This document provides a literature review on data security and integrity in cloud environments. It discusses advantages and disadvantages of cloud computing and presents statistics on cloud data security challenges. A table compares 14 papers on various cloud data security audit methodologies and features such as public/batch auditing and maintaining data privacy, integrity and confidentiality. While many methods address auditing and privacy, they have limitations like not providing confidentiality or being computationally intensive. Therefore, an effective and secure auditing scheme is needed that can perform public auditing while maintaining data integrity and confidentiality in a batch manner.

1. Data Security and Integrity in Cloud
Environment
Literature Review
Date: 11-11-2021

2. By
Mazhar Waqar
PhD-CYS
ID No. 211880

3. Introduction
Cloud computing is a paradigm shift where computing is moved away from
personal computers or an individual application server to a “cloud” of computers.
[1]
Users only asked for Services, Has no concern about implementation. [1]
Examples: iCloud, OneDrive, Google Drive, Microsoft Azure, Amazon etc.
 Advantages: Cost Saving, No dedicated storage, Easy to implement, Fast
Access, Global Accessibility, Scalability, Synchronization of Data etc. [1]
 Disadvantages: Cost Huge Bandwidth, Vulnerable to attacks, Integrity and
privacy of data. [1]
Some Statistics on Cloud Data Security
1. Forbes [2] reported that 66% of IT professionals say security is their greatest
concern, when they were asked about adopting an enterprise cloud computing
platform.
2. Cyber Security Insiders (iSC)2
[3] stated that data loss 64% and data privacy
62% as the top cloud security challenges in a latest survey.
3. According to CSO online [4], only 7% of the businesses have good visibility of
all critical data.
4. Red Lock discovered [5] that 49% databases of the organization providing cloud
services are not Encrypted. They also revealed the fact that 25% of
organizations have crypto jacking activities within their environments. [5]

4. Related Work
*1 Support Public Auditing, *2 Support Batch Auditing *3 Maintains Data Privacy, *4 Maintains Data Integrity, *5 Maintains Data Confidentiality
Paper Title Methodology
Cloud Data Security Features
Limitations
*1 *2 *3 *4 *5
A Multi-grained Log Auditing
Scheme for Cloud Data
Confidentiality [9], (2019)
Log Auditing with Access List and
Merkale Hash Tree (MHT)
Yes No Yes Yes No
Heavy on Servers coz of
Log access Lists
generation
Identity-based public auditing for
cloud storage systems against
malicious auditors via blockchain
[10], (2019)
Blockchain of Bitcoin System Yes Yes Yes Yes No Computational Overheads
S-Audit: Efficient Data Integrity
Verification for Cloud Storage [11],
(2018)
Automated Service uses
Homomorphic Authentication
with Digital Signatures
Yes Yes Yes Yes No
Homorphic Encryption is
Vulnerable to
Regeneration Attack
Privacy preserving model: a new
scheme for auditing cloud
stakeholders [7], (2017)
Session Keys and SLA Verification,
C++, GreenCloud Simulator on
Windows 8
Yes Yes Yes Yes No Operational Inefficiency
Third-Party Auditor (TPA): A
Potential Solution for Securing a
Cloud Environment [6], (2015)
Time Bound Session Keys with
SLA, GreenCloud Simulator and
C++ on Ubuntu 12.4.
Yes No Yes Yes Yes No batch Auditing
A Hierarchical Security-Auditing
Methodology for Cloud
Computing [8], (2015)
Evidence collection Analytical
Hierarchy Process
(AHP) and Multilevel Fuzzy
Comprehensive Evaluation(MFCE)
Yes Yes Yes Yes No
Weight Assigning is a
complex task, No working
model Theoretical only
Cloud Security Audit for Migration
and Continuous Monitoring [12],
(2015)
Goal Oriented Requirements
Engineering (GORE) with CSA-
(CCM)
Yes No Yes Yes No
No Audit Guidelines
provided
Secured document management
through a third party auditor
scheme in Cloud Computing [13],
(2014)
Asymmetric Encryption
Algorithms with Document
Management System.
No Yes Yes Yes No
Communication
Overheads

