Privacy-Aware Adaptive Data Encryption Strategy of Big Data in Cloud Computing

As there is a continuous delivery of big data, the researchers are showing interest in the applications of cloud computing concerning privacy, and security. On the other hand, many researchers and experts of cybersecurity have commenced on a quest for improving the data encryption to the models of big data and applications of cloud computing. Since many users of the cloud become public cloud services, confidentiality turns out to be a more compound problem. To solve the confidentiality problem, cloud clients maintain the data on the public cloud. Under this circumstance, Homomorphic Encryption (HE) appears as a probable solution, in which the information of the client is encrypted on the cloud in such a process that it permits few manipulation operations without decryption. The main intent of this paper is to present the systematic review of research papers published in the field of Fully Homomorphic Encryption (FHE) over the past 10 years. The encryption scheme is considered full when it consists of plaintext, a ciphertext, a keyspace, an encryption algorithm, and a decryption algorithm. Hence, the review mostly concentrates on reviewing more powerful and recent FHE. The contributions using different algorithms in FHE like Lattice-based, integer-based, Learning With Errors (LWE), Ring Learning With Errors (RLWE), and Nth degree Truncated polynomial Ring Units (NTRU) are also discussed. Finally, it highlights the challenges and gaps to be addressed in modeling and learning about competent, effectual, and vigorous FHE for the cloud sector and pays attention to directions for better future research.

Big Data Handling Approach for Unauthorized Cloud Computing Access

Electronics ◽

10.3390/electronics11010137 ◽

2022 ◽

Vol 11 (1) ◽

pp. 137

Author(s):

Abdul Razaque ◽

Nazerke Shaldanbayeva ◽

Bandar Alotaibi ◽

Munif Alotaibi ◽

Akhmetov Murat ◽

...

Keyword(s):

Cloud Computing ◽

Big Data ◽

Access Control ◽

Intrusion Detection ◽

Data Access ◽

Data Encryption ◽

Data Handling ◽

Unauthorized Access ◽

Data Access Control ◽

Cloud Computing System

Nowadays, cloud computing is one of the important and rapidly growing services; its capabilities and applications have been extended to various areas of life. Cloud computing systems face many security issues, such as scalability, integrity, confidentiality, unauthorized access, etc. An illegitimate intruder may gain access to a sensitive cloud computing system and use the data for inappropriate purposes, which may lead to losses in business or system damage. This paper proposes a hybrid unauthorized data handling (HUDH) scheme for big data in cloud computing. The HUDH scheme aims to restrict illegitimate users from accessing the cloud and to provide data security provisions. The proposed HUDH consists of three steps: data encryption, data access, and intrusion detection. The HUDH scheme involves three algorithms: advanced encryption standards (AES) for encryption, attribute-based access control (ABAC) for data access control, and hybrid intrusion detection (HID) for unauthorized access detection. The proposed scheme is implemented using the Python and Java languages. The testing results demonstrated that the HUDH scheme can delegate computation overhead to powerful cloud servers. User confidentiality, access privilege, and user secret key accountability can be attained with more than 97% accuracy.

Privacy-Preserving Data Encryption Strategy for Big Data in Mobile Cloud Computing

IEEE Transactions on Big Data ◽

10.1109/tbdata.2017.2705807 ◽

2017 ◽

pp. 1-1 ◽

Cited By ~ 41

Author(s):

Keke Gai ◽

Meikang Qiu ◽

Hui Zhao

Keyword(s):

Cloud Computing ◽

Big Data ◽

Mobile Cloud Computing ◽

Data Encryption ◽

Privacy Preserving ◽

Mobile Cloud

Data Encryption using SCT and access control using TRBAC in Cloud Computing for Big Data

International Journal of Advanced Networking Applications ◽

10.35444/ijana.2019.10068 ◽

2019 ◽

Vol 10 (06) ◽

pp. 4094-4098 ◽

Cited By ~ 1

Author(s):

Akhilesh Dwivedi ◽

R P Pant ◽

Durgesh Pant ◽

Martand Pande ◽

Senam Pandey

Keyword(s):

Cloud Computing ◽

Big Data ◽

Access Control ◽

Data Encryption

Challenges and Cloud Computing Environments Towards Big Data

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset207277 ◽

2014 ◽

pp. 203-208

Author(s):

Kiran Kumar S V N Madupu

Keyword(s):

Data Mining ◽

Cloud Computing ◽

Big Data ◽

Technology Development ◽

Computing Environments ◽

Modern Technologies

Big Data has terrific influence on scientific discoveries and also value development. This paper presents approaches in data mining and modern technologies in Big Data. Difficulties of data mining as well as data mining with big data are discussed. Some technology development of data mining as well as data mining with big data are additionally presented.

