Privacy-Preserving Frequent Pattern Mining from Big Uncertain Data

Leung, Carson K.; Hoi, Calvin S. H.; Pazdor, Adam G. M.; Wodi, Bryan H.; Cuzzocrea, Alfredo

doi:10.1109/bigdata.2018.8622260

Cited by 29 publications

(3 citation statements)

References 47 publications

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“…The method utility is shown through location datasets derived from geo-referenced social media. In [173], an item-centric algorithm mines frequent patterns from big uncertain data. Pattern mining aims to discover implicit and potentially useful information.…”

Section: Data Privacymentioning

confidence: 99%

Tactful Networking: Humans in the Communication Loop

Costa

Viana

Ziviani

et al. 2021

IEEE Trans. Emerg. Top. Comput. Intell.

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Data Privacymentioning

confidence: 99%

Tactful Networking: Humans in the Communication Loop

Costa

Viana

Ziviani

et al. 2021

IEEE Trans. Emerg. Top. Comput. Intell.

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“…Moreover, some data are quite detailed (e.g., "on January 23, a 56-year old male presented to Sunnybrook Health Sciences Centre in Toronto with a new onset of fever and non-productive cough following return from Wuhan, China, the day prior" [38]). Some other data are more abstract and general (e.g., "on Week 3-i.e., the third full week-of 2020, a male in his 50s-who was transmitted through international travel-in the province of Ontario showed symptoms of fever and cough"), for preserving the privacy [39][40][41][42] of the individuals.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and OLAP on Big COVID-19 Data

Leung

Chen

Hoi

et al. 2020

2020 IEEE International Conference on Big Data (Big Data)

Self Cite

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In the current technological era, huge amounts of big data are generated and collected from a wide variety of rich data sources. These big data can be of different levels of veracity in the sense that some of them are precise while some others are imprecise and uncertain. Embedded in these big data are useful information and valuable knowledge to be discovered. An example of these big data is healthcare and epidemiological data such as data related to patients who suffered from epidemic diseases like the coronavirus disease 2019 (COVID-19). Knowledge discovered from these epidemiological data-via data science techniques such as machine learning, data mining, and online analytical processing (OLAP)-helps researchers, epidemiologists and policy makers to get a better understanding of the disease, which may inspire them to come up ways to detect, control and combat the disease. In this paper, we present a machine learning and big data analytic tool for processing and analyzing COVID-19 epidemiological data. Specifically, the tool makes good use of taxonomy and OLAP to generalize some specific attributes into some generalized attributes for effective big data analytics. Instead of ignoring unknown or unstated values of some attributes, the tool provides users with flexibility of including or excluding these values, depending on their preference and applications. Moreover, the tool discovers frequent patterns and their related patterns, which help reveal some useful knowledge such as absolute and relative frequency of the patterns. Furthermore, the tool learns from the patterns discovered from historical data and predicts useful information such as clinical outcomes for future data. As such, the tool helps users to get a better understanding of information about the confirmed cases of COVID-19. Although this tool is designed for machine learning and analytics of big epidemiological data, it would be applicable to machine learning and analytics of big data in many other real-life applications and services.

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“…Suppose, for example, that bread is the most prominent thing in retail transaction databases and that the integer is most commonly contained in the transformed database, the server may assume that the integer is bread. (C. K. Leung et al, 2018) Paper-a privacy protection-centered element-centered algorithm that produces frequent patterns from largely unknown data. (Y.…”

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confidence: 99%

Privacy-Preserving in Multi-Party Data Release on Cloud for Big Data Using Fusion Learning

Dangi¹

2021

ITII

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The data volume increases every day and the next wave of apps cannot be envisaged without the created and executed data-driven algorithms. In this post, we undertook an extensive survey on privacy issues in the context of big data. At every point of the Big Data life cycle, we explored privacy challenges and discussed some of the advantages and disadvantages of the Big Data application of new privacy conservation schemes. Much progress has been made in protecting the protection of consumers from data production to data storage, but many transparent questions and hurdles exist.

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Privacy-Preserving Frequent Pattern Mining from Big Uncertain Data

Cited by 29 publications

References 47 publications

Tactful Networking: Humans in the Communication Loop

Tactful Networking: Humans in the Communication Loop

Machine Learning and OLAP on Big COVID-19 Data

Privacy-Preserving in Multi-Party Data Release on Cloud for Big Data Using Fusion Learning

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