Content uploaded by Suyel Namasudra
Author content
All content in this area was uploaded by Suyel Namasudra on Aug 02, 2022
Content may be subject to copyright.
DOI: 10.4018/IJARB.2021010105
Volume 11 • Issue 1 • January-June 2021
Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
40
Suyel Namasudra, National Institute of Technology, Patna, India
Bioinformatics is a branch of science that applies computational science in the biological world. In
bioinformatics, large sizes of biological data (genome) are processed in the cloud computing platform.
Due to the advantages of cloud computing, such as reduced cost scalability, high performance,
unlimited storage and many more, the applications of cloud computing in bioinformatics are increasing
exponentially. However, cloud computing has some disadvantages like security, privacy, transferability,
etc. Among all these problems, access control is a critical issue in the cloud computing environment.
The main objective of this paper is to present many access control models along with their advantages
and disadvantages. Moreover, some of the popular cloud-based bioinformatics applications are also
introduced for the benefit of researchers.
Access Control Models, Bioinformatics, Cloud Computing, Cloud Service Provider, Data Owner, Galaxy Cloud
Cloud computing facilitates fast and efficient parallel processing of terabyte-scale data in the virtual
environment (Huthand & Chebula, 2011). In cloud computing, there are three entities (stakeholders)
namely Data Owner (DO), Cloud Service Provider (CSP) and user. The DO shares their own data or
file on the cloud server. The CSP provides the cloud services for both DO and user. The users access
data or file from the cloud server (Namasudra et al., 2014; Zhang et al., 2010; Namasudra & Roy,
2017a). The users cannot access data randomly by their wishes. Each CSP has its own access policy
or right. So, when the users want to access any data or file from the cloud server, they must satisfy
the access right to access the requested data from the cloud environment.
There are mainly four types of cloud deployment models:
1. Private cloud
2. Public cloud
3. Community cloud
4. Hybrid cloud
A private cloud infrastructure is solely operated by a single organization. It can be managed by an
organization or by a third party. In a public cloud, the CSP provides the resources, such as network,
server, etc. to the users. Anyone can join in the public cloud. In a community cloud infrastructure,
a cloud environment is shared by a community/several communities. All these communities must
Volume 11 • Issue 1 • January-June 2021
41
have a common goal. Hybrid cloud is the combination of public, private and community cloud. It is
managed by a central administrator.
Cloud services can be provided in three ways, namely Software as a Service (SaaS), Platform
as a Service (PaaS) and Infrastructure as a Service (IaaS). Figure 1 shows the simple scenario of a
cloud environment.
In a cloud environment, when the CSP receives a data access request from a user, it must
provide public key of the DO to the user to get the secret key and other necessary credentials. So,
if the CSP takes much time to search the DO, the user must wait to get the details. So, in result, the
data accessing time is also increased. Thus, user must need to pay more for using the cloud services.
In the existing Access Control Model (ACM), the DO must be always online during the entire data
communication process, so load on the CSP i.e. system overhead is increased (Gao et al., 2012).
Another critical issue in a cloud environment is data security due to the presence of hackers. Those
hackers are geographically distributed, and always want to unauthorized access of the confidential
data. Sometimes, they change the original data, which is very difficult to identify for any cloud service
provider. Many researchers have proposed many access control models to solve these issues, namely
high searching time of the DO, high data or file accessing, high system overhead and data security
(Namasudra, 2019; Sarkar et al., 2015; Namasudra et al., 2017a; Zhao et al, 2019; Namasudra et al.,
2020a; Namasudra, 2018a; Namasudra et al., 2017b; Alguliyev et al., 2020; Namasudra et al, 2017c;
Namasudra & Roy, 2016; Namasudra et al., 2020b; Namasudra & Deka, 2018a; Li et al., 2019;
Namasudra et al., 2018a; Namasudra et al., 2020c; Namasudra & Deka, 2018b; Namasudra & Roy,
2018; Namasudra et al., 2018b; Deka & Borah, 2012; Namasudra & Roy, 2017b).
