A Review of Big Data Security and Privacy Protection Technology

Lv, Denglong; Zhu, Shibing; Xu, Huazheng; Liu, Ran

doi:10.1109/icct.2018.8600051

Cited by 6 publications

(9 citation statements)

References 54 publications

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“…In data storage phase, encryption is addressed in all studies excluding [80][81][82][83], and access control in storage phase is addressed in all studies excluding [79,80,84]. In addition, the studies addressed audit trail are [78,82,[85][86][87]. In the data analytics phase, technologies related to privacy-preserving data mining are investigated in all studies excluding [87,88], and the studies addressed access control is [78].…”

Section: Discussionmentioning

confidence: 99%

“…In the data analytics phase, technologies related to privacy-preserving data mining are investigated in all studies excluding [87,88], and the studies addressed access control is [78]. The studies addressed the audit trail in the data utilization phase are [78,84], and the studies addressed privacy-preserving data publishing-related technologies are [80,81,84,86,87]. Techniques related to data destruction such as degaussing, and overwriting are not covered in all survey studies.…”

Section: Discussionmentioning

confidence: 99%

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Security and Privacy in Big Data Life Cycle: A Survey and Open Challenges

2020

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The use of big data in various fields has led to a rapid increase in a wide variety of data resources, and various data analysis technologies such as standardized data mining and statistical analysis techniques are accelerating the continuous expansion of the big data market. An important characteristic of big data is that data from various sources have life cycles from collection to destruction, and new information can be derived through analysis, combination, and utilization. However, each phase of the life cycle presents data security and reliability issues, making the protection of personally identifiable information a critical objective. In particular, user tendencies can be analyzed using various big data analytics, and this information leads to the invasion of personal privacy. Therefore, this paper identifies threats and security issues that occur in the life cycle of big data by confirming the current standards developed by international standardization organizations and analyzing related studies. In addition, we divide a big data life cycle into five phases (i.e., collection, storage, analytics, utilization, and destruction), and define the security taxonomy of the big data life cycle based on the identified threats and security issues.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Security and Privacy in Big Data Life Cycle: A Survey and Open Challenges

2020

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“…Generally, big data can be defined as the answer to the question as to how difficult it is to handle big datasets, with different forms, including structured, semi-structured, and unstructured, which are difficult to handle using traditional data analysis techniques [3], [16]. The other definition comes from ''what big data is,'' which focuses on a data processing technology architecture to capture and analyze valuable information from large and varied datasets [3]. These two definitions represent the five dimensions of big data (Volume, Variety, Velocity, Veracity, and Value) [17], [18], also referred to as the ''5Vs'', which are described as follows:…”

Section: Background a Big Data Technologymentioning

confidence: 99%

“…management will be more complicated. Accordingly, in big data schemes, it is hard to apply the traditional asymmetric, and symmetric encryption techniques directly [3]. In the case of cloud storage, due to large scale volume, significant increment, and rapid changes, encrypting big datasets directly will raise the risk of secret key management and require a considerable computation overhead [4].…”

Section: Introductionmentioning

confidence: 99%

Towards Big Data Security Framework by Leveraging Fragmentation and Blockchain Technology

2022

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The world is facing a growth in the amount and variety of data generated by both users and machines. Despite the exponential increases, the tools and technologies developed to manage these data volumes are not intended to meet security and data protection requirements. Additionally, most of the current big data security systems are offered by a centralized third party, which is vulnerable to many security threats. Blockchain technology plays a significant role by addressing modern technology concerns such as decentralization, non-tampering, trust, data ownership, and traceability, making it great potential to protect personal information. This research presents a new big data security solution empowered by blockchain technology and incorporates fragmentation, encryption, and access control techniques. Our proposed fragmentation algorithm takes into account the data owner's demand for encryption to be added to the fragmentation process. Furthermore, data fragments will be stored in the distributed manner offered by the big data environment, resulting in an additional layer of data protection. In order to achieve an optimal security solution, we aim to enhance big data security with acceptable overhead and avoid the encryption overhead for non-sensitive and low-sensitive data portions. We present the results of our implemented techniques to highlight that the overheads (in terms of computation time) introduced by our solution are negligible relative to its security and privacy gains.

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“…Credibility, availability, data privacy, confidentiality, authentication and integrity are a few of the security and privacy issues associated with big data [3], [4], and [26]. For instance, the authors in [27] proposed a 3D security model for big data that is based on user roles, data processes as well as security requirements. According to the authors, these requirements define the security objectives and goals that should be achieved to preserve data security and privacy.…”

Section: Related Workmentioning

confidence: 99%

Knowledge Extraction and Data Visualization: A Proposed Framework for Secure Decision Making using Data Mining

Alshareef¹,

Majrashi²,

Helal³

et al. 2021

IJACSA

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The decision-making process, promptly on time, is a crucial success factor in large organizations. Generally, the data warehouses of these organizations grow rapidly with the data generated from various business activities. This huge volume of data needs to be analyzed and decisions must be made quickly to meet the market challenges. Accurate knowledge extraction and its visualization from big data can guide decisionmakers to conduct key analysis and make correct predictions. This paper proposes a decision-making framework that not only takes into account knowledge extraction and visualization but also considers the security of the data. The proposed framework uses data mining techniques to extract useful patterns, then, visualizes those patterns for further analysis and decision making. The significance of the proposed framework lies in the mechanism through which it protects the data from intruders. The data is first processed and then stored in an encrypted format on the cloud. When the data is needed for analysis and decision making, a temporary copy of the data is first decrypted, and then important patterns are visualized. The proposed framework will assist managers and other decision-makers to analyze and visualize the data in real-time with an enhanced security mechanism.

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A Review of Big Data Security and Privacy Protection Technology

Cited by 6 publications

References 54 publications

Security and Privacy in Big Data Life Cycle: A Survey and Open Challenges

Security and Privacy in Big Data Life Cycle: A Survey and Open Challenges

Towards Big Data Security Framework by Leveraging Fragmentation and Blockchain Technology

Knowledge Extraction and Data Visualization: A Proposed Framework for Secure Decision Making using Data Mining

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