CN112488721A - User-oriented credible verification method - Google Patents
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Abstract
The invention relates to a user-oriented credible verification method, and belongs to the technical field of computers. Aiming at potential threats possibly occurring in the using process of cloud rendering farms, the invention provides a cross validation method, namely two cloud rendering farms are used for applying for cloud resources to render, some same modeling data are submitted to the two cloud rendering farms, and then whether the cloud rendering farms are credible and whether some backdoor programs are operated or not is judged by comparing the rendering time, rendering result pictures and other information of the same data. Based on the cross validation method, the invention provides a cloud rendering farm trusted framework through which a user can validate rendering results in an active manner to deal with potential threats which may exist.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a user-oriented credible verification method in a cloud rendering farm.
Background
In recent years, the animation film and television industry is rapidly developed, and with the rise of three-dimensional and four-dimensional movies and the popularity of high-definition animations, the rendering as a core technology of the high-definition movies and animations also meets the challenge. The method has the advantages that dozens of hours or even days are consumed for rendering a single-frame image with ultrahigh definition and complex scene, in order to complete the rendering task of a client in time, movie and cartoon companies build internal rendering clusters of the companies, and the rendering task is allocated to rendering nodes in a parallelized manner by utilizing the natural parallelism between rendering frames, so that the time consumed by rendering is shortened.
Meanwhile, the development of cloud computing technology has attracted extensive attention in the industry and academia, and the most direct driving force is an economic factor. The cloud computing technology can enable a user to customize a constructed computing environment according to own requirements, and the user or an enterprise does not need to purchase expensive computing resources and only needs to connect to the Internet, so that the computing resources with large quantity and low price can be obtained. Cloud computing provides various types of services for users at multiple levels, and the services are mainly divided into: infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS). Currently, laaS is widely used, which pools physical resources through a virtualization technology, and a user acquires corresponding computing resources according to the needs of the user. By renting infrastructure in the cloud platform, a user can avoid a series of construction and maintenance expenses, which greatly reduces the cost of the user, and meanwhile, the cloud platform has the capability of dynamically allocating resources as required, namely, flexibly providing computing resources.
The Pay-as-You-Go (Pay as You Go) mode of cloud computing provides possibility for enterprises to build low-cost cheap rendering farms, the enterprises can build the farms without purchasing servers, the complex management work of the infrastructures is also handed to IaaS suppliers to be responsible, and meanwhile, the use cost is reduced to the maximum extent by the mode of distribution according to needs.
As a business model which is rapidly developing, with the increasing scale of a cloud computing system, a more open and more complex environment is brought, and meanwhile, the continuous expansion of the cloud computing in each business application field enables the operation cost of an enterprise to be greatly reduced and the resource management efficiency to be remarkably improved, but the security problem of the cloud environment faces a more serious challenge, so that the security of the cloud computing is always a hot point and a difficult point problem in academic and industrial fields, becomes a bottleneck which hinders further development, application and popularization of the cloud computing, and is the greatest concern of cloud users. The trust problem, namely whether the user can trust the cloud service provider, is a significant problem in the cloud computing application and popularization.
Since the use of cloud services means that all data of a user is stored at a cloud service provider, not on a machine which can be controlled by the user, the user has to bear risks of data leakage, data damage and data control loss, and various security accidents of a plurality of cloud computing companies cause people to have a trust crisis on the cloud services in recent years. Taking cloud rendering applications as an example, data security is a very interesting topic for users. The user generates a three-dimensional model through modeling software at a client, the modeling data is the crystallization of the labor of the user, and the continuous frame pictures or animations generated after the rendering relate to the copyright of movie and animation companies, and the data is very important to the user and has strong privacy. For a movie company, these modeling data are the core competitive embodiments. If the data stored in the cloud is stolen by competitors, competitors of the company can use the data for illegal use (such as piracy), and the development direction and innovation point of the company product can be known through modeling data, which not only relates to copyright problem, but also concerns the survival of the company. Therefore, how to select a trusted cloud platform is very important for a company or a common consumer, and how to establish trust between a cloud user and a cloud provider is a research topic.
