Service-Centric Dynamic Trust AnalysisBased Access Restriction and Blockchain Public Auditing for Efficient Collaborative System in Cloud
Environment
VelMurugeshKumar. N1, R. Nedunchelian2
1Research Scholar, Department of Computer Science and Engineering, Saveetha School of Engineering,Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu, India
2Professor, Department of Electronics and Communication Engineering,Karpaga Vinayaga College of engineering and technology, Madhuranthagam, Chengalpattu District, Tamilnadu, India
Abstract:
The cloud environment becomes more supportive for collaborative systems and has been used by several organizations in maintaining and access the data. The cloud environment introduces different services in different levels, which have been accessed by different users of the collaborative environment. However, restricting malformed access and public auditing becomes a key issue in a collaborative environment. To provide such operational and data transparency and consistency, different approaches are discussed in the literature. The methods perform public auditing at feature level and another scenario. Still, access restriction and public auditing-related parameters affect the entire performance in the cloud. To overcome these issues, a service-centric dynamic trust analysis access restriction and blockchain public auditing (DTAR-BCPA) model is presented in this article. The method initially receives the service request and performs service- centric feature trace trust analysis (SCFTTA) to perform the accessrestriction. The method estimates Trusted Access Score (TSA) and Feature Trust Score for specific service requested according to feature grants of the user according to the taxonomy and the traces of access. According to these two, the method estimates the trusted access score towards access restriction. Second, the method performs public auditing with blockchain technology. The method block-level trust score is measured based on the approval of different users. The method improves the performance of public auditing and access restriction towards performance improvement of a collaborative environment.
Index Terms:
Cloud Systems, Collaborative Environment, Access Restriction, Trust Analysis, SCFTTA, Public Auditing, TSA.
1. Introduction
Modern organizations maintain the number of data of huge volume in different locations of their units. As the volume of data becomes higher at each fraction, which challenges the organization in maintaining the data in their dedicated data servers due to the cost and space complexity. To handle this, the organizations move towards a cloud environment which provides several services in different layers and level of the environment. By using the services provided, the user can access and modify the data to perform their routine job. This makes it feasible for the users of a collaborative environment to work together.
The collaborative environments are meant for the users or employees of any organization to work together on a shared resource. For example, consider a design process of an automobile manufacturing unit, where the employees of the organization located in various geographic units work on the same design. Whatever changes made by a unit or employee should be reflected on the resource as well reflect on the copy of others. The collaborative environment performs this task by maintaining a shared resource.
In any collaborative environment, the organization has the responsibility in maintaining the privacy of the user data as well as it has the responsibility in providing exact and correct data to all the users. The user always looks for the correct and meaningful data for their job. As the data has been accessed by different users in the collaborative environment, the cloud should provide the exact data or correct data to all the users. Towards this, public auditing is performed. Public auditing is the process of providing correct data to the user which has been enforced at the update process. Public auditing is performed in different ways either by feature level which enforces public auditing in feature level as whatever the update made by the user has been updated to the copy of others. Similarly, public auditing is performed in block-level which performs public auditing at each block.
On the other side, the performance of a collaborative environment is depending on how the security has been enforced. If the data present in a collaborative environment is accessed by malformed users, then they would perform different threats to damage the data which in turn would affect the performance of the entire system. This is where access restriction comes to play, which would restrict the malformed access. The access restriction has been enforced in different ways. For example, it has been performed according to the feature and profile-based approaches.
Also, it has been performed on several levels towards access restriction. Due to the demerits, Service-Centric Dynamic Trust Analysis Based Access Restriction and Blockchain Public Auditing approach is presented in this paper. The blockchain is the technology recently used towards restricting access as well as public auditing. In this approach, the method maintains the data in form of blockchain and metadata available in the cloud system. By enforcing and maintaining the data in the blockchain, the public auditing and access restriction can be enforced greatly.
2. Related Works
Various methodologieswere discussed towards access restriction and public auditing in a collaborative environment.
