Mobile computing is a maturing technology with benefits for consumers. The purpose of this chapter is to furnish research on the perceptions of non-information systems students in both America and Europe on the impact of mobile computing devices on privacy and security. The chapter expands upon earlier research on only the perceptions of information systems students in America on mobile computing privacy and security. This research indicates a higher level of knowledge of the features of mobile computing, but lower levels of knowledge of inherent issues of mobile computing and consumer privacy and of precaution with mobile computing devices. Findings imply an inadequacy in general curriculum, and especially in data mining curriculum, but also an opportunity to improve the curricula. This research will benefit educators attempting to improve their pedagogy with syllabi summarized in the chapter that integrates contemporary issues of privacy and security with mobile computing technology.
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Mobile computing applications on mobile computing devices (MCDs), such as cellular phones, laptops, personal digital assistants (PDAs), tablets, and other devices, are advancing in beneficial features for consumers. Browsing information and news, game playing, instant messaging, personal and professional e-mailing, and photo and text messaging are frequent features on the devices (M: Metrics Inc., 2006). These devices have advanced from basic cellular phones and PDAs to light computing devices interfaced to the Internet with information-rich and location-based or enabled services. Innovations in mobile computing have advanced from cellular payment systems to high speed networks in Europe, which is considered further along in the development of the devices than in America (Lundquist, March, 2007). Mobile computing with location-enabled services is considered by pundits as the killer application (Lundquist, April, 2007) and the technical trend of 2007 integral to consumers (Castells et al., 2007). Miniature mobile computing is contributing to a new period of pervasive computing (Denne, 2007).
Data mining involves searching and finding hidden patterns in large databases of mostly public data to generate profiles based on personal data and behavior patterns of citizens and consumers (Tavani, 2004). Data mining analysis methods evaluate the potential of current customer profiles in order to facilitate future customer prospecting and sales. Much of the data that is mined today is either public or semi-public – our supermarket purchases, surfing habits, salary, location, and other such information. The main ethical issues in data mining are that consumers are not generally aware their data is being gathered, do not know the uses to which the data will be made, or have not consented to the use of such data.