Journal of Generalized Lie Theory and Applications

Department of Mathematics, School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China

Publications

• Mini Review   
  Tackling Data Bottlenecks and Security Channel Challenges: A Comprehensive Analysis and Overview
  Author(s): Yaping Zhang*
  
  Based upon the data and assessment hypothesis, this paper proposes a "differential common data" (DMI) rule to defend the security insurance (PP). Algorithmically, DMI-ideal arrangements can be inferred by means of the Discriminant Part Investigation (DCA). In addition, DCA has two machine learning variants that are suitable for supervised learning applications—one in the kernel space and the other in the original space. CP unifies the conventional Information Bottleneck (IB) and Privacy Funnel (PF) and results in two constrained optimizers known as Generalized Information Bottleneck (GIB) and Generalized Privacy Funnel (GPF) by extending the concept of DMI to the utility gain and privacy loss. DCA can be further extended to a DUCA machine learning variant in supervised learning environments to achieve the best possible compromise between utility.. Read More»
  DOI: 10.37421/1736-4337.2024.18.427

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