Simulation tools for the cloud environment were described in[1]. The author describes that the GDC tool was suitable for simulatingapplications with data centers. The cloud sim has been identified as a tool to manipulate network operations where the hardware-related problems are supported with MDC. In [2], the problem of scheduling various services and applications in the cloud has been considered. The author performed a detailed study on how energy and load balancing can be performed. However, the author identified different simulators that support performance analysis. Similarly, a detailed review of various technologies that support cloud services based on user needs was discussed in [3].
In [4] and [5], authors presented the importance of implementation of cloud service for various applications such as agriculture, medicine, yield prediction data storage, and so on. A detailed study on the performance analysis of security-related quality of service(QoS) was proposed in [6]. The analysis is performed due to the infeasibility of analyzing the performance using hardware as they cost higher. The analysis is performed through different simulation tools. In [7], the author presents a prediction model that measures the performance of different cloud resources. The model has been evaluated the scaling utility of computing resources. The
behaviors of the resources and systems have been captured and used to measure the performance of unknown metrics.
In [8], the author presents a comparative study on the performance of simulation tools like Opnenebula and Openstack. The analysis is performed according to the architecture, security, and provenance. Finally, the author provides different recommendations for the deployments based on the demands of users.In [10], the author presented an Open cloud Testbed for reliable network services with an exclusively adapted 4 data centers where each has 30 nodes. The data centers are located in Chicago, San Diego, and Baltimore. The testbed is widespread geographically which is connected with a 10Gb/s network that supports high-performance requirements.
In [11], a testbed named Open Cirrus is presented which provides data centers being distributed to support research and open-source service stacks in the cloud. Similarly, a testbed on the same provides both physical and virtual machines. The services are distributed globally to perform monitoring and job submission. In [14], the author presented a flexible infrastructure supportive simulator for a cloud environment named iCanCloud. The tool provides higher usability, flexibility, and more scalable. It also provides a user interface to perform various operations.
Software-hardware-based Cloud simulators andclassifierswere proposed in [15], their features of each simulator class have been analyzed and discussed. In [16], the author describes the features of the Cloud Sim simulator and compares its variants like green cloud, cloud analyst, EMUSIM, and MDCSIM. The author presents a comparative study on support towards platform services, network services, and language services.
In [17], the author presented a survey on cloud simulators according to mathematical models and different testbeds are used to measure their performance in various factors. The survey is performed to identify the suitable modeling type for the evaluation of different cloud strategies.
In [18], the author presents an open-source framework to support cloud functionality named Eucalyptus to support control on the virtual machines. It is capable of controlling the virtual machines globally and the author outlined the architecture to support portability and simpler which can be deployed even in an academic environment.
In [19], the author presented a resource allocation strategy that has been simulated over CloudSim. As the simulator provides API to control the systems and behavior of the resources of cloud-like virtual machines. It supports the extension of any resource provisioning algorithm by modifying the existing ones. In [20], the author simulated the infrastructure services and how they can be controlled through the Cloudsim simulator. A framework developed by the authors allowed modeling the self-contained platforms to support scheduling and resource allocation policies.
In [21], the problem of environmental monitoring has been performed through cloud computing.
The method developed an infrastructure to maintain the earth observation data. They designed a testbed that allows on-demand data discovery services which can be accessed by different scientists and researchers. The framework allows the integration of multiple source earth observation data obtained from different geographic locations and enables coordinative resource computing strategies. In [22], a platform named HiTempo has been designed which supports the
analysis of time-series data. The platform supports the analysis of satellite images and analyses various algorithms. A model-based algorithm like the Kalman filter has achieved a higher detection rate. Similarly in [23], the multi-source remote sensing data have been integrated to identify the changing conditions of land cover. The land cover maps have been used to perform the changing conditions.
In [24], the author a software architecture to support network-based applications. The author presents a detailed survey of various architectures that enable network services. Finally, a REST (Representational State Transfer) is presented and describes the guidelines to use through the modern web. Resource-oriented cloud applications were created by the author in [25] for solving the problems of previous models.
In [26], the author presented an updated version of Multi-Verse Optimizer to achieve higher task planning in the cloud. The author compared their proposed work with the existing algorithms and achieved minimized makespan of time. In [27], the author proposed an advanced cloud computing environment for library management systems based on the cloud. They have tested the performance of their system with various librarians, universities and proved that their design was secured and reliable. In [28], the author presented a new complex system simulation method over the existing methods. This method provides a cloud environment service to perform simulations over the data.
