Outsourcing analyses on privacy-protected multivariate categorical data stored in untrusted clouds

Domingo-Ferrer, Josep; Sánchez, David; Ricci, Sara; Muñoz-Batista, Mónica

doi:10.1007/s10115-019-01424-4

Cited by 2 publications

(2 citation statements)

References 37 publications

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“…A brief report summarizes the research conducted by several scientists in the field of sickness prognosis. According to [9], the primary goal of the data security model is to detect attacks in the rail transportation sector. An expert attack detection system known as the BAS was developed to detect assaults and reduce their impact on the subway's environment control subsystem.…”

Section: Literature Reviewmentioning

confidence: 99%

“…s , σ (b) the auditor verifies the correctness of ρ (b) . It verified the indices and coefficients by the value with T as using the equation: Third, the auditor verifies the proof ρ (b) by checking the following equation: (9) The auditor verifies that the challenged data blocks are intact if the equation holds. Auditor saves a log entry to document their auditing of the data: A random is chosen by the client from the subset B of indices of Bitcoin blocks and transmitted to the auditor.…”

Section: Store Protocolmentioning

confidence: 99%

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Development of a cloud-assisted classification technique for the preservation of secure data storage in smart cities

et al. 2023

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Cloud computing is the most recent smart city advancement, made possible by the increasing volume of heterogeneous data produced by apps. More storage capacity and processing power are required to process this volume of data. Data analytics is used to examine various datasets, both structured and unstructured. Nonetheless, as the complexity of data in the healthcare and biomedical communities grows, obtaining more precise results from analyses of medical datasets presents a number of challenges. In the cloud environment, big data is abundant, necessitating proper classification that can be effectively divided using machine language. Machine learning is used to investigate algorithms for learning and data prediction. The Cleveland database is frequently used by machine learning researchers. Among the performance metrics used to compare the proposed and existing methodologies are execution time, defect detection rate, and accuracy. In this study, two supervised learning-based classifiers, SVM and Novel KNN, were proposed and used to analyses data from a benchmark database obtained from the UCI repository. Initially, intrusions were detected using the SVM classification method. The proposed study demonstrated how the novel KNN used for distance capacity outperformed previous studies. The accuracy of the results of both approaches is evaluated. The results show that the intrusion detection system (IDS) with a 98.98% accuracy rate produces the best results when using the suggested system.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Store Protocolmentioning

confidence: 99%