Bioinformatics is an interdisciplinary field that applies computational technique to analyze a
large collection of biological data. Bioinformatics is the application of computer science to solve the
Figure 1. Simple scenario of a cloud environment
Volume 11 • Issue 1 • January-June 2021
42
problems of biological science. Enhancing the reproducibility of bioinformatics experiments requires
robust computational environment for providing secure access of large-scale distributed biomedical
data across the heterogeneous platforms in the cloud computing environment.
Researchers from across the World have been working to deal with the large sequencing data,
such as DNA sequence to discover novel findings (Qiu et al., 2010). Nowadays, these biomedical
datasets are increasingly stored on commercial as well as institutional cloud computing platforms.
Cloud computing environment provides computational environment for the users’ on-demand and
pay-per-use basis (Rosenthal et al., 2010). From a small research lab to large research lab, cloud
computing is widely used in bioinformatics for the storage, retrieval and analysis of big biological
data. Complex genetic, protein, and other life sciences data are easily manageable in a cloud computing
environment due to its flexibility (Ryding, 2020).
PaaS cloud computing model allows customizing the bioinformatics applications to retain
complete control data. CloudMan, Galaxy Cloud and Eoulsan are example, similarly, Bionimbus,
CloVR and CloudBioLinux are examples of IaaS cloud computing solutions to process genomics
and phenotypic data (Calabrese & Cannataro, 2016).
Access to data or information from different domains is very challenging. There are many access
control models with their advantages and disadvantages (Deka, 2014). So, any researcher of
bioinformatics, who wants to work on data accessing can get a clear view about state-of-the-art
technology in this field. The main contributions of this paper are given below:
a) Problems of access control models have been discussed in details.
b) Many access control models are briefly presented in this paper along with their advantages and
disadvantages.
c) Moreover, many applications of cloud computing in bioinformatics field have been discussed in
this paper.
The rest of the paper has 4 sections. Section 3 discusses problems of access control model.
Access control models are discussed in Section 4. Section 5 represents several applications of cloud
computing in bioinformatics. Finally, the paper is concluded in Section 6.
An ACM is a set of policy of a system administrator which defines users’ right. ACM ensures that
authorized users can access data (Namasudra, 2018b; Namasudra & Roy, 2017c; Majumder, 2014;
Namasudra & Roy, 2015). ACM also monitors and records all attempts made by all the users to a
system for accessing data or any kind of service. In addition, ACM manages users, file, data and
other computing resources. ACM provides security to the resources or data.
In ACM, there are four steps namely identification, authentication, authorization and
accountability. These steps must be performed before a user accesses the resources. For accessing
data from a cloud environment, there are many issues, which are listed below:
• The CSP has to check the whole Database (Db) for providing the public key of the data owner.
• As the searching time to find the public key of the data owner is increased, the data accessing
time becomes high.
• The CSP searches the whole Db to providing one data.
• The data owner should be always online during the entire data accessing process.
Volume 11 • Issue 1 • January-June 2021
43
• The user faces problem as sometimes, the CSP asks the user to register outside of the cloud server.
• There is a probability that data can be misused or stolen from the cloud server.
• The CSP has to differentiate between a sensitive data and an ordinary data because of the security
issue.
• If the vendor stops sharing data or any service, then there can be data loss or the user is not be
able to use service. Thus, it can create a very critical issue.
• In a cloud environment, the user does not know where the data are actually stored. Data can be
stored anywhere in the world. So, there can be Govt. regulatory issue for unauthorized access.
• Achieving fine-grained access control is another important problem in the cloud computing.
Many researchers have proposed many access control models. Some of the ACMs are briefly discussed
in this section.