In order to protect the communication security of a user and cloud resources, a method for encrypting a communication channel and data of the user is provided, aiming at the problem of how to establish trust between the user and a cloud service provider, a new trust management mode is used for processing trust events between the user and the cloud service provider by utilizing a domain-based trust model, and the damage of malicious users or the cloud service provider to trust is avoided. With the infrastructure of trusted cloud computing, a trust awareness based approach is used to verify and evaluate these security control policies so that users can be authorized to decide whether to trust a cloud service provider. Many approaches also arise in the context of rating trust in cloud resource providers. For example, a vector-based trust model is established that evaluates computing resources in a cloud environment by analyzing the impact of trust factors, history factors, time factors on trust values, and then trust values. Meanwhile, a trusted third party can be used for establishing a trusted channel between the user and the cloud resource to ensure data security.
With the popularization of cloud computing application, cloud rendering farms are used by more and more movie and television cartoon companies, manufacturers at home and abroad launch cloud rendering services to shorten rendering time and accelerate manufacturing processes for users, and higher requirements on security are provided for data-sensitive cloud rendering application. Typically, cloud-rendered farms will promise to protect a user's data security, they provide some measure of security from a system security perspective, but because the cloud resources are transparent to the user, the user lacks a way to verify this trust and security. That is, from the user's perspective, how can the user know whether the service provided is authentic? For example, a cloud rendering farm is typically charged by the time a user uses cloud resources, the cloud rendering farm may intentionally extend the user's resource usage time for multiple charges, or when the system lacks rendering resources, trick the user into saying that a job has been scheduled.
And if the modeling data submitted by the user is a wrong result in the rendering process, the reason is that the modeling file of the user is wrong or the data of the cloud rendering farm is damaged in the storage process. Due to lack of control over resources, users cannot protect their own rights and interests from being infringed at the technical level. Often users are very willing to trust cloud resources and cloud services, but if the users cannot take some measures to monitor these cloud services, the data security and trust of the users to the cloud services can face a great threat.
Disclosure of Invention
Technical problem to be solved
The technical problem to be solved by the invention is as follows: how to provide a method for actively verifying the credibility of cloud resources for a rendering user aiming at the problem that the rendering user cannot verify the reliability of rendering source data and result data and the promised security of a cloud resource provider.
(II) technical scheme
In order to solve the technical problem, the invention provides a user-oriented credibility verification method, which is a method for verifying reliability and safety of a cloud rendering farm by taking a user as a center, and comprises the following steps:
step 1, analyzing potential threats which may appear in the using process of a cloud rendering farm;
step 2, judging whether the cloud rendering farm is credible or not and whether some backdoor programs are operated or not in a cross validation mode;
and 3, designing a cloud rendering farm trusted framework based on the step 2, and enabling a user to respond to potential threats possibly existing by verifying a rendering result in an active mode through the framework.
Preferably, the potential threats include unreliable data, unreliable rendering results, and unreliable rendering costs.
Preferably, the potential threats include unreliable data, unreliable rendering results, and unreliable rendering costs.
Preferably, the potential threats include unreliable data, unreliable rendering results, and unreliable rendering costs.
Preferably, step 2 specifically uses two cloud rendering farms to apply for cloud resources for rendering, and submits some same modeling data to the two cloud rendering farms, and then determines whether the cloud rendering farms are trusted and whether some backdoor programs are run in a cross validation manner by comparing rendering time and rendering result picture information of the same data.
Preferably, in step 3, a trusted middleware is designed to schedule and manage jobs submitted by users, record rendering time and verify rendering results, the rendering jobs submitted by users include a series of mutually independent frames, and after receiving rendering requests of the users, the trusted middleware splits the frames in the jobs of the users and then distributes the frames to two cloud rendering farms; the trust of the cloud rendering farm is then verified in the rendered job submitted by the user based on the trusted middleware by some redundant frames, called spy frames.
Preferably, in step 3, based on the trusted middleware, in the rendering job submitted by the user, the verification process for verifying the trust of the cloud rendering farm by some redundant frames is as follows: the method comprises the steps that firstly, the trusted middleware selects a plurality of spy frames from a frame sequence submitted to one cloud rendering farm, then submits the spy frames to the other cloud rendering farm, and simultaneously selects the spy frames from the frame sequence of the other cloud rendering farm in the same mode.
Preferably, in the verification process, the verification basis is: and if the differences in the rendering time, the charge and the details of the frames of the spy frame are within a preset acceptable range, judging that the cloud rendering farm can be trusted, otherwise, judging that the cloud rendering farm is not trusted.