Novel methodology related to the cloud was proposed in this study to increase the performance of cloud storage and security.
3. Service-Centric Dynamic Trust Analysis Access Restriction and Blockchain Public Auditing (DTAR-BCPA) model
The proposed method receives the user request and identifies the service being requested.
According to the service requested, the method applies service-centric feature trace trust analysis (SCFTTA) to perform the access restriction. The SCFTTA approach estimates trusted access score (TAS) for specific services requested according to feature grants of the user according to the taxonomy and the traces of access. According to these two, the method estimates the trusted access score towards access restriction. Second, the method performs public auditing with blockchain technology. The method block-level trust score is measured based on the approval of different users. The detailed approach is presented in this section.
Figure 1: Architecture of Proposed DTAR-BCPA Model
The architecture of the proposed DTAR-BCPA model has been presented in Figure 1 and shows various functional components incorporated. Also, the working components are detailed in this section sharply.
3.1 Service Handling
The proposed method receives the user request and identifies the service being requested.
Initially, using the service taxonomy, the user request has been validated for the possession of access for the user. If the user has access to the service then the method performs SCFTTA (Service centric feature trust and trace analysis). According to the result of SCFTTA, the method allows or denies the request. If the user does not have access to the service, then the request is denied. Similarly, when the user clears the trust check, the method performs Blockchain public auditing to enforce legitimacy. The BCPA approach enforces data encryption and validates the correctness of the data. If the user clears the BCPA test, then the data has been updated successfully. The result of service handling has been given to the user.
Algorithm:
Given: Service Taxonomy ST, Service Request Se_Req.
Obtain: Null Start
Read Service Taxonomy ST and Service Request Se_Req
Identify the user Uid = 𝑈𝑠𝑒𝑟𝐼𝑑 ∈ 𝑆𝑟_𝑅𝑒𝑞 -- (1)
Identify the service requested Sid = 𝑆𝑒𝑟𝑣𝑖𝑐𝑒𝐼𝑑 ∈ 𝑆𝑟_𝑅𝑒𝑞 -- (2) Compute Trust Weight Tw = perform SCFTTA Analysis.
Compute Block Level Trust Score BLTS = Perform BCPA.
If TW>Th and BLTS >BTh then
Allow access and perform access.
Else
Deny access End
Terminate
The proposed service-centric dynamic trust analysis request-response model has been presented in the above pseudo-code. According to that the method identifies the user id and estimates TSA and BLTS. Based on these values, service handling is performed.
3.2 Trusted Access Score Estimation:
The trusted access score representsuser access authentication. The service allows multiple access for multi-user service.sometimes, the trust of service access is justified according to the behavior of how he accesses the service. If the user accesses the service and breaks the service access on the fly then it would damage the service data as well as its performance. So, by counting the number of service access and number finished the service access, the value of TAS (Trusted Access Score ) can be measured. Also, it has been measured by counting the number of levels of service he has completed and the total number of levels it has.
Consider the service claimed is S, and the user trace is maintained as UT, then the value of TAS is measured as follows:
Compute the number of service access NA = ∑size(UT)i=1 UT(i). Service = S -- (3)
The above equation (3) finds the number of times the service has been accessed by the user. Now, the number of times the service access finished as using equation (4).
Compute the Number of times completed NTC.
NTC = ∑size(UT)i=1 UT(i). Service = S&& 𝑈𝑇(i). state = Complete -- (4) The service would have several levels and it has been identified as follows:
Compute several levels the service has as Nl.
Nl = ∑size(ST)i=1 ST(i). service == S && 𝑆𝑇(i). levels -- (5) Compute Average no of levels completed Anlc.
