Abstract
With the recent development of artificial intelligence, the field of human-computer interaction (HCI) has received an increasing amount of attention. Among all topics under HCI, machine learning and privacy concerns are two of the most important ones. One of the machine learning techniques that possess a strong connection with privacy is federated learning, which distributes learning tasks to individual devices to reduce information sharing and protect privacy. In this study, a systematic literature review of our topic has been conducted using various tools including Harzing’s Publish or Perish, Google nGram, Vicinitas, VOSviewer, CiteSpace, BibExcel, and maxQDA. The software Mendeley is also used to help sort out citation items. Trend analysis, co-citation analysis, content analysis, and cluster analysis have been conducted to identify the most important articles in the literature. It has been found that the two topics have been well studied and together they have a variety of applications, including communication networks, the healthcare industry, and the Internet of Things. Finally, we discuss the potential future work beyond our topics which cover directions in application fields such as healthcare and finance, and other machine learning techniques developed based on federated learning.
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He, J., Cao, T., Duffy, V.G. (2023). Machine Learning Techniques and Privacy Concerns in Human-Computer Interactions: A Systematic Review. In: Degen, H., Ntoa, S., Moallem, A. (eds) HCI International 2023 – Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol 14059. Springer, Cham. https://doi.org/10.1007/978-3-031-48057-7_23
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