  In the IoT world, intelligent virtual assistant (IVA) is a popular service to
interact with users based on voice command. For optimal performance and
efficient data management, famous IVAs like Amazon Alexa and Google Assistant
usually operate based on the cloud computing architecture. In this process, a
large amount of behavioral traces that include user voice activity history with
detailed descriptions can be stored in the remote servers within an IVA
ecosystem. If those data (as also known as IVA cloud native data) are leaked by
attacks, malicious person may be able to not only harvest detailed usage
history of IVA services, but also reveals additional user related information
through various data analysis techniques. In this paper, we firstly show and
categorize types of IVA related data that can be collected from popular IVA,
Amazon Alexa. We then analyze an experimental dataset covering three months
with Alexa service, and characterize the properties of user lifestyle and life
patterns. Our results show that it is possible to uncover new insights on
personal information such as user interests, IVA usage patterns and sleeping,
wakeup patterns. The results presented in this paper provide important
implications for and privacy threats to IVA vendors and users as well.
