Blockchain Based Information Security and Privacy Protection: Challenges and Future Directions using Computational Literature Review

• URL: http://arxiv.org/abs/2409.14472v1
• Date: Sun, 22 Sep 2024 14:41:43 GMT
• Title: Blockchain Based Information Security and Privacy Protection: Challenges and Future Directions using Computational Literature Review
• Authors: Gauri Shankar, Md Raihan Uddin, Saddam Mukta, Prabhat Kumar, Shareeful Islam, A. K. M. Najmul Islam,
• Abstract summary: blockchain technology has gained immense popularity in enhancing individual security and privacy. Rapid proliferation of published research articles presents challenges for manual analysis and synthesis. We identify 10 topics related to security and privacy and provide a detailed description of each topic.
• Score: 1.3864583085700581
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Blockchain technology is an emerging digital innovation that has gained immense popularity in enhancing individual security and privacy within Information Systems (IS). This surge in interest is reflected in the exponential increase in research articles published on blockchain technology, highlighting its growing significance in the digital landscape. However, the rapid proliferation of published research presents significant challenges for manual analysis and synthesis due to the vast volume of information. The complexity and breadth of topics, combined with the inherent limitations of human data processing capabilities, make it difficult to comprehensively analyze and draw meaningful insights from the literature. To this end, we adopted the Computational Literature Review (CLR) to analyze pertinent literature impact and topic modelling using the Latent Dirichlet Allocation (LDA) technique. We identified 10 topics related to security and privacy and provided a detailed description of each topic. From the critical analysis, we have observed several limitations, and several future directions are provided as an outcome of this review.

Related papers

• Model Inversion Attacks: A Survey of Approaches and Countermeasures [59.986922963781]
  Recently, a new type of privacy attack, the model inversion attacks (MIAs), aims to extract sensitive features of private data for training. Despite the significance, there is a lack of systematic studies that provide a comprehensive overview and deeper insights into MIAs. This survey aims to summarize up-to-date MIA methods in both attacks and defenses.
  arXiv  Detail & Related papers  (2024-11-15T08:09:28Z)
• Automating Bibliometric Analysis with Sentence Transformers and Retrieval-Augmented Generation (RAG): A Pilot Study in Semantic and Contextual Search for Customized Literature Characterization for High-Impact Urban Research [2.1728621449144763]
  Bibliometric analysis is essential for understanding research trends, scope, and impact in urban science. Traditional methods, relying on keyword searches, often fail to uncover valuable insights not explicitly stated in article titles or keywords. We leverage Generative AI models, specifically transformers and Retrieval-Augmented Generation (RAG), to automate and enhance bibliometric analysis.
  arXiv  Detail & Related papers  (2024-10-08T05:13:27Z)
• A Survey on Personalized Content Synthesis with Diffusion Models [57.01364199734464]
  PCS aims to customize the subject of interest to specific user-defined prompts. Over the past two years, more than 150 methods have been proposed. This paper offers a comprehensive survey of PCS, with a particular focus on the diffusion models.
  arXiv  Detail & Related papers  (2024-05-09T04:36:04Z)
• A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures [50.987594546912725]
  Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures.
  arXiv  Detail & Related papers  (2024-03-31T12:44:48Z)
• Fine-Grained Zero-Shot Learning: Advances, Challenges, and Prospects [84.36935309169567]
  We present a broad review of recent advances for fine-grained analysis in zero-shot learning (ZSL) We first provide a taxonomy of existing methods and techniques with a thorough analysis of each category. Then, we summarize the benchmark, covering publicly available datasets, models, implementations, and some more details as a library.
  arXiv  Detail & Related papers  (2024-01-31T11:51:24Z)
• Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models [52.24001776263608]
  This comprehensive survey delves into the recent strides in HS moderation. We highlight the burgeoning role of large language models (LLMs) and large multimodal models (LMMs) We identify existing gaps in research, particularly in the context of underrepresented languages and cultures.
  arXiv  Detail & Related papers  (2024-01-30T03:51:44Z)
• Tackling Cyberattacks through AI-based Reactive Systems: A Holistic Review and Future Vision [0.10923877073891446]
  This paper presents a comprehensive survey of recent advancements in AI-driven threat response systems. The most recent survey covering the AI reaction domain was conducted in 2017. A total of seven research challenges have been identified, pointing out potential gaps and suggesting possible areas of development.
  arXiv  Detail & Related papers  (2023-12-11T09:17:01Z)
• A Survey on Detection of LLMs-Generated Content [97.87912800179531]
  The ability to detect LLMs-generated content has become of paramount importance. We aim to provide a detailed overview of existing detection strategies and benchmarks. We also posit the necessity for a multi-faceted approach to defend against various attacks.
  arXiv  Detail & Related papers  (2023-10-24T09:10:26Z)
• Resilience of Deep Learning applications: a systematic literature review of analysis and hardening techniques [3.265458968159693]
  The review is based on 220 scientific articles published between January 2019 and March 2024. The authors adopt a classifying framework to interpret and highlight research similarities and peculiarities.
  arXiv  Detail & Related papers  (2023-09-27T19:22:19Z)
• A Comprehensive Analysis of the Role of Artificial Intelligence and Machine Learning in Modern Digital Forensics and Incident Response [0.0]
  The goal is to look closely at how AI and ML techniques are used in digital forensics and incident response. This endeavour digs far beneath the surface to unearth the intricate ways AI-driven methodologies are shaping these crucial facets of digital forensics practice. Ultimately, this paper underscores the significance of AI and ML integration in digital forensics, offering insights into their benefits, drawbacks, and broader implications for tackling modern cyber threats.
  arXiv  Detail & Related papers  (2023-09-13T16:23:53Z)
• SoK: Blockchain Solutions for Forensics [8.185918509343818]
  This paper provides an overview and classification of the available blockchain-based digital forensic tools. We also offer an analysis of the various benefits and challenges of the symbiotic relationship between blockchain technology and the current digital forensics approaches.
  arXiv  Detail & Related papers  (2020-05-26T11:43:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.