[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118928-en":3,"doc-seo-118928-105":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},118928,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","User Profiling through Zero-Permission Sensors and Machine Learning - Master’s Thesis","With the rise of mobile and pervasive computing, user attention is fragmented and services increasingly compete to adapt to individual needs and interests. Traditional personalization often relies on account registration, where privacy and security concerns lead users to avoid data sharing or provide inaccurate profiles. This thesis investigates machine learning combined with zero-permission sensors to profile users without explicit input. An application collects gyroscope, accelerometer, and ambient light data, followed by feature selection, filtration, and homogenization, then trains and evaluates models. Results show biological gender can be predicted using 1-day intervals with a support vector machine.","American University in Cairo  \nAUC Knowledge Fountain  \n\n| Theses and Dissertations | Student Research |\n| --- | --- |\n| Spring 6-21-2023\u003Cbr>User Profiling through Zero-Permission Sensors and Learning\u003Cbr>Ahmed ElHussiny\u003Cbr>[aelhussiny@aucegypt.edu](aelhussiny@aucegypt.edu)\u003Cbr>Follow this and additional works at: [https://fount.aucegypt.edu/etds](https://fount.aucegypt.edu/etds)\u003Cbr> Part of the Other Computer Engineering Commons | Machine |\n\nRecommended Citation  \nAPA Citation  \nElHussiny, A. (2023) . User Profiling through Zero-Permission Sensors and Machine Learning [Master's Thesis, the American University in Cairo] . AUC Knowledge Fountain.  \n[https://fount.aucegypt.edu/etds/2156](https://fount.aucegypt.edu/etds/2156)  \nMLA Citation  \nElHussiny, Ahmed. User Profiling through Zero-Permission Sensors and Machine Learning. 2023. American University in Cairo, Master's Thesis. AUC Knowledge Fountain.  \n[https://fount.aucegypt.edu/etds/2156](https://fount.aucegypt.edu/etds/2156)  \nThis Master's Thesis is brought to you for free and open access by the Student Research at AUC Knowledge Fountain. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of AUC Knowledge Fountain. For more information, please contact [thesisadmin@aucegypt.edu](thesisadmin@aucegypt.edu).  \nGraduate Studies  \nUser Profiling through Zero-Permission Sensors and Machine  \nLearning  \nA THESIS SUBMITTED BY  \nAhmed Khalid ElHussiny  \nTO THE  \nDepartment of Computer Science and Engineering  \nSUPERVISED BY  \nProfessor Sherif Aly and Professor Tamer ElBatt  \n16/5/2023  \nin partial fulfillment of the requirements for the degree of  \nMaster of Computer Science  \nDeclaration of Authorship  \nI, Ahmed Khalid ElHussiny, declare that this thesis titled,“User Profiling through ZeroPermission Sensors and Machine Learning” and the work presented in it are my own. Iconfirm that:  \n• This work was done wholly or mainly while in candidature for a research degree at this University.  \n• Where any part of this thesis has previously been submitted for a degree or any other qualification at this University or any other institution, this has been clearly stated.  \n• Where I have consulted the published work of others, this is always clearly attributed.  \n• Where I have quoted from the work of others, the source is always given. With the exception of such quotations, this thesis is entirely my own work.  \n• I have acknowledged all main sources of help.  \n• Where the thesis is based on work done by myself jointly with others, I have made clear exactly what was done by others and what I have contributed myself.  \nSigned:  \nDate: 15/05/2023  \nSTUDENT TO INSERT HERE THE PAGE WITH THE SIGANTURES OF THE THESIS DEFENSE  \nCOMMITTEE  \nAbstract  \nWith the rise of mobile and pervasive computing, users are often ingesting content on the go. Services are constantly competing for attention in a very crowded field. It is only logical that users would allot their attention to the services that are most likely to adapt to their needs and interests. This matter becomes trivial when users create accounts and explicitly inform the services of their demographics and interests. Unfortunately, due to privacy and security concerns, and due to the fast nature of computing today, users see the registration process as an unnecessary hurdle to bypass, effectively refusing to provide services with personalization information. In other cases, they may provide inaccurate profile information, either due to lack of accuracy, or for malicious purposes. In this thesis, we use machine learning with zero-permission sensors to test the degree to which it can be used to effectively profile a user without necessitating any explicit input. We do so through first iterating through building an application that collects data from the following zeropermission sensors: the gyroscope, accelerometer, and ambient light sensor. Following that, we pass the data through a multi-step transformati","cbCaiaX3xkdjIrnd","https://ap.wps.com/l/cbCaiaX3xkdjIrnd","pdf",1576793,1,101,"English","en",105,"# Abstract\n# Acknowledgements\n# Contents\n## Declaration of Authorship\n## List of Figures","[{\"question\":\"What problem does the thesis address in user profiling?\",\"answer\":\"The thesis addresses how personalization is typically hindered when users avoid registration data due to privacy/security concerns or submit inaccurate profiles.\"},{\"question\":\"Which sensors and processing steps are used to build the profiling approach?\",\"answer\":\"The work collects data from gyroscope, accelerometer, and ambient light sensors, then applies feature selection, filtration, and homogenization before training.\"},{\"question\":\"What key result does the thesis report about predictive performance?\",\"answer\":\"It reports accurate prediction of a user’s biological gender using 1-day intervals with a support vector machine.\"}]","User Profiling through Zero-Permission Sensors and Machine Learning - 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