ICCCA2016: Big Data Security Issues, Challenges, and Solutions

This report examines the multifaceted challenges associated with Big Data, including issues related to volume, velocity, veracity, and variety of data. It delves into the limitations of traditional database systems in handling and analyzing Big Data, covering aspects such as data representation, compression, lifecycle management, and security. The report highlights specific challenges in data processing, storage, and analysis, along with the need for effective online data analysis and machine learning techniques. Furthermore, it explores potential solutions, such as data mining techniques for preserving privacy, including anonymization, generalization, and randomization. The report also addresses the challenges of joining data from diverse sources and the security implications of data generated from social media and personal records. The assignment is based on the context of an International Conference on Computing, Communication and Automation (ICCCA2016) and MITS4003 Database Systems, emphasizing the importance of addressing these issues to harness the benefits of Big Data effectively.

ISSUES AND CHALLENGES
IN BIG DATA
The disadvantages of Big Data and their
Solution

THE CONCEPT OF COMPLEXITY IN BIG
DATA
The reasons for which the
complexity in Big Data arises to
create challenges and issues for
the data analysts:
Volume of the Big Data
Velocity of the Big Data
Veracity of the Big Data
Variety of the Big Data

CHALLENGES THAT THE TRADITIONAL
DATABASE SYSTEM FACE IN THE HANDLING OF
THE BIG DATA AND ITS ANALYSIS
 Representation of data
 Data Compression and Redundancy
Reduction
 Management of data Life Cycle
 Analytical Phases
 Confidentiality of the data
 Energy management
 Data scalability and Expandability
 Cooperation within the data analytics

CONTINUED…
 Capturing and Storage of Data
 Data Processing and Analysis
 Result Delivery (Hossain, Fotouhi and Hasan 2015)
 Data preparation, Quality and Trustworthiness
 Distributed Storage and Search
 Effective Online Data Analysis (Yi, Qin and Li 2015)
 Machine Learning and Big Data mining Techniques
 Efficient Handling of Big Data Streams
 Semantic Lifting Techniques
 Programming Models
 Social Analytics
 Full Data Analytics

SOLUTION TO THE PROBLEMS AND
ISSUES IN THE BIG DATA TECHNIQUE
The solution to this problem can be generated
as Data Mining techniques for preserving
privacy that aims to preserve the privacy of
the data depending on the type of data sets
generated. Following are the techniques:
 Anonymization technique
 Generalization of data
 Randomization of data
 Probabalistic or no results for queries

ISSUES RELATED TO THE BIG DATA
TECHNIQUES
 Joining data from different sources
 Providing context for the joining of data from
different sources (Chen 2017)
 Data generated from the social media,
medical records, and data containing the
personal and private information of
individuals (Diamantoulakis, Kapinas and
Karagiannidis 2015).

REFERENCES
 Chen, E.T., 2017. The Internet of Things: Opportunities,
Issues, and Challenges. In The Internet of Things in the
Modern Business Environment (pp. 167-187). IGI Global.
 Diamantoulakis, P.D., Kapinas, V.M. and Karagiannidis,
G.K., 2015. Big data analytics for dynamic energy
management in smart grids. Big Data Research, 2(3),
pp.94-101.
 Hossain, M.M., Fotouhi, M. and Hasan, R., 2015, June.
Towards an analysis of security issues, challenges, and
open problems in the internet of things. In Services
(SERVICES), 2015 IEEE World Congress on (pp. 21-28).
IEEE.
 Yi, S., Qin, Z. and Li, Q., 2015, August. Security and
privacy issues of fog computing: A survey. In International
conference on wireless algorithms, systems, and
applications (pp. 685-695). Springer, Cham.