RBAC provides Access Right (AR) to a user based on the job roles (Ferraiolo & Kuhn; 1992). The
role of a particular user depends on the latest privileges. Role means minimum amount of permissions,
which are needed for the job to be finished. Permissions can be changed, if the role is changed. RBAC
is basically depends on access control decisions. RBAC allows and promotes the Central Authority
(CA) of an organization to assign security policy. The main problem arises in RBAC, when users
want to access data from outside their domain.
UCON can easily implement the security policy of RBAC (Danwei et al., 2009). It also can establish
many ACMs under any type of critical situation. UCON provides decision-making ability to the
CSP. It can easily give cloud services to the users. The main advantage of UCON is that there is a
negotiation model for negotiating between the CSP and user. In UCON, there are several modules.
So, data accessing time is increased.
In ABAC, access is granted based on the user’s attributes (Yu et al., 2010). Here, the user has to prove
the attributes at the time of accessing the data. In ABAC, to assign a set of attributes by the CSP, it
is very difficult most of the time, especially when the user wants to access data from outside his/her
domain. ABAC provides fine-grained data accessing, and it does not reveal the original content of
data. In ABAC, setting of a group or set of attribute is quite difficult.
CBAC addresses security and access control issues by using capability based access control that
ensures only the authorized users can access data (Hota et al., 2011). CBAC is composed of three
parties: DO, CSP and user. The DO sends the keys and certificate to the user after receiving the data
access request. The user shows the certificate to the CSP. If the certificate is valid, then the CSP
provides the encrypted data. This ACM is very flexible, and the DO may come online, if necessary.
In CBAC, data accessing time is high.
GBAC represents a virtual private cloud, and it is based on gateway (Wu et al., 2012). GBAC offers
users to access other private clouds from their own private cloud in the collaboration manner. For
each and every organization, there is a gateway that converts user’s data into the SAML format.
Volume 11 • Issue 1 • January-June 2021
44
Then, this SAML goes to the target organization. Unauthorized user faces quite difficulties in GBAC
to get the data since it is converted into the SAML format. In GBAC, access cannot be possible in
bidirectional manner.
In 2012, to achieve fine-grained, flexible and scalable access control, Wan et al. (Wan et al., 2012) have
proposed a hierarchical attribute set based encryption scheme that extends the concept of Attribute
Set Based Encryption (ASBE) with hierarchical structure. In this scheme, user’s access rights are
expired after a predefined time. This scheme increases overhead because workload of higher-level
trusted authority is transferred to lower-level domain authority.
MAC can be referred as giving access policy based on the sensitivity information about the data
(Crues, 2013). In MAC, when a user wants to access a data, only the CA decides whether the access
policy can be given to the user and what type of access policy can be given to the user. MAC ensures
security by controlling all the tasks by the CA. The disadvantage of MAC is that it does not ensure
fine-grained data accessing.
In DAC, the CSP provides separate rules for every user (Crues, 2013). DAC provides a single access
policy or rule for a group of users. Here, accessing of data or file is fully controlled by the operating
system. DAC allows user or a group of user to access a data from the cloud server. DAC is basically
default ACM for almost all the cloud environments. The main advantage of this model is that in DAC,
the users need not to ask permission from the CA to give his/her own AR to another user. Maintenance
of the CSP and security are big challenge in DAC.
MTBACM (Guoyuan et al., 2014) has been proposed to achieve mutual trust among different entities
of cloud environment. It uses the credibility of the CSP and user. Here, the relationship of trust is
achieved between the CSP and users by using mutual trust technique. There are five entities in the
system model of MTBACM: authentication and authorization center, CSP, users, CSP’s trust database
and user’s behavior trust database. However, MTBACM does not minimize high data accessing time.
Ajgaonkar et al. (Ajgaonkar et al., 2015) have used the concept of RBAC to propose activity-based
ACM. This scheme supports accessing of any data by the designation of the user in a company s/he
works. Here, the data decryption key is encrypted by using Rivest-Shamir-Adleman (RSA) algorithm
to improve data security. This model comprises of some components: roles, tasks, users, permissions,
session, user assignment, security clearance, security level and data. Therefore, the chances to increase
the system overhead are high.
