Big data analytics for security and criminal investigations

Pramanik, Manojit; Lau, Raymond Y. K.; Yue, Wei Thoo; Ye, Yunming; Li, Chunping

doi:10.1002/widm.1208

Cited by 36 publications

(11 citation statements)

References 81 publications

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“…For the case of big data, data mining task is deployed by running some parallel programming, dimensionality reduction, and distributed tools, such as MapReduce. Recently, researchers give significant attention to handle the volume and velocity of big data by upgrading the scope of MapReduce and extending the real-time nature of high-volume data processing units (Pramanik et al, 2017). For obtaining the statistics to solve or optimize model parameters, the training data are scanned using existing data mining algorithms.…”

Section: Privacy-preserving Methods For Big Datamentioning

confidence: 99%

Privacy preserving big data analytics: A critical analysis of state‐of‐the‐art

Pramanik

Lau

Hossain

et al. 2020

WIREs Data Min & Knowl

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In the era of “big data,” a huge number of people, devices, and sensors are connected via digital networks and the cross‐plays among these entities generate enormous valuable data that facilitate organizations to innovate and grow. However, the data deluge also raises serious privacy concerns which may cause a regulatory backlash and hinder further organizational innovation. To address the challenge of information privacy, researchers have explored privacy‐preserving methodologies in the past two decades. However, a thorough study of privacy preserving big data analytics is missing in existing literature. The main contributions of this article include a systematic evaluation of various privacy preservation approaches and a critical analysis of the state‐of‐the‐art privacy preserving big data analytics methodologies. More specifically, we propose a four‐dimensional framework for analyzing and designing the next generation of privacy preserving big data analytics approaches. Besides, we contribute to pinpoint the potential opportunities and challenges of applying privacy preserving big data analytics to business settings. We provide five recommendations of effectively applying privacy‐preserving big data analytics to businesses. To the best of our knowledge, this is the first systematic study about state‐of‐the‐art in privacy‐preserving big data analytics. The managerial implication of our study is that organizations can apply the results of our critical analysis to strengthen their strategic deployment of big data analytics in business settings, and hence to better leverage big data for sustainable organizational innovation and growth. This article is categorized under: Commercial, Legal, and Ethical Issues > Security and Privacy Fundamental Concepts of Data and Knowledge > Big Data Mining Fundamental Concepts of Data and Knowledge > Data Concepts

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Section: Privacy-preserving Methods For Big Datamentioning

confidence: 99%

Privacy preserving big data analytics: A critical analysis of state‐of‐the‐art

Pramanik

Lau

Hossain

et al. 2020

WIREs Data Min & Knowl

View full text Add to dashboard Cite

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“…In a similar view, M. I. Pramanik et al [9] see the Big Data Analytics and its applications as a potential for effective resolution of complex issues, including criminal analysis. Most law enforcement and government agencies need to think about adopting Big Data analytics techniques like Data Mining to cope with the large volume of data from a variety of data sources.…”

Section: Law Enforcement and Information Technologiesmentioning

confidence: 99%

A mobile crowd sensing framework for suspect investigation: An objectivity analysis and de-identification approach

et al. 2020

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The ubiquity of mobile devices and their advanced features have increased the use of crowdsourcing in many areas, such as the mobility in the smart cities. With the advent of high-quality sensors on smartphones, online communities can easily collect and share information. These information are of great importance for the institutions, which must analyze the facts by facilitating the data collecting on crimes and criminals, for example. This paper proposes an approach to develop a crowdsensing framework allowing a wider collaboration between the citizens and the authorities. In addition, this framework takes advantage of an objectivity analysis to ensure the participants' credibility and the information reliability, as law enforcement is often affected by unreliable and poor quality data. In addition, the proposed framework ensures the protection of users' private data through a de-identification process. Experimental results show that the proposed framework is an interesting tool to improve the quality of crowdsensing information in a government context.

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“…For instance, professional sectoral media sources can be useful for up-to-date business analytics and marketing research. Broader media and news sources are often used when an analysis has a goal to identify on-the-spot patterns and deviations like criminal networks activity (Pramanik et al 2017 ) or stock price fluctuations due to newsworthy events (Wang et al 2019 , Nassirtoussi et al 2015 ). Various monitoring research can benefit from constructing an automated tracking of text mining indicators based on digital media data, such as sustainability indicators developed by Juventia et al ( 2020 ) and Rivera et al ( 2014 ), or economic policy uncertainty index with high predictive power proposed by Yono et al ( 2019 ) and Tobback et al ( 2018 ).…”

Section: Literature Reviewmentioning

confidence: 99%