[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120991-en":3,"doc-seo-120991-105":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},120991,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","AI-Driven Anonymization - Protecting Personal Data Privacy While Leveraging Machine Learning - Abstract","Artificial intelligence accelerates data-driven services but increases exposure risks for personal information, leading to serious privacy and security concerns. This work centers on protecting personal data privacy by promoting anonymization while enabling machine-learning-based analysis. A differential privacy approach is used for privacy protection and detection, and existing machine learning privacy challenges are reviewed. Dataset-related factors influencing detection and protection are analyzed to support timely, effective personal data privacy safeguarding.","AI-Driven Anonymization: Protecting Personal Data Privacy While Leveraging Machine Learning  \nLe Yang1*& Miao Tian1  \nComputer Information Science,Sam Houston State University, Huntsville，TX，USA,[wesleyyang96@gmail.com](wesleyyang96@gmail.com)[ ](wesleyyang96@gmail.com)Master of Science in Computer Science,San Fransisco Bay University , Fremont CA, USA,[miao.hnlk@gmail.com](miao.hnlk@gmail.com)[ ](miao.hnlk@gmail.com)Duan Xin2  \nAccounting,Sun Yat-Sen University,HongKong,[duanxin12314057@gmail.com](duanxin12314057@gmail.com)[ ](duanxin12314057@gmail.com)Qishuo Cheng3  \nDepartment of Economics,University of Chicago, Chicago, IL, USA,[qishuoc@uchicago.edu](qishuoc@uchicago.edu)[ ](qishuoc@uchicago.edu)Jiajian Zheng4  \nBachelor of Engineering, Guangdong University of Technology,ShenZhen, CN,[im.jiajianzheng@gmail.com](im.jiajianzheng@gmail.com)  \nAbstract: The development of artificial intelligence has significantly transformed people's lives. However, it has also posed a significant threat to privacy and security, with numerous instances of personal information being exposed online and reports of criminal attacks and theft. Consequently, the need to achieve intelligent protection of personal information through machine learning algorithms has become a paramount concern. Artificial intelligence leverages advanced algorithms and technologies to effectively encrypt and anonymize personal data, enabling valuable data analysis and utilization while safeguarding privacy. This paper focuses on personal data privacy protection and the promotion of anonymity as its core research objectives. It achieves personal data privacy protection and detection through the use of machine learning's differential privacy protection algorithm. The paper also addresses existing challenges in machine learning related to privacy and personal data protection, offers improvement suggestions, and analyzes factors impacting datasets to enable timely personal data privacy detection and protection.  \nCCS Concept:Choose Relevance:Security and privacy• Database and storage security•Database activity monitoring  \nAdditional Keywords and Phrases: Machine learning; Differential privacy algorithm; Personal data protection; Drive anonymization  \n1 These authors contributed equally to this work and should be considered co-first authors.  \n1 INTRODUCTION  \n\"Artificial intelligence technology is constantly being iterated and applied to more and more industries. Generative AI, which can create text and chat with users, presents a unique challenge because it can make people feel like they're interacting with a human. Anthropomorphism is the ascription of human attributes or personality to nonhumans. People often anthropomorphize artificial intelligence (especially Generative AI) because it can create human-like outputs. Among them, information transmission activities based on artificial intelligence technology have received more and more attention. With the help of artificial intelligence technology to obtain information and transmit information, it can be more convenient and accelerate the realization of information interaction, industry marketing, user interaction, brand publicity, and advertising, and create more creative content. Artificial intelligence technology has brought great changes and more availability to everyone's daily life and receiving information channels. However, the collection of personal data is more and more extensive, which also makes the problem of personal data privacy and security more serious. Therefore, combined with the double-sided nature of artificial intelligence, this paper analyzes the advantages and disadvantages of intelligent data processing in personal data privacy, applies the machine learning differential privacy algorithm combined with intelligent data processing to the research, and realizes the risk prediction and protection of personal data. This serves as a reminder for everyone on how to use artificial intelligence to protect ","cbCaiudWX3gWbWkz","https://ap.wps.com/l/cbCaiudWX3gWbWkz","pdf",789975,1,9,"English","en",105,"# 1 INTRODUCTION\n# 2 MACHINE LEARNING AND PRIVACY PROTECTION\n## 2.1 Machine learning\n## 2.2 Privacy protection based on machine learning","[{\"question\":\"Why is personal data privacy a growing concern in AI systems?\",\"answer\":\"AI-based information processing enables broader and more extensive collection of personal data, increasing the likelihood of exposure and misuse, alongside security incidents such as attacks and theft.\"},{\"question\":\"How does the paper protect personal data privacy using machine learning?\",\"answer\":\"It focuses on anonymization as a core objective and uses a machine-learning differential privacy protection approach to achieve privacy protection and detection.\"},{\"question\":\"What privacy-related research directions are discussed for machine learning?\",\"answer\":\"The document outlines two main lines—federated learning with homomorphic encryption, and perturbation methods represented by differential privacy—then considers challenges and future research directions.\"}]","AI-Driven Anonymization - Protecting Personal Data Privacy While Leveraging Machine Learning - Abstract | PDF",1785733214,23,{"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},"ai-driven-anonymization-protecting-personal-data-privacy-while-leveraging-machine-learning-abstract","",{"@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/ai-driven-anonymization-protecting-personal-data-privacy-while-leveraging-machine-learning-abstract/120991/",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","2026-08-03",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 personal data privacy a growing concern in AI systems?","Question",{"text":75,"@type":76},"AI-based information processing enables broader and more extensive collection of personal data, increasing the likelihood of exposure and misuse, alongside security incidents such as attacks and theft.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper protect personal data privacy using machine learning?",{"text":80,"@type":76},"It focuses on anonymization as a core objective and uses a machine-learning differential privacy protection approach to achieve privacy protection and detection.",{"name":82,"@type":73,"acceptedAnswer":83},"What privacy-related research directions are discussed for machine learning?",{"text":84,"@type":76},"The document outlines two main lines—federated learning with homomorphic encryption, and perturbation methods represented by differential privacy—then considers challenges and future research directions.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]