Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Anonymization Based Deep Privacy Preserving Convolutional Autoencoder Learning Technique for High Dimensional Data Clustering in Big Data Cloud Subject The topic of the resource Advanced Networking; Anonymization; Big Data Clustering; Deep Learning Architecture; High Dimensional Big Data; Reconstruction Error Description An account of the resource Data Clustering is a primary research focus in large data-driven application domains in the big data cloud as performance of the clustering dynamic data with high dimensionality is highly challenging due to major concern in the effectiveness and efficiency on data representation. Machine learning is a conventional approach to distribute the data into soft partition still it leads to increasing sparsity of data and increasing difficulties in distinguishing distance between data points. In addition, securing the personnel and confidential information of the user is also becoming vital. In order to tackle those issues, a new anonymization based deep privacy preserving learning paradigm has been presented in this paper. The proposed model is represented as deep privacy preserving convolutional auto encoder learning architecture for secure high dimensional data clustering on inferring the distribution of the data over time. Initially dimensionality reduction and feature extraction is carried out and those extracted feature has been taken for clustering on generation of objective function to produce maximum margin cluster. Those clusters are further fine tuned to feature refinement on the hyper parameter of various layers of deep learning model network to establish the minimum reconstruction error by feature refinement. Softmax layer minimizes the intra cluster similarity and inter cluster variation in the feature space for cluster assignment. Hyper parameter tuning using stochastic gradient descent has been enabled in the output layer to make the data instance in the cluster to be close to each other by determining the affinity of the data on new representation. It results significant increase in the clustering performance on the discriminative informations. Detailed experimental analysis has been performed on benchmarks datasets to compute the proposed model performance with conventional approaches. The performance outcome represents that anonymization based deep privacy preserving clustering learning architecture can produce good scalability and effectiveness on high dimensional data. 2023 American Institute of Physics Inc.. All rights reserved. Creator An entity primarily responsible for making the resource Kiruthika B.; Srinivasan B.; Poornima N.V.; Prabhusundhar P. Source A related resource from which the described resource is derived AIP Conference Proceedings, Vol-2909, No. 1 Publisher An entity responsible for making the resource available American Institute of Physics Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1063/5.0183809" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0183809</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179877724&amp;doi=10.1063%2F5.0183809&amp;partnerID=40&amp;md5=96ae1d7a6261b97d8dc50453f13beb6f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179877724&amp;doi=10.1063%2f5.0183809&amp;partnerID=40&amp;md5=96ae1d7a6261b97d8dc50453f13beb6f</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 0094243X; ISBN: 978-073544750-9 | CS; 2023-20234; Vol-1; 00261-00262 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kiruthika B., Gobi Arts &amp; Science College, Gobichettipalayam, India; Srinivasan B., Gobi Arts &amp; Science College, Gobichettipalayam, India; Poornima N.V., Christ University, Bangalore, India; Prabhusundhar P., Gobi Arts &amp; Science College, Gobichettipalayam, India