Differential Privacy Preserving Using TensorFlow DP-SGD and 2D-CNN for Large-Scale Image Data

Abstract

Abstract: Although smart wearables have many potential advantages, their widespread and ongoing use raises a number of privacy issues and difficult information security challenges. This article, present a thorough analysis of current wearable sensorbased big data analytics applications that protect user privacy. We draw attention to the fundamental aspects of privacy and security for applications on wearable technology. Then, we look at how deep learning techniques like 2D CNN are used for better evaluation and privacy preservation as well as for differential privacy of Tensor flow. DP-SGD (Differentially private stochastic gradient descent)The metrics are epsilon as well as accuracy, with 0.56 epsilon and 85.17% accuracy for three epochs and 100.09 epsilon and 95.28 accuracies for twenty epochs, respectively. Model training Accuracy is 94.71%. Also, present a case study on privacy-preserving machine learning techniques. Herein, we theoretically and empirically evaluate the privacypreserving deep learning framework's performance. We explain the implementation details of a case study of a secure prediction service using the 2D convolutional neural network (2D CNN) model