The invention also provides application of the method in the technical field of cloud computing.
The invention also provides an application of the method in the technical field of computers.
(III) advantageous effects
Aiming at potential threats possibly occurring in the using process of cloud rendering farms, the invention provides a cross validation method, namely two cloud rendering farms are used for applying for cloud resources to render, some same modeling data are submitted to the two cloud rendering farms, and then whether the cloud rendering farms are credible and whether some backdoor programs are operated or not is judged by comparing the rendering time, rendering result pictures and other information of the same data. Based on the cross validation method, the invention provides a cloud rendering farm trusted framework through which a user can validate rendering results in an active manner to deal with potential threats which may exist.
Drawings
FIG. 1 is a schematic diagram of a cloud-rendered farm trusted framework provided by the present invention;
FIG. 2 is a diagram of an example of the verification method of the present invention.
Detailed Description
In order to make the objects, contents, and advantages of the present invention clearer, the following detailed description of the embodiments of the present invention will be made in conjunction with the accompanying drawings and examples.
In order to reduce the risk of using the cloud rendering service by a user and effectively evaluate the cloud rendering service, a user-oriented credible verification method is provided.
The method is a method for verifying the reliability and the safety of cloud rendering farms by taking a user as a center, and provides an active way for verifying the credibility of the cloud resources for rendering users through cross verification of a plurality of cloud rendering farms. Firstly, analyzing the potential risk analysis of the cloud rendering farm, and then, introducing the proposed verification method in detail, wherein the specific steps are as follows:
step 1, carrying out potential threat analysis
The cloud resources are transparent to the user, so that the method is beneficial and disadvantageous to the user, the user can use the provided service and resources only by combining the needs without considering the details of the cloud, and the method has the disadvantages that the data submitted by the user is in the cloud instead of the local, and the user cannot monitor the data and the verification result by adopting a technical means. Generally, cloud resource providers guarantee the credibility of companies and ensure the reliability and credibility of own services for users, but some trust problems are caused. For example, if the cloud rendering farm is designed with some backdoor programs, only when the user uses the rendering service, the backdoor programs are restarted to perform some illegal operations, such as stealing or modifying the user's data. The transparency of the cloud platform makes these backdoor programs easy to hide and difficult to discover, and lacks some supervision mechanisms and verification mechanisms. Some potential threats are introduced next in conjunction with the features of the rendering application.
Threat 1: unreliable data.
The cloud rendering farm stores modeling data submitted by a user in a storage device at the cloud end, and if the data of the user is lost or damaged, the cloud rendering farm is likely to be attributed to data errors or damaged data uploaded by the user in the data uploading process. In addition, when rendering users use these data to render pictures, they are likely to attribute the reason to incomplete data or wrong data submitted by the users, rather than problems with their own storage devices, because once admitted, the reliability and trust of the cloud platform are greatly questioned, and many users are likely to be lost.
Threat 2: untrusted rendering results.
When the user receives the rendering completion information and obtains the rendering result picture, the user can hardly judge whether the rendering result of each frame is calculated by the rendering software of the version specified by the user. Generally, most of rendering software provided by a cloud rendering farm is commercial software, corresponding license files need to be purchased, even license files of different versions of the same software are paid separately, and for the cloud rendering farm with large-scale rendering resources, a part of large economic expenses are required. Assuming that the user needs the Maya 2013 version of the renderer, in order to save expenses, the cloud rendering farm uses a low-version and low-price Maya 2009 to render frame data submitted by the user, so that the user is deceived, and the user is charged for a high version.
Threat 3: untrusted rendering costs.
The use of cloud resources usually requires a certain fee, and the charging form is generally charged according to the use time and the machine type. Due to the transparency of the cloud platform to the user, it is likely that the cloud resource provider will intentionally extend the usage time of the user for multiple charges. Since the charging period is usually in hours, for example, the usage time of the user is 1 hour and 1 minute, and the charging period is also charged according to the time of 2 hours, the cloud resource provider can easily control the usage time of the user to achieve the purpose of multi-charging. In addition, some cloud resource providers are likely to delay scheduling of jobs of new users when a user peak causes resource shortage, but inform the users that the submitted jobs are scheduled, which is also a means for defrauding expenses.
