Big Data Security and Privacy: Opportunities, Challenges and Solutions

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Cybersecurity".

Deadline for manuscript submissions: closed (30 December 2021) | Viewed by 10042

Special Issue Editor

School of Computing & Mathematics, Charles Sturt University, Bathurst 2795, Australia
Interests: computational intelligence; cyber security; big data; electronic fraud (efraud) and threats; IoT security; human dimensions of cybersecurity; social media security

Special Issue Information

Dear Colleagues,

The amount of big data is growing exponentially due to various technological advancements and applications, such as the Internet of Things (IoT), smart health and other smart technology systems, etc. According to an IDC report, the total volume of digital data will grow by 61% by 2025.

Due to the scale, complexity, and heterogeneity of big data and the environment in which they operate, it is impossible for commonly used technology to create, manage, and process such data in an efficient and timely manner. Similarly, the critical functions of ensuring security and privacy of such data also cannot be achieved using common techniques. Security requirements, such as access control, threat filtering, etc. take on a new dimension in the context of big data. Among other requirements, scalability and high performance are crucial for many big data security and privacy solutions.

This Special Issue aims to present cutting-edge research addressing opportunities, challenges, and solutions concerning all aspects of big data security and privacy. Original and unpublished high-quality research findings are solicited to explore topics including but not limited to:

  • Big data system security;
  • Cryptography in big data security and privacy;
  • Big data forensics;
  • Privacy preserving big data applications, systems, and services;
  • Security and privacy of big databases;
  • Threat and attack prediction using big data analytics;
  • Management of big data security;
  • Big data security models and architectures;
  • Big data policy and standard;
  • Human dimensions of big data security;
  • Trust management in big data systems;
  • Big data security and privacy issues in emergent technologies;
  • Big data security and privacy challenges and solutions.

Dr. Maumita Bhattacharya
Guest Editor

Manuscript Submission Information

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Keywords

  • Big data security challenges and solutions
  • Big data privacy challenges and solutions
  • Management of big data security and privacy
  • Big data security models and architectures
  • Human dimensions of big data security

Published Papers (3 papers)

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Research

12 pages, 303 KiB  
Article
Improved Classification of Blockchain Transactions Using Feature Engineering and Ensemble Learning
by Chandrashekar Jatoth, Rishabh Jain, Ugo Fiore and Subrahmanyam Chatharasupalli
Future Internet 2022, 14(1), 16; https://0-doi-org.brum.beds.ac.uk/10.3390/fi14010016 - 28 Dec 2021
Cited by 3 | Viewed by 3227
Abstract
Although the blockchain technology is gaining a widespread adoption across multiple sectors, its most popular application is in cryptocurrency. The decentralized and anonymous nature of transactions in a cryptocurrency blockchain has attracted a multitude of participants, and now significant amounts of money are [...] Read more.
Although the blockchain technology is gaining a widespread adoption across multiple sectors, its most popular application is in cryptocurrency. The decentralized and anonymous nature of transactions in a cryptocurrency blockchain has attracted a multitude of participants, and now significant amounts of money are being exchanged by the day. This raises the need of analyzing the blockchain to discover information related to the nature of participants in transactions. This study focuses on the identification for risky and non-risky blocks in a blockchain. In this paper, the proposed approach is to use ensemble learning with or without feature selection using correlation-based feature selection. Ensemble learning yielded good results in the experiments, but class-wise analysis reveals that ensemble learning with feature selection improves even further. After training Machine Learning classifiers on the dataset, we observe an improvement in accuracy of 2–3% and in F-score of 7–8%. Full article
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17 pages, 3484 KiB  
Article
A Data Sharing Scheme for GDPR-Compliance Based on Consortium Blockchain
by Yangheran Piao, Kai Ye and Xiaohui Cui
Future Internet 2021, 13(8), 217; https://0-doi-org.brum.beds.ac.uk/10.3390/fi13080217 - 21 Aug 2021
Cited by 18 | Viewed by 3628
Abstract
After the General Data Protection Regulation (GDPR) was introduced, some organizations and big data companies shared data without conducting any privacy protection and compliance authentication, which endangered user data security, and were punished financially for this reason. This study proposes a blockchain-based GDPR [...] Read more.
After the General Data Protection Regulation (GDPR) was introduced, some organizations and big data companies shared data without conducting any privacy protection and compliance authentication, which endangered user data security, and were punished financially for this reason. This study proposes a blockchain-based GDPR compliance data sharing scheme, aiming to promote compliance with regulations and provide a tool for interaction between users and service providers to achieve data security sharing. The zero-knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARK) algorithm is adopted for protecting data and ensure that the user’s private data can satisfy the individual requirements of the service provider without exposing user data. The proposed scheme ensures mutual authentication through the Proof of Authority consensus based on the Committee Endorsement Mechanism (CEM-PoA), and prevents nodes from doing evil using the reputation incentive mechanism. Theoretical analysis and performance comparison indicate that the scheme meets the confidentiality, availability, and other indicators. It has superiority in efficiency and privacy protection compared with other schemes. Full article
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13 pages, 330 KiB  
Article
Secure Internal Data Markets
by Peter Kieseberg, Sebastian Schrittwieser and Edgar Weippl
Future Internet 2021, 13(8), 208; https://0-doi-org.brum.beds.ac.uk/10.3390/fi13080208 - 12 Aug 2021
Viewed by 2011
Abstract
The data market concept has gained a lot of momentum in recent years, fuelled by initiatives to set up such markets, e.g., on the European level. Still, the typical data market concept aims at providing a centralised platform with all of its positive [...] Read more.
The data market concept has gained a lot of momentum in recent years, fuelled by initiatives to set up such markets, e.g., on the European level. Still, the typical data market concept aims at providing a centralised platform with all of its positive and negative side effects. Internal data markets, also called local or on-premise data markets, on the other hand, are set up to allow data trade inside an institution (e.g., between divisions of a large company) or between members of a small, well-defined consortium, thus allowing the remuneration of providing data inside these structures. Still, while research on securing global data markets has garnered some attention throughout recent years, the internal data markets have been treated as being more or less similar in this respect. In this paper, we outline the major differences between global and internal data markets with respect to security and why further research is required. Furthermore, we provide a fundamental model for a secure internal data market that can be used as a starting point for the generation of concrete internal data market models. Finally, we provide an overview on the research questions we deem most pressing in order to make the internal data market concept work securely, thus allowing for more widespread adoption. Full article
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