content of the first and second substances,
related event e expressed as uploaded by provider jlThe size of the confidence level of the data of (c),
represented as the distance between user j and the location of the event occurrence,
denoted as time t at which the content of the event is known for user jjTime difference from event occurrence time t
b is the lower limit of the reliability of the data, α and β control the rate of change of the reliability, and α + β is 1. The shorter the distance between the user j and the event occurrence, the earlier the event occurrence time is known, and the more trustworthy the data is.
The fog node collects data sets within its communication range, and data e can be obtained using equation (1) abovelConfidence level set Cl，
Base on obtaining credibility setOn the basis, the Bayesian model is used for reasoning and calculating the event elAggregate reliability P of (1):
wherein P (e/C) represents the aggregate credibility of event e,
complementary events denoted e, P (c)j/e)＝cj；
P (e) is expressed as the prior probability of event e. P (e/C) belongs to [0,1 ]]. Once P (e/C) exceeds a preset threshold Thr, the data related to the event is considered to be authentic by the fog node; if P (e/C) does not exceed the set threshold, the data is considered unreliable. The user who uploads the unreliable data will be kicked out of the sharing list and no longer participate in the round of data sharing.
Further, in step S3, the user should consider not only the credibility of the data itself but also the behavior of the data provider in data sharing when requesting the data. If the provider is malicious, the provider has the behaviors of missing transmission, forging data and the like, and provides false data service. Therefore, the data requester should also judge the authenticity of the data by combining the past behavior of the user and the satisfaction degree of the service provided by the provider before on the basis of the result of the data pre-judgment of the fog node.
The experience-based trust value is a trust value which is updated by using the past behaviors of the user, so that the authenticity of data is indirectly judged, and the value is continuously accumulated along with the time. After the data sharing is completed, the data requester can score T for the provider based on the data qualityi,j,Ti,jE (-1, 1). Fog node averaging scoring of requesters
L is the number of requestors interacting with data provider j this time. By sjRepresenting user j radicalsFrom the empirical confidence measure, sjE (-1,1) in
On the basis of (1) to sjAnd (6) updating.
When a requester makes a share request to a provider, the provider's satisfaction with the previously provided service is measured, which is related to the historical interaction between the two. p is a radical ofijIndicating the level of satisfaction, p, of the current serviceij∈[0,1]. The historical interactive cumulative value is:
wherein p isij(ti) Represents a level of satisfaction with the currently provided data;
cumulative, constantly updated, indicating satisfaction
The instant of requesting a service is denoted tn＝tN＞…＞t2＞t1(ii) a N is the number of times data is requested. A higher N means that the requester has more prior knowledge of the provider, so that the provider is judged more accurately, and the acquired data is more complete and accurate.
Further, in step S4, the evaluation weights of the three scoring indexes, i.e., the data reliability, the experience-based confidence value, and the historical interaction score, are evaluated by using the entropy weight method, so as to comprehensively evaluate the reliability of the data provided by the user. In the select data provider phase, requestor RiBased on the three indexes, related providers are evaluated, and an n multiplied by m provider scoring matrix W is establishedn×mWhere n is the number of data providers and m is 3. First to obtain a normalized evaluation matrix
And carrying out normalization processing on the matrix elements by adopting a normalization method. Secondly, the weight of each evaluation index m of the provider and the information entropy H of the evaluation index m are calculatedm. And (3) carrying out normalization processing on the information entropy of the rating index m, finally obtaining the current satisfaction score of the requester i to each provider j about the data l, and then initiating a data sharing request to the data provider with the highest score by the data requester to obtain the most reliable data.
The invention has the beneficial effects that: according to the characteristics of the provided network scene, the fog node pre-judges the data abstract information uploaded by the data provider by means of Bayesian inference, and the data requester combines experience-based trust values of the provider and historical interaction to obtain the satisfaction degree of the provided data on the basis of the pre-judgment result.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a data sharing model based on fog calculations.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.
FIG. 1 depicts a data sharing network model consisting of users and fog nodes. The model mainly comprises users and interconnected fog nodes.
The user: advanced communication equipment is equipped, wireless communication capability is provided, local data is collected from the sensing equipment, summary information (data size, time, address and the like) of the data is uploaded to a fog node with the closest communication distance, and the data is shared with a data requester. User impersonation data provider for collecting and sharing data, denoted as
The user needing the data acts as a data requester, denoted as
Users play different roles according to their different needs.
Fog node: the method has certain storage and calculation capabilities, collects the data summary information set uploaded by the users in the communication range, evaluates the credibility of the data set, and obtains a pre-judgment result for reference of a data requester.