Anlc = ∑𝑠𝑖𝑧𝑒(𝑈𝑇)𝑖=1 𝑈𝑇(𝑖).𝑠𝑒𝑟𝑣𝑖𝑐𝑒==𝑆 && 𝑈𝑇(𝑖).𝑙𝑒𝑣𝑒𝑙
𝑠𝑖𝑧𝑒(𝑈𝑇) -- (6)
Using all these values, the value of the Trusted access score has been measured as follows:
Compute TAS = 𝑁𝑇𝐶
𝑁𝐴 ×𝐴𝑛𝑙𝑐
𝑁𝑙 -- (7)
Calculated TAS was implemented in SCFTTA analysis for user authorization.
3.3 Feature Trust Score Estimation
The feature trust score is the measure that represents the trust of service access according to the service taxonomy. The service taxonomy contains the protocols of the service as well as the set of features to which it has access to complete the service. Similarly, the taxonomy has user profiles that contain details of features to which the user has access. Using both of them, the method computes the value of the feature trust score (FTS). It has been measured as follows:
First, compute the number of features the service needs to access as follows:
NFNA = ∑size(Taxonomy)Taxonomy(i) → Sreq.
i=1 -- (8)
Second compute the number of features to which the user has access as follows:
NFA = ∑size(Taxonomy)Taxonomy(i) → Sreq&& 𝑇𝑎𝑥𝑜𝑛𝑜𝑚𝑦(𝑖) ∈ 𝑈𝑠𝑒𝑟
i=1 -- (9)
Compute the value of FTS = 𝑁𝐹𝐴
⁄𝑁𝐹𝑁𝐴 -- (10)
The estimated value of FTS was used to perform authorization.
3.4 Service-Centric Feature Trace Trust Analysis (SCFTTA)
The SCFTTA approach read the traces of user access. Using the user access trace the method computes the value of trusted access score (TSA) which is calculated based on the number of service access and service completion. Also, the method computes the feature trusted score (FTS) according to the service taxonomy which specifies the number of features the service needs to be accessed and several features to which the user has access. Using both of them, the method computes the value of trust weight, based on which the access restriction is performed.
Algorithm:
Given: User Trace UT, Service Taxonomy ST, User Request Ureq Obtain: Trust Weight Tw
Start
Read UT, ST, Ureq.
TAS = Estimate Trusted Access Score.
FTS = Estimate Feature Trust Score.
Compute Trust Weight Tw = TAS×FTS. – (11)
Stop
The above-discussed algorithm estimates trusted access to score and feature trust score to measure the trust weight towards access restriction in the cloud environment.
3.5 Blockchain Public Auditing (BCPA)
The proposed approach enforces public auditing with blockchain technology. The method maintains the data in form of blocks and each block has been tied with a reference number which denotes the link to the next block. However, the user has access to different data in the chain, they will be allowed to access only specific blocks and features of the chain. To enforce this, when a user request for update request, the method computes the block level trust score (BLTS) towards various blocks the user request claims. It has been measured based on the genuine of the data submitted. To estimate this, the method decrypts each data in the block where the data update to be done and verify each of them. According to that, the method computes the number of blocks the request accesses and the number of blocks verified correctly. Also, the method collects approval from limited owners and count on them. Using both of them, the method computes the value of BLTS and based on that the method performs public auditing and allows the update service.
Algorithm:
Given: Update Request Ureq, Blockchain BC Obtain: BLTS
Start
Read Ureq and BC.
Identify the number of blocks to be updated Nobu =
𝑠𝑖𝑧𝑒(𝐵𝐶) 𝑈𝑟𝑒𝑞 ∈ 𝐵𝐶(𝑖)
𝑖 = 1
-- (12) For each block b
Block Data Bd = 𝐷𝑒𝑐𝑟𝑦𝑝𝑡(𝐵𝑐(𝑏)) Boolean bool = Verify Bd.Signature If true then
Increase count End
End
Compute BLTS = 𝑐𝑜𝑢𝑛𝑡
𝑁𝑜𝑏𝑢 ×∑𝑁𝑜𝑏𝑢𝑖=1 𝐵𝐶(𝑖).𝑎𝑐𝑐𝑒𝑠𝑠==𝑡𝑟𝑢𝑒
𝑁𝑜𝑏𝑢 -- (13)
Stop
The above-discussed algorithm represents how the blockchain public auditing is performed by counting the signature as well as the number of blocks to be updated and the number of blocks to which the user has access. Also, the method verifies the signature of the blocks to enforce public auditing and according to that, the method computes the value of BLTS. The value of BLTS has been used in enforcing the service handling.
