Achieving data privacy before publishing has been becoming an extreme concern of researchers, individuals and service providers. A novel methodology, Cooperative Privacy Game (CoPG), has been proposed to achieve data privacy in which Cooperative Game Theory is used to achieve the privacy and is named as Cooperative Privacy (CoP). The core idea of CoP is to play the best strategy for a player to preserve his privacy by himself which in turn contributes to preserving other players privacy. CoP considers each tuple as a player and tuples form coalitions as described in the procedure. The main objective of the CoP is to obtain individuals (player) privacy as a goal that is rationally interested in other individuals’ (players) privacy. CoP is formally defined in terms of Nash equilibria, i.e., all the players are in their best coalition, to achieve k-anonymity. The cooperative values of the each tuple are measured using the characteristic function of the CoPG to identify the coalitions. As the underlying game is convex; the algorithm is efficient and yields high quality coalition formation with respect to intensity and disperse. The efficiency of anonymization process is calculated using information loss metric. The variations of the information loss with the parameters $$\alpha$$
α
(weight factor of nearness) and $$\beta$$
β
(multiplicity) are analyzed and the obtained results are discussed.
Generally, for a machine learning model to perform well, the data instances on which the model is being trained have to be relevant to the use case. In the case of relevant samples not being available, Zero-shot learning can be used to perform classification tasks. Zero-shot learning is the process of solving a problem when there are no examples of that problem in the phase of training. It lets us classify target classes on which the deep learning model has not been trained.In this article, Zero-shot learning is used to classify food dish classes through an object recognition model. First, the data is collected from Google Images and Kaggle. The image attributes are then extracted using a VGG16 model. The image attributes belonging to the training categories are then used to train a custom-built deep learning model. Various hypermeters of the model are tuned and the results are analyzed in order to get the best possible performance. The image attributes extracted from the zero-shot learning categories are used to test the model after the training process is completed. The predictions are made by comparing the vectors of the target class with the training classes in the Word2Vec space. The metric used to evaluate the model is Top-5 accuracy which indicates whether the expected result is present in the predictions. A Top-5 accuracy of 92% is achieved by implementing zero-shot learning for the classification of unseen food dish images.
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