5. Related Work (con…)
*1 Support Public Auditing, *2 Support Batch Auditing *3 Maintains Data Privacy, *4 Maintains Data Integrity, *5 Maintains Data Confidentiality
Paper Title Methodology
Cloud Data Security Features
Limitations
*1 *2 *3 *4 *5
An architecture for Cloud
Accountability Audit [16], (2014)
Audit Agent System based on SLA ,
Like SIEM
Yes No Yes Yes No No Batch Auditing
TETPA: A Case for Trusted Third Party
Auditor in Cloud Environment [15],
(2013)
Trusted Platform Module, TPM built
in chip, USBKey and CDP
Yes No Yes Yes Yes
Highly secure requires a lot
of resources
Supporting Cloud Accountability by
Collecting Evidence Using Audit
Agents[17], (2013)
Audit Agents to collect evidence
Information from logs
Yes Yes No Yes No
Maintaining Privacy on a big
system is a big issue
Cloud Data Security while using Third
Party Auditor[18], (2013)
RSA-with Elgamal Digital Signature Yes No Yes Yes No Batch Auditing
Ensuring data storage security
through a novel third party auditor
scheme in cloud computing [14],
(2011)
Bilinear Diffie-Hellman and RSA Yes No Yes Yes No Low Efficiency
In this table, a comparison is given on several security features (i.e. used
methodology, whether supports public auditing, batch auditing, data privacy
preserving, maintains data integrity and confidentiality etc). It is evident from the
table that various methods have been applied to verify cloud data confidentiality,
but every method has some limitations associated with it. Most of the existing
methods provide privacy preserving and auditing capabilities but unable to
maintain data confidentiality at some steps or the other. Therefore the need to
conceive an effective and secure auditing scheme that could perform public
auditing effectively by maintaining the data integrity and confidentiality in batch
manner is critical.

6. References
[1] https://ils.unc.edu/cws/Handouts/Cloud%20Storage/Cloud-Storage.pdf
[2] https://www.forbes.com/sites/louiscolumbus/2018/01/07/83-of-enterprise-workloads-will-be-in-the-cloud-by-2020/#27f90b496261
[3] https://www.isc2.org/-/media/ISC2/Landing-Pages/2019-Cloud-Security-Report-ISC2.ashx?la=en&hash=06133FF277
FCCFF720FC8B96DF505CA66A7CE565
[4] https://www.csoonline.com/in/
[5] https://redlock.io/blog/13-cloud-security-statistics-to-know-in-2019-with-9-best-practices
[6] S. Rizvi, A. Razaque, and K. Cover, “Third-party auditor (tpa): a potential solution for securing a cloud environment,” in 2015 IEEE 2nd International
Conference on Cyber Security and Cloud Computing. IEEE, 2015, pp. 31–36.
[7] A. Razaque and S. S. Rizvi, “Privacy preserving model: a new scheme for auditing cloud stakeholders,” J. Cloud Comput., vol. 6, no. 1, pp. 1–17, 2017.
[8] Z. Han, X. Li, and E. Stroulia, “A Hierarchical Security-Auditing Methodology for Cloud Computing,” Proc. - 2015 IEEE Int. Conf. Serv. Comput. SCC 2015,
pp. 202–209, 2015.
[9] Z. Yang, W. Wang, Y. Huang, and X. Li, “A multi-grained log auditing scheme for cloud data confidentiality,” Mobile Networks and Applications, pp. 1–9,
2019
[10] J. Xue, C. Xu, J. Zhao, and J. Ma, “Identity-based public auditing for cloud storage systems against malicious auditors via blockchain,”Science China
Information Sciences, vol. 62, no. 3, p. 32104, 2019.
[11] F. Apolin´ario, M. Pardal, and M. Correia, “S-audit: Efficient data integrity verification for cloud storage,” in 2018 17th IEEE International Conference On
Trust, Security And Privacy In Computing And Communications/12th IEEE International Conference On Big Data Science And Engineering
(TrustCom/BigDataSE). IEEE, 2018, pp. 465–474.
[12] U. M. Ismail, S. Islam, and H. Mouratidis, “Cloud security audit for migration and continuous monitoring,” in 2015 IEEE Trustcom/BigDataSE/ISPA, vol. 1.
IEEE, 2015, pp. 1081–1087.
[13] K. Parasuraman, P. Srinivasababu, S. R. Angelin, and T. A. M. Devi, “Secured document management through a third party auditor scheme in Cloud
Computing,” 2014 Int. Conf. Electron. Commun. Comput. Eng. ICECCE 2014, pp. 109–118, 2014.
[14] S. Han and J. Xing, “Ensuring data storage security through a novel third party auditor scheme in cloud computing,” in 2011 IEEE International
Conference on Cloud Computing and Intelligence Systems. IEEE, 2011, pp. 264–268.
[15] S. Mei, C. Liu, Y. Cheng, J. Wu, and Z. Wang, “Tetpa: A case for trusted third party auditor in cloud environment,” in IEEE Conference Anthology. IEEE,
2013, pp. 1–4.
[16] T. R¨ubsamen, C. Reich, M. Knahl, and N. Clarke, “An architecture for cloud accountability audits,” BW-CAR— SINCOM, p. 55, 2014.
[17] T. Ruebsamen and C. Reich, “Supporting cloud accountability by collecting evidence using audit agents,” in 2013 IEEE 5th International
Conference on Cloud Computing Technology and Science, vol. 1. IEEE, 2013, pp. 185–190.
[18] A. Bhagat and R. K. Sahu, “Cloud data security while using third party auditor,” International Journal of Computer Applications, vol. 70, no. 16, 2013.
[19] https://www.slideshare.net/CloudLightningProject/cloudlightning-simulator

7. The End ….
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