More Web Proxy on the site http://driver.im/

research-article

Big Data: : Trade-off between Data Quality and Data Security

Authors:

A. ABOU EL KALAM,

N. ELMARZOUQIAuthors Info & Claims

Volume 151, Issue C

Pages 916 - 922

https://doi.org/10.1016/j.procs.2019.04.127

Published: 01 January 2019 Publication History

Abstract

The essence of an information system lies in the data; if it is not of good quality or not sufficiently protected, the consequences will undoubtedly be harmful. Quality and Security are two essential aspects that add value to data and their implementation has become a real need and must be adopted before any data exploitation. Due to the high volume of data, their diversity and their rapid generation, effective implementation of such systems requires well thought out mechanisms and strategies. This paper provides an overview of Data Quality and Data Security in a Big Data context. We want through this paper to highlight the conflicts that may exist during the implementation of data security and data quality management systems. Such a conflict makes the complexity greater and requires new adapted solutions.

References

[1]

Batini C, Cappiello C, Francalanci C, Maurino A., Methodologies for Data Quality Assessment and Improvement, ACM Computing Surveys (2009) 41.

[2]

Crosby PB, Quality is free, McGraw-Hill, New York, 1979.

[3]

ISO, ISO/IEC 25012:2008—Software engineering. Software product quality requirements and evaluation (SQuaRE). Data quality model, Report, International Organization for Standarization 2009

[4]

Laranjeiro N, Soydemir SN, Bernardino J. A Survey on Data Quality: Classifying Poor Data. IEEE 21st Pacific Rim International Symposium on Dependable Computing 2015; 179-188

[5]

Bertot JC, Choi H. Big Data and e-Government: Issues, Policies, and Recommendations. The Proceedings of the 14th Annual International Conference on Digital Government Research 2014

[6]

Müller H, Freytag JC. Problems, Methods, and Challenges in Comprehensive Data Cleansing. Technical Report HUB-IB-164, Humboldt University, Berlin 2003

[7]

Redman TC. Data’s Credibility Problem. Harvard Business Review 2013

[8]

Cappiello C, Francalanci C, Pernici B. Data quality assessment from the user’s perspective. The 2004 international workshop on Information quality in information systems 2004; 68-73

[9]

Bizer C. Quality-driven information filtering in the context of web-based information systems. Ph.D. Thesis. Freie Universit, Berlin 2007

[10]

Merino J, Caballero I, Rivas B, Serrano M, Piattini M, A Data Quality in Use model for Big Data, Future Generation Computer Systems (2015).

[11]

Cai L, Zhu Y. The, Challenges of Data Quality and Data Quality Assessment in the Big Data Era, Data Science Journal (2015) 1–10.

[12]

Chen M, Mao S, Liu Y, Big Data: A Survey, Springer Science+Business Media, 2014.

[13]

Barna S, Divesh S. Data Quality: The other Face of Big Data. IEEE, the 30th International Conference on Data Engineering 2014

[14]

Katal A, Wazid M, Goudar RH. Big Data: Issues, Challenges, Tools and Good. IEEE, the 6th International Conference on Contemporary Computing (IC3) 2013

[15]

Strong DM, Yang WL, Wang RY, Data uamity in context, Communications of the ACM 41 (1997) 103–110.

[16]

ISO, ISO/IEC 27001:2013-Information technology -- Security techniques -- Information security management systems -- Requirements, International Organization for Standarization 2013

[17]

Hakuta K, Sato H. Cryptographic, Technology for Benefiting from Big Data, Springer, The Impact of Applications on Mathematics (2014) 85–95.

[18]

Alladi BS, Prasad S., Big data life cycle: security issues, challenges, threat and security model, International Journal of Engineering & Technology (2018).

[19]

Sudarsam SD, Jetley RP, Ramaswamy S., Security and Privacy of Big Data, Big Data: A Primer (2015) 121–136.

[20]

Bertino E, Big Data – Security and Privacy, IEEE International Congress on Big Data, 2015.

[21]

Xu L, Shi W. Security, Theories and Practices for Big Data, Springer International Publishing Switzerland (2016) 157–192.