Workflow Scheduling for Scientific Application in Homogeneous Cloud Environment

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i7.114 ◽

2017 ◽

Vol 7 (7) ◽

pp. 137

Author(s):

. Monika ◽

Pardeep Kumar ◽

Sanjay Tyagi

Keyword(s):

Cloud Computing ◽

Big Data ◽

Large Data ◽

Workflow Scheduling ◽

Cloud Environment ◽

Computing Environment ◽

Scientific Application ◽

Cloud Computing Environment ◽

Flow Of Information

In Cloud computing environment QoS i.e. Quality-of-Service and cost is the key element that to be take care of. As, today in the era of big data, the data must be handled properly while satisfying the request. In such case, while handling request of large data or for scientific applications request, flow of information must be sustained. In this paper, a brief introduction of workflow scheduling is given and also a detailed survey of various scheduling algorithms is performed using various parameter.

Issues in security and privacy of big data

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i12.482 ◽

2018 ◽

Vol 7 (12) ◽

pp. 1

Author(s):

Shaveta Bhatia

Keyword(s):

Cloud Computing ◽

Big Data ◽

Approximate Method ◽

Biomedical Research ◽

Cyber Security ◽

Data Science ◽

Third Party ◽

Security And Privacy ◽

Security Threats ◽

The Third

The epoch of the big data presents many opportunities for the development in the range of data science, biomedical research cyber security, and cloud computing. Nowadays the big data gained popularity. It also invites many provocations and upshot in the security and privacy of the big data. There are various type of threats, attacks such as leakage of data, the third party tries to access, viruses and vulnerability that stand against the security of the big data. This paper will discuss about the security threats and their approximate method in the field of biomedical research, cyber security and cloud computing.

Big Data and Cloud computing Review and future trends

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i12.361365 ◽

2018 ◽

Vol 6 (12) ◽

pp. 361-365

Author(s):

Meble Varghese ◽

Victor Jose

Keyword(s):

Cloud Computing ◽

Big Data ◽

Future Trends

A Survey on Various Issues Big Data in Cloud Computing

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v5i9.131134 ◽

2017 ◽

Vol 5 (9) ◽

Author(s):

Gaurav Jain

Keyword(s):

Cloud Computing ◽

Big Data

Trends and Opportunities in Health Analytics as a Service and Implications for Use in Low Resource Settings: A Literature Review Abstract (Preprint)

10.2196/preprints.15737 ◽

2019 ◽

Author(s):

Meghana Bastwadkar ◽

Carolyn McGregor ◽

S Balaji

Keyword(s):

Data Mining ◽

Cloud Computing ◽

Big Data ◽

Intensive Care ◽

Literature Review ◽

Health Monitoring ◽

Data Analytics ◽

Neonatal Intensive Care ◽

Big Data Analytics ◽

Healthcare Facilities

BACKGROUND This paper presents a systematic literature review of existing remote health monitoring systems with special reference to neonatal intensive care (NICU). Articles on NICU clinical decision support systems (CDSSs) which used cloud computing and big data analytics were surveyed. OBJECTIVE The aim of this study is to review technologies used to provide NICU CDSS. The literature review highlights the gaps within frameworks providing HAaaS paradigm for big data analytics METHODS Literature searches were performed in Google Scholar, IEEE Digital Library, JMIR Medical Informatics, JMIR Human Factors and JMIR mHealth and only English articles published on and after 2015 were included. The overall search strategy was to retrieve articles that included terms that were related to “health analytics” and “as a service” or “internet of things” / ”IoT” and “neonatal intensive care unit” / ”NICU”. Title and abstracts were reviewed to assess relevance. RESULTS In total, 17 full papers met all criteria and were selected for full review. Results showed that in most cases bedside medical devices like pulse oximeters have been used as the sensor device. Results revealed a great diversity in data acquisition techniques used however in most cases the same physiological data (heart rate, respiratory rate, blood pressure, blood oxygen saturation) was acquired. Results obtained have shown that in most cases data analytics involved data mining classification techniques, fuzzy logic-NICU decision support systems (DSS) etc where as big data analytics involving Artemis cloud data analysis have used CRISP-TDM and STDM temporal data mining technique to support clinical research studies. In most scenarios both real-time and retrospective analytics have been performed. Results reveal that most of the research study has been performed within small and medium sized urban hospitals so there is wide scope for research within rural and remote hospitals with NICU set ups. Results have shown creating a HAaaS approach where data acquisition and data analytics are not tightly coupled remains an open research area. Reviewed articles have described architecture and base technologies for neonatal health monitoring with an IoT approach. CONCLUSIONS The current work supports implementation of the expanded Artemis cloud as a commercial offering to healthcare facilities in Canada and worldwide to provide cloud computing services to critical care. However, no work till date has been completed for low resource setting environment within healthcare facilities in India which results in scope for research. It is observed that all the big data analytics frameworks which have been reviewed in this study have tight coupling of components within the framework, so there is a need for a framework with functional decoupling of components.