Through the analysis on the potential threats, if some supervision measures are not taken, a cloud rendering farm has many bugs and potential safety hazards, once the backdoor programs are operated in a stealing mode, a user can hardly perceive the bugs, and meanwhile the user lacks a certain technical means to process and deal with the threats.
Step 2, judging whether the cloud rendering farm is credible or not and whether some backdoor programs are operated or not in a cross validation mode;
aiming at potential threats possibly occurring in the using process of cloud rendering farms, the invention provides a cross validation method, namely two cloud rendering farms are used for applying for cloud resources to render, some same modeling data are submitted to the two cloud rendering farms, and then the credibility of the cloud rendering farms and the running of certain backdoor programs are judged in a cross validation mode by comparing the rendering time, rendering result pictures and other information of the same data. Generally, there may be some potential threats to using a company's cloud-rendering farm to render a modeling file, and it is difficult for two or more companies' cloud resources to exist in a backdoor program at the same time and, if so, to run at the same time. For example, if both cloud service providers extend the service time of the user, the percentage of the extension may be different.
Step 3, designing a cloud rendering farm trusted framework based on the step 2, and enabling a user to verify a rendering result in an active mode through the framework to deal with potential threats possibly existing;
based on a cross validation method, the invention provides a cloud rendering farm trusted framework through which a user can validate rendering results in an active manner to cope with potential threats which may exist, as shown in fig. 1.
The invention designs a Trusted Middleware (TM) to schedule and manage the operation submitted by the user, record the rendering time and verify the rendering result. Generally, a rendering job submitted by a user includes a series of mutually independent frames, and after receiving a rendering request from the user, the trusted middleware splits the frames in the user job and then allocates the split frames to two cloud rendering farms, for example, if one rendering job submitted by the user includes 40 frames, the trusted middleware allocates 20 frames of tasks to each cloud rendering farm. For easier verification, the invention proposes the concept of "spy frames", i.e. verifying the trust of a cloud-rendering farm by some redundant frames. The verification process is as follows: the method comprises the steps that firstly, the trusted middleware selects a plurality of spy frames from a frame sequence submitted to one cloud rendering farm, then submits the spy frames to the other cloud rendering farm, and simultaneously selects the spy frames from the frame sequence of the other cloud rendering farm in the same mode. A cloud-rendering farm may be trusted if the spy frame's rendering time, charges, and details of the frame are not very different, otherwise the cloud-rendering farm may have performed some background or cheating means to spoil the user's interest.
Suppose a job J submitted by a useriThe trusted middleware receives the user's job, and divides the user's job into two groups of frame sequences: (1-7) and (8-14), which are referred to as regular frames. The frame numbered (1,3,5) of the first group of normal frame sequences is then selected as a spy frame and added to the second group of frame sequences, which now have the frame sequence number: (1,3,5,8-14), in the same way, the frame numbered (8,10,12) in the second set of conventional frame sequences is added to the first set of frame sequences numbered: (1-7,8,10,12). The first and second sets of frame sequences are then assigned to cloud rendering farms a and B for which the spy frame sequences and the regular frame sequences are transparent. After the cloud rendering farm completes rendering of the sequence of frames, the user may compare the spy frames (1,3,5) in the second group with the pairs in the first groupThe frames which are to be numbered are compared, the spy frames (8,10,12) in the first group are also compared with the frames which are correspondingly numbered in the second group, and if the difference of the rendering time is smaller than a set value and the effect of rendering pictures is not very different, the service provided by the cloud rendering farm can be determined to be credible.
Through the simple redundant rendering mode, a user can verify the credibility of the cloud rendering service at any time in an active mode, and therefore the risk of using the cloud rendering service is reduced.
It can be seen that the method achieves the purpose of trust verification by using cross-verification spy frames of a plurality of cloud rendering farms, and is realized by a cloud rendering farm trusted framework. Therefore, the risk of potential threats of the cloud rendering farm can be reduced, meanwhile, the user can effectively evaluate the cloud rendering service, and trust between the user and the cloud platform can be more actively verified.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A user-oriented credibility verification method is a method for verifying reliability and safety of a cloud rendering farm by taking a user as a center, and comprises the following steps:
step 1, analyzing potential threats which may appear in the using process of a cloud rendering farm;
step 2, judging whether the cloud rendering farm is credible or not and whether some backdoor programs are operated or not in a cross validation mode;
and 3, designing a cloud rendering farm trusted framework based on the step 2, and enabling a user to respond to potential threats possibly existing by verifying a rendering result in an active mode through the framework.