Index generation-based ACM (Raghavendra et al., 2016) was proposed by generating substring index.
Here, RSA algorithm with Chinese Remainder Theorem provides a secure and efficient multi-owner
data storage technique. This scheme is mainly for dynamic data group, and it reduces storage space,
computational and storage overhead. However, data may face security issue since a group member
can access any data corresponding to the same group.
Volume 11 • Issue 1 • January-June 2021
45
In 2017, CTAC scheme was proposed by Alam et al. (Alam et al. 2017). In this scheme, the cloud
service provider plays the activity of the trusted third party. CTAC supports sharing of resources
between two different tenants. There are four algorithms in CTAC for delegation mechanism and
permission activation between tenants: activation, forward revocation, delegation and backward
revocation. CTAC does not support fast data accessing.
In a cloud computing environment, data leakage issue is increasing because of sharing resources. To
solve this issue, a novel mechanism has been proposed by Almutairi et al. (Almutairi et al., 2018) for
multitenant cloud environment, namely Notion Based ACM (NBACM). NBACM introduces notion
of sensitivity at the data centre’s end. NBACM is based on RBAC, and it is designed to minimize
the risk of assigning the Virtual Machine (VM) for each task. However, this scheme does not support
fast data accessing.
Modified hierarchical attribute-based encryption (Xie, 2019) scheme has been proposed by using
attribute-based encryption and Hierarchical Identity-Based Encryption (HIBE). MHABE is designed
for focusing data processing, data accessing and data storing in the multi-user cloud environment.
Nevertheless, searching time of DO is high in MHABE, so users need to pay more for using the
cloud services.
There are many applications of cloud computing in bioinformatics field. In this section, many
applications are discussed.
In bioinformatics, comparative genomics is used to analyze genome size. Here, Reciprocal Shortest
Distance (RSD) algorithm is utilized to compare the size. Three applications of bioinformatics are
used by RSD, namely BLAST, Codeml and ClustalW. The genomic sequence is searched by shortest
distance algorithm, and the sequence is annotated by using the best score. This is an iterative process,
which takes much computation power and consumes a large amount of time to execute all the processes.
The cloud computing environment can be used to implement the above mentioned algorithm,
so that the entire genome data can be analyzed in a short time (Wall, 2010). Here, the code can be
implemented on the master node by using Amazon’s Elastic Map Reduce (EMR) algorithm. The
given or input sequence can be mapped into a number of chunks, and the whole process is executed
here in a distributed manner over slave nodes.
Neuroscience is the branch of science that deals with nervous system. It discusses function and structure
of brain and nervous system. As the technology is getting advanced day-by-day, it is producing data
or information in a high rate. In future, data is going to be increased in an exponential rate. The tools
that are used to get neurological data produce informal metadata format. So, data are unable to share.
Thus, it creates a major issue. Moreover, data size is also very large.
A tool, namely CARMEN has been developed by using the concept of cloud computing to solve
this issue (Watson et al., 2008). This tool supports to share, integrate and analyze neurological data over
a cloud environment. Here, no external tool or software is required to install for metadata formatting
by using the cloud platform. Thus, researcher can easily share the neurological data.
Volume 11 • Issue 1 • January-June 2021
46
Iterative analysis of genomic data is termed as metagenomics. In metagenomics, the entire genome
data sequence is searched to get the best match to recognize physiological function and organism.
This approach reveals the initial information of groups, genus and class from environmental samples,
such as intestinal cavity, marine, hot spring and many more to analyze further in the laboratory. In the
traditional technique, the data are chunked into a number of small fragments, and then, the chunk is
searched for its identical and homologues fragments or portion for identifying the organism. Due to
the advancement of the sequencing approaches, the data that need to be analyzed has been drastically
increased. Thus, the demand for high power fast computational infrastructure is rapidly increasing.
