[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120199-en":3,"doc-seo-120199-105":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},120199,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Managing Privacy Challenges in Digital Services and Machine Learning","Companies’ data-driven digital services depend on collecting personal data and processing it with self-learning algorithms to deliver personalized experiences. Intensive data collection can shift users’ sense of control, raising significant privacy concerns, intensified by high-profile breaches such as the Cambridge Analytica scandal. As customers become more reluctant to share data, algorithm performance may decline, reducing the quality of data-driven services. The dissertation aims to balance stronger privacy protection with improved value creation, asking how firms can mitigate the dilemma between individual privacy and enhanced digital services.","Managing Privacy Challenges in Digital Services  \nand Machine Learning  \nDepartment of Law and Economics at the Technical University of Darmstadt  \nApproved  \nDissertation  \nsubmitted by  \nAnne Zöll, M. Sc.  \nfor the award of the academic degree Doctor rerum politicarum (Dr. rer. pol.)  \nFirst Assessor: Prof. Dr. Peter Buxmann Second Assessor: Prof. Dr. Dr. Christian Reuter  \nDarmstadt 2024  \nZöll, Anne: Managing Privacy Challenges in Digital Services and Machine Learning  \nDarmstadt, Technische Universität Darmstadt Dissertation published on TUprints in the year 2024 Date of the viva voce: November 20, 2024  \nPublished under CC BY-NC-ND 4.0 International  \n[https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)  \nDeclaration of Authorship  \nI hereby declare that the submitted dissertation is my own work. All quotes, whether word byword or in my own words, have been marked as such.  \nThe dissertation has not been published anywhere else nor presented to any other examination board.  \nIch erkläre hiermit ehrenwörtlich, dass ich die vorliegende Arbeit selbstständig angefertigthabe. Sämtliche aus fremden Quellen direkt oder indirekt übernommenen Gedanken sind als solche kenntlich gemacht.  \nDie Arbeit wurde bisher weder einer anderen Prüfungsbehörde vorgelegt noch veröffentlicht.  \nAnne Zöll  \nDarmstadt, 03.06.2024  \nAcknowledgements  \nAlthough research can be thrilling and fulfilling when achieving results, especially when recognized and respected by peers, the journey to these outcomes is often exhausting and lengthy. Gratefully, a multitude of remarkable individuals have supported and accompanied me along this journey, and I am sincerely grateful for their assistance.  \nFirst and foremost, I am deeply grateful to my supervisor, Professor Peter Buxmann, for his invaluable support. His guidance has not only provided opportunities for my professional growth, such as conference participations and publications, but also allowed me the freedom to pursue my research interests. His confidence in my abilities has been a source of motivation. I would also like to thank my co-supervisor Professor Christian Reuter for his expertise and support in the completion of my dissertation.  \nI would like to extend my heartfelt appreciation to my colleagues for their continuous exchange of ideas, unwavering support, critical discussions, and thought-provoking insights. Many of my colleagues have become dear friends, and I am grateful for their camaraderie.  \nI owe a special debt of gratitude to Professor Ofir Turel for his exceptional mentorship and the invaluable research experience he provided me with. His guidance has profoundly shaped my academic journey, offering insights into international research and motivating me to learn and grow. I am also thankful for his invitation to the University of Melbourne, which expanded my horizons.  \nI am indebted to my husband for his unwavering support throughout this process. He has not only been a pillar of strength but has also been my rock, providing endless encouragement, understanding, and love. His belief in me has been a guiding light, pushing me to strive for excellence even in the face of challenges.  \nLastly, I am sincerely grateful to my parents, my brother, and my grandma for their lifelong support, understanding, and constructive questioning of my decisions. I wouldn't bethe person I am today without them. I want to extend my heartfelt thanks to my friends, who accompanied me on this journey, bringing joy, laughter, and exiting moments into my life.  \nAbstract  \nCompanies‘ data-driven digital services rely on the collection of personal data and its processing by self-learning algorithms. With the help of machine learning, companies can offer personalized services tailored to customer needs. As a result of the intensive collection of personal information by companies, customers have a sense of loss of control over their own personal information. They also have high privacy","cbCaikDfPDqVDtha","https://ap.wps.com/l/cbCaikDfPDqVDtha","pdf",5908915,1,198,"English","en",105,"# Abstract\n## Background and research objective\n## Research question and scope\n## Perspectives and privacy-enhancing technologies","[{\"question\":\"What problem does the dissertation address regarding privacy in data-driven digital services?\",\"answer\":\"It addresses the tension between protecting individuals’ privacy and maintaining strong data-driven service performance, given that privacy concerns reduce customers’ willingness to share data.\"},{\"question\":\"How does machine learning relate to the privacy dilemma described in the abstract?\",\"answer\":\"Machine learning enables personalized services using collected personal data, but intensive collection and processing can increase users’ privacy concerns, which can undermine algorithm performance and service quality.\"},{\"question\":\"Which three perspectives does the dissertation examine?\",\"answer\":\"It examines the issue from technological, individual, and organizational perspectives, including why privacy-enhancing technologies are often not adopted in data-driven digital services.\"}]","Managing Privacy Challenges in Digital Services and Machine Learning | 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