The recognition of data as a natural resource has made headlines in the new era of industrialization. Companies now leverage this resource to enhance their services and products, often promising optimal outcomes. While data has always held significant value, recent advancements in AI and ML frameworks have brought this fact to the forefront. However, it was not until major scandals involving large corporations came to light that the critical issue of privacy was remembered. Consequently, the intersection of data, AI and ML frameworks, and privacy has emerged as a new area of research. Although numerous works have reviewed the development of various data protection techniques, it seems that most of them address the subject from a single perspective or attribute the entire concept of data privacy to a specific technique. This review aims to present an overarching view of the topic. It offers a systematic guideline that establishes a proper connection among the three elements: data, AI & ML frameworks, and privacy. The paper delves into each element from both an abstract and concrete standpoint, presenting the latest techniques to address data privacy concerns, including numerous lab simulations. It also recommends tools and resources for further study. Ultimately, it wraps up the central topic by outlining the challenges and prospective future research directions. Keywords: Data Privacy; AI & ML; Differential Privacy; Federated Learning;

This paper presents a new method for solving a classification problem; the  BireyselValue method assumes that the individual traits of a class help to classify an observation based on similarity measures. The method involves three stages to solve the classification problem:  the building stage, the training stage, and the prediction stage. The first two stages accomplish two key steps: firstly, five parameters are used to transform any observation of size \(n\) variables into six variables; secondly, subsets of the individual traits of each class are created. As a result, the parameters, the subsets of the individual traits, and a scaled version of the training dataset are saved as a predictive model. Ultimately, the prediction stage uses the elements in the predictive model to transform the observations that are to be classified and of size \(n\) into the size of six variables and to perform similarity measures between the observation and the individual traits of class to make the final prediction. The experimental results obtained on 6 multiclass datasets from different domains showed that the proposed method is efficient at solving classification problems. Moreover, the method can potentially be used for purposes other than solving a classification problem.Keywords: BireyselValue Method, Classification, Prediction, Dimension Reduction