BLAST, a bioinformatics tool may be used for such type of search. BLAST needs high
computation power and heavy system resources for execution (Wilkening et al., 2009). A researcher
can choose local server setup or cost effective cloud computing environment to run BLAST. The main
advantage of using cloud server (VM) is time efficiency. So, for researchers/research organizations
using BLAST, cloud is the choice.
In the trivial technique, user can access any data directly from the websites or databases to maintain
local copy in his/her own resources. The user then can analyze the data in their own environment.
Here, all the data transfers are executed by using Hypertext transfer protocol or File transfer protocol.
Trivial technique of genome informatics was working smoothly till 1980s. However, when the Next
Generation Sequencing (NGS) technique was introduced, it became difficult to analyze data locally
because of increasing demand of data.
Cloud computing can be one of the solutions as cloud computing offers unlimited storage space
without installing any kind of external tool. In a cloud environment, all the data can be stored on the
cloud server, and data can be accessed based on demand for any kind of processing. Therefore, there
is no need to maintain a local copy, when a cloud computing environment is used.
Nowadays, RNA analysis is very popular. A RNA sequence gives the details about which gene is
active in a cell and when it has become active. A biologist can easily understand many details of cell
by RNA analysis. RNA analysis requires high computational power. Moreover, it requires a large
storage space.
MYNRA is a cloud computing based tool to calculate differential gene expression in a large
RNA sequence database. It can be done by using Hadoop to support cloud infrastructure. Table 1
represents the details for calculating gene expression of 2.2 billion RNA sequence.
Table 1 implies that when the number of nodes is increased, the performance of the cloud
environment is also increased. The load on the system must be considered along with data transmission
rate during the analysis. Elastic Computing Cloud (EC2) is one of the best cloud services to execute
the tasks.
Table 1. Hadoop based RNA sequence analysis
Sl. No. Master Node Worker Node Time required
1 1 10 8 hours 40 minutes
2 1 20 5 hours 4 minutes
Volume 11 • Issue 1 • January-June 2021
47
SNP detection technology is used for scanning to get new polymorphisms and for determining the
allele of a known or given polymorphism in the target sequence. If scanning is done to get new
polymorphisms and for determining the allele in a traditional computer system, it requires week of
time to analyze.
This above execution process can be done with Hadoop in less than 3 hours. Hence, a cloud
computing environment can be the better option for SNP detection (Schatz et al., 2010). Further, when
a cloud computing environment is used, the input can be given from different locations across the
World. Most importantly, the cloud computing does not compromise the accuracy during any execution.
Data access in a cloud environment is a very challenging issue nowadays because of hackers, high data
accessing time, etc. The main problem arises, when the user wants to access data or information from
outside his/her domain. In this paper, many access control models have been briefly discussed along
with their advantages and disadvantages. The CSP can easily understand which access control model
can be given to the user in a particular situation. Moreover, many applications of cloud computing
in bioinformatics field have been discussed in this paper.
Volume 11 • Issue 1 • January-June 2021
48
Ajgaonkar, S., Indalkar, H., & Jeswani, J. (2015). Activity based access control model for cloud computing.
International Journal of Current Engineering and Technology, 5(2), 2347–5161.
Alam, Q., Malik, S. U. R., Akhunzada, A., Choo, K. K. R., Tabbasum, S., & Alam, M. (2017). A cross tenant
access control (CTAC) model for cloud computing: Formal specification and verification. IEEE Transactions
on Information Forensics and Security, 12(6), 1259–1268. doi:10.1109/TIFS.2016.2646639
Alguliyev, R. M., Aliguliyev, R. M., & Sukhostat, L. V. (2020). Efficient algorithm for big data clustering on
single machine. CAAI Transactions on Intelligence Technology, 5(1), 9–14. doi:10.1049/trit.2019.0048
Almutairi, A., Sarfraz, M. I., & Ghafoo, A. (2018). Risk-Aware Management of Virtual Resources in Access
Controlled Service-Oriented Cloud Datacenters. IEEE Transactions on Cloud Computing, 6(1), 168–181.
doi:10.1109/TCC.2015.2453981
Calabrese, B. & Cannataro, M. (2016). Cloud Computing in Bioinformatics: current solutions and challenges.
