In the context of a myriad of mobile apps which collect personally identifiable information (PII) and a prospective market place of personal data, we investigate a user-centric monetary valuation of mobile PII. During a 6-week long user study in a living lab deployment with 60 participants, we collected their daily valuations of 4 categories of mobile PII (communication, e.g. phonecalls made received, applications, e.g. time spent on different apps, location and media, e.g. photos taken) at three levels of complexity (individual data points, aggregated statistics and processed, i.e. meaningful interpretations of the data). In order to obtain honest valuations, we employ a reverse second price auction mechanism. Our findings show that the most sensitive and valued category of personal information is location. We report statistically significant associations between actual mobile usage, personal dispositions, and bidding behavior. Finally, we outline key implications for the design of mobile services and future markets of personal data.
Data from do-not-call registries and other sources show discernible patterns in the demographics of consumers who signed up for do-not-call lists. Such patterns might also be useful in analyzing the prospects for a do-not-spam registry This paper introduces results of a study into the value of location privacy for individuals using mobile devices. We questioned a sample of over 1200 people from five EU countries, and used tools from experimental psychology and economics to extract from them the value they attach to their location data. We compare this value across national groups, gender and technical awareness, but also the perceived difference between academic use and commercial exploitation. We provide some analysis of the self-selection bias of such a study, and look further at the valuation of location data over time using data from another experiment.  In studies of people's privacy behavior, the extent of disclosure of personal information is typically measured as a summed total or a ratio of disclosure. In this paper, we evaluate three information disclosure datasets using a six-step statistical analysis, and show that people's disclosure behaviors are rather multidimensional: participants' disclosure of personal information breaks down into a number of distinct factors. Moreover, people can be classified along these dimensions into groups with different ''disclosure styles''. This difference is not merely in degree, but rather also in kind: one group may for instance disclose location-related but not interest-related items, whereas another group may behave exactly the other way around. We also found other significant differences between these groups, in terms of privacy attitudes, behaviors, and demographic characteristics. These might for instance allow an online system to classify its users into their respective privacy group, and to adapt its privacy practices to the disclosure style of this group. We discuss how our results provide relevant insights for a more user-centric approach to privacy and, more generally, advance our understanding of online privacy behavior. We use techniques from experimental economics and psy- chology to determine how much compensation must be oered to per- suade someone to allow precise information about their location to be collected. We pretend that we are running a study that needs volunteers to have their location monitored (via their mobile phone) over a period of one month. Volunteers apply by specifying the amount of compen- sation which they would require to participate in the experiment. The experimental subjects are led to believe that we will run a sealed-bid second-price auction on these values, and thus we obtain an estimate of the value that users attach to their location data being used by third parties. Traditional theory suggests consumers should be able to manage their privacy. Yet, empirical and theoretical research suggests that consumers often lack enough information to make privacy-sensitive decisions and, even with sufficient information, are likely to trade off long-term privacy for short-term benefits The advent of the Internet has made the transmission of personally identifiable information more common and often unintended by the user. As personal information becomes more accessible, individuals worry that businesses misuse the information that is collected while they are online. Organizations have tried to mitigate this concern in two ways: (1) by offering privacy policies regarding the handling and use of personal information and (2) by offering benefits such as financial gains or convenience. In this paper, we interpret these actions in the context of the information-processing theory of motivation. Information-processing theories, also known as expectancy theories in the context of motivated behavior, are built on the premise that people process information about behavior-outcome relationships. By doing so, they are forming expectations and making decisions about what behavior to choose. Using an experimental setting, we empirically validate predictions that the means to mitigate privacy concerns are associated with positive valences resulting in an increase in motivational score. In a conjoint analysis exercise, 268 participants from the United States and Singapore face trade-off situations, where an organization may only offer incomplete privacy protection or some benefits. While privacy protections (against secondary use, improper access, and error) are associated with positive valences, we also find that financial gains and convenience can significantly increase individuals' motivational score of registering with a Web site. We find that benefits-monetary reward and future convenience-significantly affect individuals' preferences over Web sites with differing privacy policies. We also quantify the value of Web site privacy protection. Among U.S. subjects, protection against errors, improper access, and secondary use of personal information is worth @math 44.62. Finally, our approach also allows us to identify three distinct segments of Internet users-privacy guardians, information sellers, and convenience seekers.  