4. Results and Discussions
The present approach has been implemented inthe Microsoft Azure platform and its metrics were calculated on different parameters. obtained results have been compared with the results of other approaches. The details of the evaluation are presented in Table 1 below.
Table 1: Evaluation Details
Parameter Value
Tool Microsoft Azure
Programming Advanced Java
Number of Services 200
The details of evaluation towards performance analysis of proposed approach have been presented in Table 1. The methods are evaluated on different metrics and obtained results are presented and discussed in this section.
Table 2: Analysis of content sharing
Performance on Content Sharing
50 Services 100 Services 200 Services
BT 76 81 84
OCT 82 85 87
REST 85 87 89
STS 87 89 91
DTAR-BCPA 89 93 97
The performance on content sharing has been measured and presented in Table 2. The proposed DTAR-BCPA method improves the performance in content sharing up to 97% which is higher than any other approach.
Figure 2: Analysis of content Sharing performance
The performance of DTAR-BCPA towards content sharing has been measured on a varying number of services. The proposed DTAR-BCPA approach resulted in improved performance in data sharing.
Table 3: Analysis of Access restriction
Performance on Access Restriction
50 Services 100 Services 200 Services
BT 72 76 79
OCT 75 78 83
REST 78 82 86
STS 81 85 89
DTAR-BCPA 84 87 96
The performance on access restriction has been measured and presented in Table 3. The proposed DTAR-BCPA method improves the performance in access restriction up to 96% which is higher than any other approach.
0 10 20 30 40 50 60 70 80 90 100
BT OCT REST STS DTAR-BCPA
Content Sharing Performance %
Content Sharing
50 Services 100 Services 200 Services
Figure 3: Analysis of access restriction performance
The performance of DTAR-BCPA towards access restriction has been measured on a varying number of services. The proposed DTAR-BCPA approach resulted in improved access restriction compared to other approaches.
Table 4: Analysis of Public Auditing Performance Performance on Public Auditing
50 Services 100 Services 200 Services
BT 68 71 74
OCT 71 74 78
REST 75 78 81
STS 81 85 89
DTAR-BCPA 84 87 96
The performance on public auditing has been measured and presented in Table 4. The proposed DTAR-BCPA method improves the performance in public auditing up to 96% which is higher than any other approach.
0 10 20 30 40 50 60 70 80 90 100
BT OCT REST STS DTAR-BCPA
Access Restriction Performance %
Access Restriction Performance
50 Services 100 Services 200 Services
Figure 4: Analysis of public auditing performance
The performance of DTAR-BCPA towards public auditing has been measured on a varying number of services. The proposed DTAR-BCPA approach resulted inimproved performance in public auditing compared with other models.
Table 5: Analysis of Throughput Performance Performance on Throughput
50 Services 100 Services 200 Services
BT 72 76 79
OCT 75 78 83
REST 78 82 86
STS 81 85 89
DTAR-BCPA 84 87 96
The performance on throughput has been measured and presented in Table 3. The proposed DTAR-BCPA method improves the performance in throughput up to 96% which is higher than any other approach.
0 10 20 30 40 50 60 70 80 90 100
BT OCT REST STS DTAR-BCPA
Public Auditing Performance %
Public Auditing Performance
50 Services 100 Services 200 Services
Figure 5: Analysis of throughput performance
The performance of DTAR-BCPA towards throughput achievement has been measured on a varying number of services. The proposed DTAR-BCPA approach has shown progress in throughput than other approaches mentioned.
5. Conclusion
This article presented a novel service-centric dynamic trust analysis access restriction and public auditing scheme for the cloud environment. The method receives the user request and analyzes the trust of the user by applying a service-centric feature trust trace analysis scheme which measures features trust score and trusted access score to measure trust weight. Further, the method applies, blockchain public auditing to enforce public auditing. The proposed method estimates blockchain trust scores to enforce blockchain public auditing. The proposed method improves the performance of access restriction and public auditing up to 97%.
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