TY - GEN
T1 - Understanding the Behavior Transparency of Voice Assistant Applications Using the ChatterBox Framework
AU - Natatsuka, Atsuko
AU - Iijima, Ryo
AU - Watanabe, Takuya
AU - Akiyama, Mitsuaki
AU - Sakai, Tetsuya
AU - Mori, Tatsuya
N1 - Funding Information:
A part of this work was supported by JSPS Grant-in-Aid for Scientific Research, Grant Number 19H04111.
Publisher Copyright:
© 2022 ACM.
PY - 2022/10/26
Y1 - 2022/10/26
N2 - A voice assistant (VA) is a platform that provides users with a wide range of services via interaction with a voice application using verbal commands. Since the VA application is deployed in the cloud, its behavior is not transparent to the user, which raises privacy concerns. In this study, we developed a framework called ChatterBox, which attempts to analyze VA applications via extensive continuous interaction, to understand their behavior. ChatterBox is capable of parsing and generating dialogues by utilizing natural language processing approach. It can also parse application-level messages to understand how a VA app acquires personal information. ChatterBox supports English and Japanese, which are completely different languages, and can extract more than twice as many dialogues from VA applications compared to SkillExplorer, a state-of-the-art VA dialogue analysis system. Based on analyses of English and Japanese VA applications using ChatterBox, we revealed that 5-15% of VA applications collect personal information or recorded user identifiers in a non-transparent manner, and 76-94% applications collected personal information without providing appropriate privacy policies. In light of these findings, we discuss the implementation of a highly transparent VA application platform.
AB - A voice assistant (VA) is a platform that provides users with a wide range of services via interaction with a voice application using verbal commands. Since the VA application is deployed in the cloud, its behavior is not transparent to the user, which raises privacy concerns. In this study, we developed a framework called ChatterBox, which attempts to analyze VA applications via extensive continuous interaction, to understand their behavior. ChatterBox is capable of parsing and generating dialogues by utilizing natural language processing approach. It can also parse application-level messages to understand how a VA app acquires personal information. ChatterBox supports English and Japanese, which are completely different languages, and can extract more than twice as many dialogues from VA applications compared to SkillExplorer, a state-of-the-art VA dialogue analysis system. Based on analyses of English and Japanese VA applications using ChatterBox, we revealed that 5-15% of VA applications collect personal information or recorded user identifiers in a non-transparent manner, and 76-94% applications collected personal information without providing appropriate privacy policies. In light of these findings, we discuss the implementation of a highly transparent VA application platform.
UR - http://www.scopus.com/inward/record.url?scp=85142518620&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85142518620&partnerID=8YFLogxK
U2 - 10.1145/3545948.3545970
DO - 10.1145/3545948.3545970
M3 - Conference contribution
AN - SCOPUS:85142518620
T3 - ACM International Conference Proceeding Series
SP - 143
EP - 159
BT - Proceedings of 25th International Symposium on Researchin Attacks, Intrusions and Defenses, RAID 2022
PB - Association for Computing Machinery
T2 - 25th International Symposium on Researchin Attacks, Intrusions and Defenses, RAID 2022
Y2 - 26 October 2022 through 28 October 2022
ER -