DOI: 10.7287/PEERJ.PREPRINTS.2261
Crues, R. A. (2013). Methods for access control: advances and limitations. Available: http://www.cs.hmc.
edu/~mike/public_html/courses/security/s06/projects/ryan.pdf
Danwei, C., Xiuli, H., & Xunyi, R. (2009). Access control of cloud service based on UCON. Proceedings of the
1st International Conference on Cloud Computing, 559-564. doi:10.1007/978-3-642-10665-1_52
Deka, G. C. (2014). Handbook of Research on Securing Cloud-Based Databases with Biometric Applications.
IGI Global.
Deka, G. C., & Borah, M. D. (2012). Cost benefit analysis of cloud computing in education. Proceedings of the
International Conference on Computing, Communication and Applications, 1-6.
Ferraiolo, D. F., & Kuhn, D. R. (1992). Role-based access controls. Proceedings of the 15th National Computer
Security Conference, 554-563.
Gao, X., Jiang, Z., & Jiang, R. (2012). A novel data access scheme in cloud computing. Proceedings of the
2nd International Conference on Computer and Information Applications, 124-127. doi:10.2991/iccia.2012.30
Guoyuan, L., Danru, W., Yuyu, B., & Min, L. (2014). MTBAC: A mutual trust based access control model in
cloud computing. China Communications, 11(4), 154–162. doi:10.1109/CC.2014.6827577
Hota, C., Sanka, S., Rajarajan, M., & Nair, S. K. (2011). Capability-based cryptographic data access control in
cloud computing. Advanced Networking and Applications, 1(1), 1152–1161.
Huthand, A., & Chebula, J. (2011). The basics of cloud computing. Carnegie Mellon University.
Li, S., Wang, G., & Yang, J. (2019). Survey on cloud model based similarity measure of uncertain concepts.
CAAI Transactions on Intelligence Technology, 4(4), 223–230. doi:10.1049/trit.2019.0021
Majumder, A., Namasudra, S., & Nath, S. (2014). Taxonomy and classification of access control models for
cloud environments. In Z. Mahmood (Ed.), Continued Rise of the Cloud (pp. 23–53). Springer. doi:10.1007/978-
1-4471-6452-4_2
Namasudra, S. (2018). Taxonomy of DNA-based security models. In S. Namasudra & G. C. Deka (Eds.),
Advances of DNA Computing in Cryptography (pp. 53–68). Taylor & Francis. doi:10.1201/9781351011419-3
Namasudra, S. (2018). Cloud computing: A new era. Journal of Fundamental and Applied Sciences, 10(2),
113–135.
Namasudra, S. (2019). An improved attribute-based encryption technique towards the data security in cloud
computing. Concurrency and Computation: Practice and Exercise, 31(3), e4364. Advance online publication.
doi:10.1002/cpe.4364
Namasudra, S., Chakraborty, R., Majumder, A., & Moparthi, N. R. (2020). (in press). Securing Multimedia
by Using DNA Based Encryption in the Cloud Computing Environment. ACM Transactions on Multimedia
Computing Communications and Applications.
Volume 11 • Issue 1 • January-June 2021
49
Namasudra, S., & Deka, G. C. (2018). Advances of DNA computing in cryptography. Taylor & Francis.
doi:10.1201/9781351011419
Namasudra, S., & Deka, G. C. (2018). Introduction of DNA computing in cryptography. In S. Namasudra
& G. C. Deka (Eds.), Advances of DNA Computing in Cryptography (pp. 27–34). Taylor & Francis.
doi:10.1201/9781351011419-1
Namasudra, S., Deka, G. C., & Bali, R. (2018). Applications and future trends of DNA computing. In
S. Namasudra & G. C. Deka (Eds.), Advances of DNA Computing in Cryptography. Taylor & Francis.
doi:10.1201/9781351011419-9
Namasudra, S., Deka, G. C., Johri, P., Hosseinpour, M., & Gandomi, A. H. (2020). (in press). The Revolution
of Blockchain: State-of-the-Art and Research Challenges. Archives of Computational Methods in Engineering.