[22]

Terzi DS, Terzi R, Sagiroglu S. A Survey on Security and Privacy Issues in Big Data. IEEE, The 10th International Conference for Internet Technology and Secured Transactions 2015

[23]

ISO, ISO/IEC 25010, Systems and software engineering— Systems and software quality requirements and evaluation (square)—System and software quality models 2011

[24]

Wang RY, Reddy MP, Kon HB, Toward quality data: an attribute-based approach, Decision Support Systems (DSS) 13 (1995) 349–372.

[25]

Wang RY, Strong DM, Beyond Accuracy: What Data Quality Means to Data Consumers, Journal of Management Information Systems 12 (1996) 5–33.

Digital Library

[26]

Li J, Chen X, Li M, Li J, Lee PPC, Lou W. Secure, Deduplication with Efficient and Reliable Convergent Key Management, IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 25 (2014).

Cited By

Goknil ANguyen PSen SPolitaki DNiavis HPedersen KSuyuthi AAnand AZiegenbein A(2023)A Systematic Review of Data Quality in CPS and IoT for Industry 4.0ACM Computing Surveys10.1145/359304355:14s(1-38)Online publication date: 17-Jul-2023
https://dl.acm.org/doi/10.1145/3593043
Zibak ASauerwein CSimpson A(2022)Threat Intelligence Quality Dimensions for Research and PracticeDigital Threats: Research and Practice10.1145/34842023:4(1-22)Online publication date: 10-Mar-2022
https://dl.acm.org/doi/10.1145/3484202
Toapanta SAstudillo KGallegos L(2020)Appropriate Security Algorithms to Mitigate the Risks in a Database of the National Customs Service of EcuadorProceedings of the 2020 the 4th International Conference on Information System and Data Mining10.1145/3404663.3404669(89-95)Online publication date: 15-May-2020
https://dl.acm.org/doi/10.1145/3404663.3404669

Index Terms

Big Data: Trade-off between Data Quality and Data Security

Index terms have been assigned to the content through auto-classification.

Recommendations

A Data Quality in Use model for Big Data

Beyond the hype of Big Data, something within business intelligence projects is indeed changing. This is mainly because Big Data is not only about data, but also about a complete conceptual and technological stack including raw and processed data, ...
Big data and big risk: a four-factor framework for big data security and privacy

Big data refers to a very large volume of data with possibly varied and complex structure. With growing data processing and data analytic techniques, big data provides significant benefits to organisations and individuals by improving productivity and ...
Relating Big Data and Data Quality in Financial Service Organizations
Challenges and Opportunities in the Digital Era
Abstract
Today’s financial service organizations have a data deluge. A number of V’s are often used to characterize big data, whereas traditional data quality is characterized by a number of dimensions. Our objective is to investigate the complex ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Procedia Computer Science

Procedia Computer Science Volume 151, Issue C

2019

1275 pages

ISSN:1877-0509

EISSN:1877-0509

Issue’s Table of Contents

The Author(s).

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 January 2019

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 03 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Goknil ANguyen PSen SPolitaki DNiavis HPedersen KSuyuthi AAnand AZiegenbein A(2023)A Systematic Review of Data Quality in CPS and IoT for Industry 4.0ACM Computing Surveys10.1145/359304355:14s(1-38)Online publication date: 17-Jul-2023
https://dl.acm.org/doi/10.1145/3593043
Zibak ASauerwein CSimpson A(2022)Threat Intelligence Quality Dimensions for Research and PracticeDigital Threats: Research and Practice10.1145/34842023:4(1-22)Online publication date: 10-Mar-2022
https://dl.acm.org/doi/10.1145/3484202
Toapanta SAstudillo KGallegos L(2020)Appropriate Security Algorithms to Mitigate the Risks in a Database of the National Customs Service of EcuadorProceedings of the 2020 the 4th International Conference on Information System and Data Mining10.1145/3404663.3404669(89-95)Online publication date: 15-May-2020
https://dl.acm.org/doi/10.1145/3404663.3404669

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents