[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118042-en":3,"doc-seo-118042-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118042,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Data Privacy in the Digital Era - Machine Learning Solutions for Confidentiality","Data privacy has become critical in today’s digitally driven world as the volume of sensitive information grows rapidly across domains. This work examines advanced machine learning methods to strengthen confidentiality protections and provide proactive defense against cyber threats. By analyzing large datasets, machine learning models can help reveal vulnerabilities and likely breaches in near real time. The study addresses access control, encryption, and data anonymization, and highlights federated learning for privacy-preserving collaborative analysis. It also emphasizes ethics and compliance in balancing privacy with data utility.","Data Privacy in the Digital Era: Machine Learning Solutions for Confidentiality  \n1Dr. Sukhvinder Singh Dari, 2 * Dharmesh Dhabliya 3K Govindaraju, 4Anishkumar Dhablia, 5Prof. (Dr.) Parikshit N. Mahalle,  \n1Director, Symbiosis Law School, Nagpur Campus, Symbiosis International (Deemed University), Pune, India. Email: [director@slsnagpur.edu.in](director@slsnagpur.edu.in)  \n21Professor, Department of Information Technology, Vishwakarma Institute of Information Technology, Pune, Maharashtra, [India. Email: ](India. Email: dharmesh.dhabliya@viit.ac.in)[dharmesh.dhabliya@viit.ac.in](India. Email: dharmesh.dhabliya@viit.ac.in)[ ](India. Email: dharmesh.dhabliya@viit.ac.in)3Associate Professor, Dept ofCSE, Aditya Engineering College, Surampalem, India  \n4Engineering Manager, Altimetrik India Pvt Ltd, Pune, Maharashtra, India Email: [anishdhablia@gmail.com](anishdhablia@gmail.com)  \n5 Department of Artificial Intelligence & Data Science, Vishwakarma Institute of Information Technology, Pune, [INDIA. Email: parikshit.mahalle@viit.ac.in](INDIA. Email: parikshit.mahalle@viit.ac.in)  \nABSTRACT:Data privacy has grown to be of utmost importance intoday's digitally driven world. Protecting sensitive information has never been more important due to the explosion of data across many areas. This abstract explores cutting-edge machine learning techniques for improving data privacy in the digital age.Artificial intelligence's subset of machine learning presents a viable way to overcome issues with data privacy. This study investigates how machine learning algorithms can be used to strengthen confidentiality protections in a range of applications. Machine learning models may uncover vulnerabilities and potential breaches in realtime by analysing large information, offering proactive defence against cyber threats.We explore a number of data privacy topics, such as access control, encryption, and data anonymization, while emphasising how machine learning approaches might improve these procedures. We also cover how federated learning protects privacy during collaborative data analysis, enabling different parties to gain knowledge without jeopardising the integrity of the data.The importance of ethics and compliance in the creation and application of machine learning solutions for data confidentiality is also emphasised in this abstract. It highlights the necessity for ethical AI practises and highlights the difficulties in finding a balance between the preservation of privacy and the usefulness of data.This study investigates how machine learning could strengthen data confidentiality, paving the path for a more safe and considerate digital future. It highlights the value of interdisciplinary cooperation between data scientists, ethicists, and policymakers to fully utilise machine learning's  \npromise in protecting our sensitive information in the digital world.  \nKeywords: Machine Learning, Confidentiality, Data Privacy, Encryption  \n* Corresponding author Email: [dharmesh.dhabliya@viit.ac.in](dharmesh.dhabliya@viit.ac.in)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \n1. INTRODUCTION  \nData has become a crucial resource in the digital age that powers innovation, informs choices, and supports the operation of contemporary society. However, the prevalence of data has created hitherto unheard-of problems with respect to confidentiality and privacy. Protecting this data against unauthorised access, breaches, and exploitation has grown crucial as individuals and organisations generate and share enormous volumes of sensitive information [1] . This growing worry has prompted the creation and use of cutting-edge technologies, particularly machine learning, to strengthen data confidentiality. This introduction gives a general overview of the changing data pri","cbCaim5vXU1n8K4x","https://ap.wps.com/l/cbCaim5vXU1n8K4x","pdf",2240914,1,13,"English","en",105,"# Introduction\n## Data confidentiality and privacy challenges in the digital era\n## Role of machine learning in data privacy\n## Federated learning and privacy-preserving collaboration","[{\"question\":\"Why is data privacy especially important in the digital era?\",\"answer\":\"Digital transformation has increased the collection, storage, and sharing of sensitive data, making confidentiality and privacy risks more urgent. This attracts unauthorized access and exploitation by malicious actors and through accidental exposure.\"},{\"question\":\"How can machine learning improve data confidentiality protections?\",\"answer\":\"Machine learning can analyze large datasets to detect patterns and recognize possible threats quickly. It can also support stronger anonymization and other privacy-preserving approaches.\"},{\"question\":\"What is the privacy benefit of federated learning in collaborative settings?\",\"answer\":\"Federated learning trains models across distributed data sources while enabling secure information exchange. It helps parties gain insights without jeopardizing data integrity or exposing raw data.\"}]","Data Privacy in the Digital Era - Machine Learning Solutions for Confidentiality | PDF",1785680967,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"data-privacy-in-the-digital-era-machine-learning-solutions-for-confidentiality","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/data-privacy-in-the-digital-era-machine-learning-solutions-for-confidentiality/118042/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is data privacy especially important in the digital era?","Question",{"text":75,"@type":76},"Digital transformation has increased the collection, storage, and sharing of sensitive data, making confidentiality and privacy risks more urgent. This attracts unauthorized access and exploitation by malicious actors and through accidental exposure.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How can machine learning improve data confidentiality protections?",{"text":80,"@type":76},"Machine learning can analyze large datasets to detect patterns and recognize possible threats quickly. It can also support stronger anonymization and other privacy-preserving approaches.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the privacy benefit of federated learning in collaborative settings?",{"text":84,"@type":76},"Federated learning trains models across distributed data sources while enabling secure information exchange. 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