During the data sharing process, users may propagate error messages due to sensor failures, virus infections, and even selfish reasons. To reduce the impact of these malicious messages on other users, the propagation of spurious messages must be suppressed. To this end, the present invention contemplates a method to quantify the authenticity of the data. Firstly, the fog node carries out pre-judgment on the data to obtain the reliability value of the data. Secondly, the requester integrates the experience-based trust value of the provider and historical interaction to obtain the satisfaction degree of the provided data on the basis of the mist node pre-judgment result, so that the authenticity evaluation of the data is more comprehensive.
1) Pre-determination of data
The pre-judgment of the data is equivalent to the credibility evaluation of the data, and the step is carried out on the fog node. First, the fog node groups the data uploaded by all users into E1,E2,…,El,…In which ElIndicating a related event elData sets of (2), such as events: "snow is accumulated on a certain road section". However, the data in the same group does not have the same credibility, and the credibility of the data uploaded by the user j is defined as:
wherein the content of the first and second substances,
related event e expressed as uploaded by provider jlThe size of the confidence level of the data of (c),
represented as the distance between user j and the location of the event occurrence,
denoted as time t at which the content of the event is known for user jjTime difference from event occurrence time t
b is the lower limit of the reliability of the data, α and β control the rate of change of the reliability, and α + β is 1. The shorter the distance between the user j and the event occurrence, the earlier the event occurrence time is known, and the more trustworthy the data is.
The fog node collects the data set in the communication range of the fog node, and the data e can be obtained by using the formula (4)lConfidence level set Cl，
On the basis of obtaining a credibility set, an event e is calculated by inference through a Bayesian modellAggregate reliability P of (1):
wherein P (e/C) represents the aggregate credibility of event e,
complementary events denoted e, P (c)j/e)＝cj；
P (e) is expressed as the prior probability of event e. P (e/C) belongs to [0,1 ]]. Once P (e/C) exceeds a preset threshold Thr, the data related to the event is considered to be authentic by the fog node; if P (e/C) does not exceed the set threshold, the data is considered unreliable. The user who uploads the unreliable data will be kicked out of the sharing list and no longer participate in the round of data sharing.
2) User experience-based trust value
The experience-based trust value is obtained by updating the trust value of the user by using the past behavior of the user to indirectly judge the authenticity of data, and the value is continuously accumulated along with the timeAnd (4) accumulating. After the data sharing is completed, the data requester can score T for the provider based on the data qualityi,j,Ti,jE (-1, 1). Fog node averaging scoring of requesters
L is the number of requestors interacting with data provider j this time. By sjRepresenting the experience-based confidence level, s, of user jjE (-1,1), the update criterion is as follows:
if it is
Degree of trust sjThe increase is as follows:
if it is
Degree of trust sjThe reduction is:
wherein s isjRepresenting an empirically based trust value, s 'at the current time'kRepresenting an updated trust value. Eta is a positive increment factor 0 < eta < 1; mu is a negative attenuation factor-1 < mu < 0. If the user has cheating behavior, the trust is easily destroyed and the trust is difficult to establish. Lambda represents a forgetting factor, lambda is more than 0 and less than 1, t represents the time difference between the interaction time of the current data provider and the data requester and the previous interaction time, the behavior of the user may change after a period of time, and lambda is discounted for the previous trust value in order to make the accumulated trust value of the previous behavior have less influence on the current timetOr (lambda)-t) The speed at which experience-based confidence increases or decreases is slowed.
3) Historical interaction of users
When a requester makes a share request to a provider, the provider's satisfaction with the previously provided service is measured, which is related to the historical interaction between the two. p is a radical ofijIndicating the level of satisfaction, p, of the current serviceij∈[0,1]. The historical interactive cumulative value is:
wherein p isij(ti) Represents a level of satisfaction with the currently provided data;
cumulative, constantly updated, indicating satisfaction
The instant of requesting a service is denoted tn＝tN＞…＞t2＞t1(ii) a N is the number of times data is requested. A higher N means that the requester has more prior knowledge of the provider, so that the provider is judged more accurately, and the acquired data is more complete and accurate.
In the select data provider phase, requestor RiBased on the three indexes, related providers are evaluated, and an n × m provider scoring matrix is established as follows:
wherein n is the number of data providers, and n is less than or equal to j; c. CnRepresents QnThe provided data of (1) a credibility score; snRepresentation provider QnAn experience-based trust value; h isnRepresents a requestor RiFor provider QnThe historical interaction score of.