2. The method of claim 1, wherein the potential threats include unreliable data, untrusted rendering results, untrusted rendering costs.
3. The method of claim 2, wherein the potential threats include unreliable data, untrusted rendering results, untrusted rendering costs.
4. The method of claim 3, wherein the potential threats include unreliable data, untrusted rendering results, untrusted rendering costs.
5. The method as claimed in claim 4, wherein step 2 is to use two cloud rendering farms to request for cloud resources for rendering, submit the same modeling data to the two cloud rendering farms, and then determine whether the cloud rendering farms are trusted and run backdoor programs in a cross-validation manner by comparing rendering time and rendering result picture information of the same data.
6. The method of claim 5, wherein in step 3, a trusted middleware is designed to schedule and manage the jobs submitted by the users, record rendering time and verify rendering results, the rendering jobs submitted by the users comprise a series of mutually independent frames, and after receiving the rendering requests of the users, the trusted middleware splits the frames in the jobs of the users and then distributes the split frames to the two cloud rendering farms; the trust of the cloud rendering farm is then verified in the rendered job submitted by the user based on the trusted middleware by some redundant frames, called spy frames.
7. The method of claim 6, wherein in step 3, based on the trusted middleware, in the submitted rendering job of the user, the verification process of verifying the trust of the cloud rendering farm by some redundant frames is as follows: the method comprises the steps that firstly, the trusted middleware selects a plurality of spy frames from a frame sequence submitted to one cloud rendering farm, then submits the spy frames to the other cloud rendering farm, and simultaneously selects the spy frames from the frame sequence of the other cloud rendering farm in the same mode.
8. The method of claim 7, wherein in the authentication process, the authentication basis is: and if the differences in the rendering time, the charge and the details of the frames of the spy frame are within a preset acceptable range, judging that the cloud rendering farm can be trusted, otherwise, judging that the cloud rendering farm is not trusted.
9. Use of the method according to any one of claims 1 to 8 in the field of cloud computing technology.
10. Use of the method according to any one of claims 1 to 8 in the field of computer technology.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103905461A (en) * | 2014-04-14 | 2014-07-02 | 北京工业大学 | Cloud service behavior trustworthiness attestation method and system based on trusted third party |
| CN104202168A (en) * | 2014-09-19 | 2014-12-10 | 浪潮电子信息产业股份有限公司 | Cloud data integrity verification method based on trusted third party |
| CN104901959A (en) * | 2015-05-26 | 2015-09-09 | 浪潮电子信息产业股份有限公司 | Method and system for verifying credibility of computing pool |
| CN106790064A (en) * | 2016-12-20 | 2017-05-31 | 北京工业大学 | The method that both sides are communicated in credible root server cloud computing server model |
| CN111859467A (en) * | 2020-07-23 | 2020-10-30 | 中国工商银行股份有限公司 | Cloud data integrity auditing method and device based on SGX |
-
2020
- 2020-12-08 CN CN202011444471.3A patent/CN112488721A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103905461A (en) * | 2014-04-14 | 2014-07-02 | 北京工业大学 | Cloud service behavior trustworthiness attestation method and system based on trusted third party |
| CN104202168A (en) * | 2014-09-19 | 2014-12-10 | 浪潮电子信息产业股份有限公司 | Cloud data integrity verification method based on trusted third party |
| CN104901959A (en) * | 2015-05-26 | 2015-09-09 | 浪潮电子信息产业股份有限公司 | Method and system for verifying credibility of computing pool |
| CN106790064A (en) * | 2016-12-20 | 2017-05-31 | 北京工业大学 | The method that both sides are communicated in credible root server cloud computing server model |
| CN111859467A (en) * | 2020-07-23 | 2020-10-30 | 中国工商银行股份有限公司 | Cloud data integrity auditing method and device based on SGX |
Non-Patent Citations (1)
| Title |
|---|
| 李谦 等: "A Novel Trustworthy Framework for Cloud based Rendering Application", 《2016 IEEE TRUSTCOM-BIGDATASE-ISPA》 * |
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