Empirical Assessment of Privacy-Preserving Techniques in Big Data Analytics
DOI:
https://doi.org/10.29070/qnkj2h53Keywords:
privacy-preserving, data analytics, big data, multiple healthcare, filtering techniqueAbstract
A privacy-preserving paradigm for large data data mining is proposed in this research. Two potential use cases have been defined according to the suggested architecture. The first is a collaborative filtering method that protects patients' privacy and is used to generate recommendations in a healthcare system where patients' data is randomly dispersed across different locations. When data is handled quickly and the information it contains can be used as a basis for decisions, the true benefit of big data may be realized in today's data-driven world. Using data mining techniques, valuable insights and patterns have been uncovered inside massive databases. Aside from the potential benefits to analytics, there are a number of security concerns that might arise from making all of this data available to data miners. This is because bad actors could potentially abuse this data. Therefore, it is important to strike a balance when it comes to the availability and security of data, as it is necessary to protect critical information without compromising application performance.
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