Advance online publication. doi:10.1007/s11831-020-09426-0
Namasudra, S., Devi, D., Choudhary, S., Patan, R., & Kallam, S. (2018). Security, privacy, trust, and anonymity.
In S. Namasudra & G. C. Deka (Eds.), Advances of DNA Computing in Cryptography (pp. 153–166). Taylor &
Francis. doi:10.1201/9781351011419-7
Namasudra, S., Devi, D., Kadry, S., Sundarasekar, R., & Shanthini, A. (2020). Towards DNA based data security in
the cloud computing environment. Computer Communications, 151, 539–547. doi:10.1016/j.comcom.2019.12.041
Namasudra, S., Nath, S., & Majumder, A. (2014). Profile based access control model in cloud computing
environment. In Proceedings of the International Conference on Green Computing, Communication and Electrical
Engineering. IEEE. doi:10.1109/ICGCCEE.2014.6921420
Namasudra, S., & Roy, P. (2015). Size based access control model in cloud computing. In Proceedings of
the International Conference on Electrical, Electronics, Signals, Communication and Optimization. IEEE.
doi:10.1109/EESCO.2015.7253753
Namasudra, S., & Roy, P. (2016). Secure and efficient data access control in cloud computing environment: A
survey. Multiagent and Grid Systems-An International Journal, 12(2), 69–90. doi:10.3233/MGS-160244
Namasudra, S., & Roy, P. (2016). A new secure authentication scheme for cloud computing environment.
Concurrency and Computation: Practice and Exercise, 29(20), e3864. Advance online publication. doi:10.1002/
cpe.3864
Namasudra, S., & Roy, P. (2017). A new table based protocol for data accessing in cloud computing. Journal
of Information Science and Engineering, 33(3), 585–609.
Namasudra, S., & Roy, P. (2017). Time saving protocol for data accessing in cloud computing. IET
Communications, 11(10), 1558–1565. Advance online publication. doi:10.1049/iet-com.2016.0777
Namasudra, S., & Roy, P. (2018). PpBAC: Popularity based access control model for cloud computing. Journal
of Organizational and End User Computing, 30(4), 14–31. doi:10.4018/JOEUC.2018100102
Namasudra, S., Roy, P., & Balamurugan, B. (2017). Cloud computing: fundamentals and research issues. In
Proceedings of the International Conference on Recent Trends and Challenges in Computational Models. IEEE.
Namasudra, S., Roy, P., Balamurugan, B., & Vijayakumar, P. (2017). Data accessing based on the popularity value
for cloud computing. In Proceedings of the International Conference on Innovations in Information, Embedded
and Communications Systems (ICIIECS). IEEE. doi:10.1109/ICIIECS.2017.8275892
Namasudra, S., Roy, P., Vijayakumar, P., Audithan, S., & Balamurugan, B. (2017). Time efficient secure DNA
based access control model for cloud computing environment. Future Generation Computer Systems, 73, 90–105.
doi:10.1016/j.future.2017.01.017
Qiu, J., Ekanayake, J., Gunarathne, T., Choi, J. Y., Bae, S.-H., Li, H., Zhang, B., Wu, T.-L., Ruan, Y., Ekanayake, S.,
Hughes, A., & Fox, G. (2010). Hybrid cloud and cluster computing paradigms for life science applications. BMC
Bioinformatics, 11(S12), S3. Advance online publication. doi:10.1186/1471-2105-11-S12-S3 PMID:21210982
Raghavendra, S., Meghana, K., Doddabasappa, P. A., Geeta, C. M., Buyya, R., Venugopal, K. R., Iyengar, S. S.,
& Patnaik, L. M. (2016). Index generation and secure multi-user access control over an encrypted cloud data.
