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 gain and privacy loss. Finally, a golden-section iterative method is developed specifically for the two constrained optimization problems in order to speed up convergence: GPF and GIB. Data bottleneck and security channel issues are critical challenges in data transmission and communication. Data bottleneck arises when the rate of data production or transfer exceeds the capacity of the communication channel or system. Security channel issues involve vulnerabilities that compromise the confidentiality, integrity, or availability of transmitted data. This article provides an overview of these challenges, their causes and implications. It discusses strategies for addressing data bottleneck, such as optimizing bandwidth, storage and processing capabilities. It also explores security measures, including authentication, encryption and intrusion detection, to mitigate security channel issues. A holistic approach integrating efficient data management and robust security practices is crucial for ensuring smooth and secure information flow. This paper investigates a Compressive Security (CP) philosophy for ideal tradeoff between utility increase and protection misfortune. CP addresses an aspect diminished subspace plan of ideally desensitized question that might be securely imparted to the general population. Based upon the data and assessment hypothesis, this paper proposes a "differential common data" (DMI) rule to defend the security insurance (PP).
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