Visible to the public De-identification and Privacy Issues on Bigdata Transformation

TitleDe-identification and Privacy Issues on Bigdata Transformation
Publication TypeConference Paper
Year of Publication2020
AuthorsLee, H., Cho, S., Seong, J., Lee, S., Lee, W.
Conference Name2020 IEEE International Conference on Big Data and Smart Computing (BigComp)
Date PublishedFeb. 2020
ISBN Number978-1-7281-6034-4
KeywordsBig Data, big data privacy, Big Data transformation, controllable privacy level, Data analysis, Data models, data privacy, de-identification processes, diseases, government sectors, Guidelines, Human Behavior, ICT industry, Industries, k-anonymity, l-Diversity, Metrics, personal information, privacy, privacy attacks, pubcrawl, resilience, Resiliency, Scalability, t-closeness

As the number of data in various industries and government sectors is growing exponentially, the `7V' concept of big data aims to create a new value by indiscriminately collecting and analyzing information from various fields. At the same time as the ecosystem of the ICT industry arrives, big data utilization is treatened by the privacy attacks such as infringement due to the large amount of data. To manage and sustain the controllable privacy level, there need some recommended de-identification techniques. This paper exploits those de-identification processes and three types of commonly used privacy models. Furthermore, this paper presents use cases which can be adopted those kinds of technologies and future development directions.

Citation Keylee_-identification_2020