TY - GEN
T1 - Secure Agents for Supporting Best-Balanced Multilingual Communication
AU - Pituxcoosuvarn, Mondheera
AU - Nakaguchi, Takao
AU - Lin, Donghui
AU - Ishida, Toru
N1 - Funding Information:
Acknowledgments. This research was partially supported by a Grant-in-Aid for Scientific Research (A) (17H00759, 2017–2020) and (B) (18H03341, 2018–2020) from Japan Society for the Promotion of Science (JSPS). We thank Prof. Masayuki Abe for providing insight and expertise in secure computation used in this research.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - There are technologies that support intercultural collaboration by allowing people to communicate more easily across the barriers of culture and language. However, sometimes user-sensitive information needs to be accessed. In best-balanced machine translation, a method that recommends the languages and machine translation services that should be used to assist multilingual group communication, user test scores must be disclosed to generate the language recommendations. There are various methods that can protect the data (test scores) and methods that allow simple statistic calculations, however, no existing method supports the complex calculations needed by the best-balanced machine translation method. This paper emphasizes the importance of user privacy in intercultural collaboration. We provide the initial idea and show how user test scores can be protected while supporting the recommendation system. We introduce a detailed example to discuss the design of a suitable user interface.
AB - There are technologies that support intercultural collaboration by allowing people to communicate more easily across the barriers of culture and language. However, sometimes user-sensitive information needs to be accessed. In best-balanced machine translation, a method that recommends the languages and machine translation services that should be used to assist multilingual group communication, user test scores must be disclosed to generate the language recommendations. There are various methods that can protect the data (test scores) and methods that allow simple statistic calculations, however, no existing method supports the complex calculations needed by the best-balanced machine translation method. This paper emphasizes the importance of user privacy in intercultural collaboration. We provide the initial idea and show how user test scores can be protected while supporting the recommendation system. We introduce a detailed example to discuss the design of a suitable user interface.
KW - Multilingual communication
KW - Secured implementation
KW - User privacy
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U2 - 10.1007/978-3-030-49913-6_32
DO - 10.1007/978-3-030-49913-6_32
M3 - Conference contribution
AN - SCOPUS:85088742415
SN - 9783030499129
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 376
EP - 388
BT - Cross-Cultural Design. Applications in Health, Learning, Communication, and Creativity - 12th International Conference, CCD 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings
A2 - Patrick Rau, Pei-Luen
PB - Springer
T2 - 12th International Conference on Cross-Cultural Design, CCD 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020
Y2 - 19 July 2020 through 24 July 2020
ER -