Procedia Computer Science, 89, 293–300. doi:10.1016/j.procs.2016.06.062
Volume 11 • Issue 1 • January-June 2021
50
Rosenthal, A., Mork, P., Li, M. H., Stanford, J., Koester, D., & Reynolds, P. (2010). Cloud computing: A new
business paradigm for biomedical information sharing. Journal of Biomedical Informatics, 43(2), 342–353.
doi:10.1016/j.jbi.2009.08.014 PMID:19715773
Ryding, S. (2020). Impact of Cloud-Based Technologies on Research. Available: https://www.azolifesciences.
com/article/Impact-of-Cloud-Based-Technologies-on-Research.aspx
Sarkar, M., Saha, K., Namasudra, S., & Roy, P. (2015). An efficient and time saving web service based android
application. SSRG International Journal of Computer Science and Engineering, 2(8), 18–21.
Schatz, M. C., Langmead, B., & Salzberg, S. L. (2010). Cloud computing and the DNA data race. Nature
Biotechnology, 28(7), 691–693.
Wall, D. P., Kudtarkar, P., Fusaro, V. A., Pivovarov, R., Patil, P., & Tonellato, P. J. (2010). Cloud computing for
comparative genomics RSD algorithm summary. BMC Bioinformatics, 11.
Wan, Z., Liu, J., & Deng, R. H. (2012). HASBE: A hierarchical attribute-based solution for flexible and scalable
access control in cloud computing. IEEE Transactions on Information Forensics and Security, 7(2), 743–754.
doi:10.1109/TIFS.2011.2172209
Watson, P., Lord, P., Gibson, F., Periorellis, P., & Pitsilis, G. (2008). Cloud Computing for e-Science with
CARMEN. Proceedings of the Iberian GRID infrastructure conference.
Wilkening, J., Wilke, A., Desai, N., & Meyer, F. (2009). Using clouds for metagenomics – A case study.
Proceedings of the IEEE Cluster International Conference.
Wu, Y., Suhendra, V., & Guo, H. (2012). A gateway-based access control scheme for collaborative clouds.
Proceedings of the 7th International Conference on Internet Monitoring and Protection, 54-60.
Xie, Y., Wen, H., Wu, B., Jiang, Y., & Meng, J. (2019). A modified hierarchical attribute-based encryption
access control method for mobile cloud computing. IEEE Transactions on Cloud Computing, 7(2), 383–391.
doi:10.1109/TCC.2015.2513388
Yu, S., Wang, C., Ren, K., & Lou, W. (2010). Achieving secure, scalable, and fine-grained data access control
in cloud computing. Proceedings of the IEEE INFOCOM, 1-9. doi:10.1109/INFCOM.2010.5462174
Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: State-of-the-art and research challenges. Journal
of Internet Services and Applications, 1(1), 7–18. doi:10.1007/s13174-010-0007-6
Zhao, X., Li, R., & Zuo, X. (2019). Advances on QoS-aware web service selection and composition with nature-
inspired computing. CAAI Transactions on Intelligence Technology, 4(3), 159–174. doi:10.1049/trit.2019.0018
Suyel Namasudra is an Assistant Professor in the Department of Computer Science and Engineering at the
National Institute of Technology Patna, Bihar, India. Prior to joining the National Institute of Technology Patna,
Dr. Namasudra was an Assistant Professor in the Department of Computer Science Engineering at the Bennett
University, India. He has received PhD in Computer Science and Engineering from National Institute of Technology
Silchar, Assam, India. His research interests include Computer Networks, Cloud Computing, Information Security
and DNA Computing. Dr. Namasudra has edited 1 book and 30 publications in refereed journals, book chapters
and conference proceedings. He has participated in many international conferences as an Organizer and Session
Chair. Dr. Namasudra is a member of the Editorial Board and Reviewer of many journals.
