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Title: “In Eighty Percent of the Cases, I Select the Password for Them”: Security and Privacy Challenges, Advice, and Opportunities at Cybercafes in Kenya
Cybercafes remain a popular way to access the Internet in the developing world as many users still lack access to personal computers. Coupled with the recent digitization of government services, e.g. in Kenya, many users have turned to cybercafes to access essential services. Many of these users may have never used a computer, and face significant security and privacy issues at cybercafes. Yet, these challenges as well as the advice offered remain largely unexplored. We investigate these challenges along with the security advice and support provided by the operators at cybercafes in Kenya through n = 36 semi-structured interviews (n = 14 with cybercafe managers and n = 22 with customers). We find that cybercafes serve a crucial role in Kenya by enabling access to printing and government services. However, most customers face challenges with computer usage as well as security and usability challenges with account creation and password management. As a workaround, customers often rely on the support and advice of cybercafe managers who mostly direct them to use passwords that are memorable, e.g. simply using their national ID numbers or names. Some managers directly manage passwords for their customers, with one even using the same password for all their customers. These results suggest the need for more awareness about phone-based password managers, as well as a need for computer training and security awareness among these users. There is also a need to explore security and privacy advice beyond Western peripheries to support broader populations  more » « less
Award ID(s):
1845300
NSF-PAR ID:
10417661
Author(s) / Creator(s):
Date Published:
Journal Name:
2023 IEEE Symposium on Security and Privacy
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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The breast corpus subset should be released by November 2021. By December 2021 we should also release the unannotated FCCC data. We are currently annotating urinary tract data as well. We expect to release about 5,600 processed TUH slides in this subset. We have an additional 53,000 unprocessed TUH slides digitized. Corpora of this size will stimulate the development of a new generation of deep learning technology. In clinical settings where resources are limited, an assistive diagnoses model could support pathologists’ workload and even help prioritize suspected cancerous cases. ACKNOWLEDGMENTS This material is supported by the National Science Foundation under grants nos. CNS-1726188 and 1925494. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. REFERENCES [1] N. Shawki et al., “The Temple University Digital Pathology Corpus,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York City, New York, USA: Springer, 2020, pp. 67 104. https://www.springer.com/gp/book/9783030368432. [2] J. Picone, T. Farkas, I. Obeid, and Y. Persidsky, “MRI: High Performance Digital Pathology Using Big Data and Machine Learning.” Major Research Instrumentation (MRI), Division of Computer and Network Systems, Award No. 1726188, January 1, 2018 – December 31, 2021. https://www. isip.piconepress.com/projects/nsf_dpath/. [3] A. Gulati et al., “Conformer: Convolution-augmented Transformer for Speech Recognition,” in Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH), 2020, pp. 5036-5040. https://doi.org/10.21437/interspeech.2020-3015. [4] C.-J. 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