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Fig. 1 Big data life cycle stages of big data life cycle, i.e., data generation, storage, and processing are shown.
These mechanisms can be grouped based on the stages of big data life cycle [7] Fig. 1, i.e., data generation, storage, and processing.
There have been a number of privacy-preserving mechanisms developed for privacy protection at different stages (for example, data generation, data storage, and data processing) of a big data life cycle.
Privacy challenges in each phase of big data life cycle [7] are presented along with the advantages and disadvantages of existing privacy-preserving technologies in the context of big data applications.
In this paper, we suggest a model that combines the phases presented in [20] and phases mentioned in [21], in order to provide encompass policies and mechanisms that ensure addressing threats and attacks in each step of big data life cycle.
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Fig. 1 Big data security life cycle in healthcare Data collection phase This is the obvious first step.
With the increasing use of private vehicles with positioning services, GPS trajectory data of vehicles has become one of the major sources of big data about urban life.
Our future work plans to fully implement user's (gaming) application and to evaluate transaction systems using a real life big data set in the experimentation.
Persistent homology provides a new approach for the topological simplification of big data via measuring the life time of intrinsic topological features in a filtration process and has found its success in scientific and engineering applications.
Anything else a life around Big Data is teaching him?
Health Linkages uses a combination of blockchain and big data to enable healthcare and life science institutions to trust, protect and share their data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com