Clustering based Privacy Preserving of Big Data using Fuzzification and Anonymization Operation

Abstract

Big Data is used by data miner for analysis purpose which may contain sensitive information. During the procedures it raises certain privacy challenges for researchers. The existing privacy preserving methods use different algorithms that results into limitation of data reconstruction while securing the sensitive data. This paper presents a clustering based privacy preservation probabilistic model of big data to secure sensitive information..model to attain minimum perturbation and maximum privacy. In our model, sensitive information is secured after identifying the sensitive data from data clusters to modify or generalize it.The resulting dataset is analysed to calculate the accuracy level of our model in terms of hidden data, lossed data as result of reconstruction. Extensive experiements are carried out in order to demonstrate the results of our proposed model. Clustering based Privacy preservation of individual data in big data with minimum perturbation and successful reconstruction highlights the significance of our model in addition to the use of standard performance evaluation measures.

Privacy‐preserving data mining and machine learning in healthcare: Applications, challenges, and solutions

“…Zhou et al (2015) proposed medical text mining. Sensitive attributes based on medical data clustering were developed by S. Khan et al (2020). T. Y. Wu et al (2019) proposed a method for data sanitization for preparing clinical datasets.…”

SABPP: Privacy-Preserving Data Exchange in The Big Data Market Using The Smart Contract Approach

“…Though the PABIDOT excels in execution speed, scalability, attack resistance and accuracy in large-scale privacy-preserving data classification it requires more storage area. In (15) preferred the clustering-based privacy preservation probabilistic model to preserve the privacy of the big data. The clustering method provides better privacy over sanitization-based methods.…”

“…Big data is defined as the massive quantity of organized and unorganized data, which is increased by 2.5 Exabytes per day (1) . Social media such as Twitter and Facebook, mobile data, YouTube, file hosting websites, digital cameras, GPS signals, healthcare data and some popular web services are responsible for the rapid enhancement in the data volume.…”

Data Anonymization through Collaborative Multi-view Microaggregation

“…This algorithm is similar to the previous k-member clustering algorithms [5] but with the constraint of maximizing the dissimilarity of sensitive data values (privacy) and minimizing the similarity of the quasi-identifiers (usefulness). Those algorithms gave an opening to further studies on anonymization using clustering [21].…”

“…The method itself is interesting and was widely studied [3,26,27,30], what gave a strong basis to further works on anonymization. Since the k-anonymity is a group based method, clustering was considered as one of its strongest assets [21,32]. Microaggregating k elements and replacing the data by the group representatives gives a good trade-off between the information loss and the potential data identification risk [5].…”