After the scoring matrix W is obtained, the scoring weights of three scoring indexes of data reliability, experience-based trust value and historical interaction score are evaluated by adopting an entropy weight method, so that the evaluation indexes are usedThe data provided by the user is evaluated. First to obtain a normalized evaluation matrix
Normalization processing is carried out on the matrix elements by adopting a normalization method:
calculating the weight of each evaluation index m of the provider:
information entropy H of evaluation index mmComprises the following steps:
and (3) carrying out normalization processing on the information entropy of the evaluation index m:
get the current data reliability score of requester i for each provider j on data l:
the data requester obtains a reliability score G of the datajAnd then sending a data sharing request to the data provider with the highest correlation score so as to obtain credible data, and preventing the propagation of false messages.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (5)
1. A multi-factor data reliability assessment method in fog calculation is characterized in that: the method comprises the following steps:
s1: establishing a two-layer data sharing model based on a fog network;
s2: a data pre-judging method based on Bayesian inference;
s3: updating a trust value based on historical interaction and experience of the user;
s4: and evaluating the reliability of the data based on an entropy weight method.
2. The method of claim 1, wherein the method comprises the following steps: in said S1, establishing a data sharing model; the system comprises a user and a fog node connected with each other; the user is provided with advanced communication equipment with wireless communication capability; the user collects local data from the sensing equipment, uploads the data abstract to the fog node with the closest communication distance, and shares the content with the data requester; the fog node has certain storage and calculation capacity, collects data uploaded by users in a communication range of the fog node, and pre-judges the credibility of the data; wherein, the data summary comprises data size, time and address.
3. The method of claim 2, wherein the method comprises the following steps: in the step S2, the data has uncertainty, and in order to prevent the user from spreading false information, the fog node performs pre-judgment on the data uploaded by the user in the coverage area; the credibility of a certain data l is defined as
The credibility is related to the distance and timeliness of the occurrence of the content; the fog node collects data sets within its communication range and corresponding credibility set COn the basis of obtaining the credibility set, the aggregate credibility P of the data l is calculated by Bayesian inference, and when P is larger than a threshold Thr, the data is considered to be credible.
4. The method of claim 3, wherein the method comprises the following steps: in S3, the user requests the data not only considering the credibility of the data itself, but also considering the behavior of the data provider in data sharing; if the provider is malicious, the conditions of missing transmission and counterfeiting data exist, and false data service is provided; the requester judges the authenticity of the data by combining the past behavior of the user and the satisfaction degree of the service provided by the provider before on the basis of the data pre-judgment result of the fog node; when a requester sends a sharing request to a provider, the historical interaction of a user measures the satisfaction degree of the service provided by the provider before, and the satisfaction degree is related to the historical interaction between the requester and the provider; the experience-based trust value is obtained by updating the trust value of the user by using the past behavior of the user, so that the authenticity of the data is indirectly judged, and the trust value is continuously accumulated along with the time.
5. The method of claim 4, wherein the method comprises the following steps: in the S4, in the stage of selecting data provider, the requester RiEvaluating related providers based on three indexes of pre-judgment of data, historical interaction of users and experience-based trust values of the users, and establishing an n multiplied by m provider scoring matrix as W; and after the scoring matrix W is obtained, evaluating the scoring weights of the three scoring indexes of the data by adopting an entropy weight method, and comprehensively evaluating the data provided by the user to finally obtain a satisfaction value of the data.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115426661A (en) * | 2022-09-06 | 2022-12-02 | 华中科技大学 | Credible coverage reliability assessment method for Internet of things based on trust management |
| CN116052832A (en) * | 2023-04-03 | 2023-05-02 | 青岛市妇女儿童医院（青岛市妇幼保健院、青岛市残疾儿童医疗康复中心、青岛市新生儿疾病筛查中心） | Tamper-proof transmission method based on medical information |
| CN116806038A (en) * | 2023-08-18 | 2023-09-26 | 上海临滴科技有限公司 | Decentralizing computer data sharing method and device |
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2021
- 2021-12-09 CN CN202111499031.2A patent/CN114282783A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115426661A (en) * | 2022-09-06 | 2022-12-02 | 华中科技大学 | Credible coverage reliability assessment method for Internet of things based on trust management |
| CN116052832A (en) * | 2023-04-03 | 2023-05-02 | 青岛市妇女儿童医院（青岛市妇幼保健院、青岛市残疾儿童医疗康复中心、青岛市新生儿疾病筛查中心） | Tamper-proof transmission method based on medical information |
| CN116806038A (en) * | 2023-08-18 | 2023-09-26 | 上海临滴科技有限公司 | Decentralizing computer data sharing method and device |
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