Valuing Personal Data with Privacy Consideration.

A key challenge in information privacy research is how to value personal data with privacy consideration. In this study, we propose an experimental auction approach for valuing personal data. We use the generalized second-price auction to assess the monetary values of individuals' identity, demographic, and private information. We find that individuals' economic valuation of personal data is consistent with their actual self-disclosure behaviors. The economic valuation approach also produces results that are consistent with some well-accepted observations about consumer demographics and privacy. On the other hand, individuals' stated privacy preferences and attitudes are not consistent with their economic valuation. The findings of this study suggest that the proposed approach can be an effective mechanism for measuring personal data privacy. This study also provides important insights into valuing personal information for practical uses with several implications to policy decision makers, corporate executives and managers, data analysts, as well as decision science researchers.

Preserving Patient Privacy When Sharing Same-Disease Data.

Medical and health data are often collected for studying a specific disease. For such same-disease microdata, a privacy disclosure occurs as long as an individual is known to be in the microdata. Individuals in same-disease microdata are thus subject to higher disclosure risk than those in microdata with different diseases. This important problem has been overlooked in data-privacy research and practice, and no prior study has addressed this problem. In this study, we analyze the disclosure risk for the individuals in same-disease microdata and propose a new metric that is appropriate for measuring disclosure risk in this situation. An efficient algorithm is designed and implemented for anonymizing same-disease data to minimize the disclosure risk while keeping data utility as good as possible. An experimental study was conducted on real patient and population data. Experimental results show that traditional reidentification risk measures underestimate the actual disclosure risk for the individuals in same-disease microdata and demonstrate that the proposed approach is very effective in reducing the actual risk for same-disease data. This study suggests that privacy protection policy and practice for sharing medical and health data should consider not only the individuals' identifying attributes but also the health and disease information contained in the data. It is recommended that data-sharing entities employ a statistical approach, instead of the HIPAA's Safe Harbor policy, when sharing same-disease microdata.

Unveiling consumer's privacy paradox behaviour in an economic exchange.

Privacy paradox is of great interest to IS researchers and firms gathering personal information. It has been studied from social, behavioural, and economic perspectives independently. However, prior research has not examined the degrees of influence these perspectives contribute to the privacy paradox problem. We combine both economic and behavioural perspectives in our study of the privacy paradox with a price valuation of personal information through an economic experiment combined with a behavioural study on privacy paradox. Our goal is to reveal more insights on the privacy paradox through economic valuation on personal information. Results indicate that general privacy concerns or individual disclosure concerns do not have a significant influence on the price valuation of personal information. Instead, prior disclosure behaviour in specific scenario, like with healthcare providers or social networks, is a better indicator of consumer price valuations.

Microlearning mApp raises health competence: hybrid service design.

Work place health support interventions can help support our aging work force, with mApps offering cost-effectiveness opportunities. Previous research shows that health support apps should offer users enough newness and relevance each time they are used. Otherwise the 'eHealth law of attrition' applies: 90 % of users are lost prematurely. Our research study builds on this prior research with further investigation on whether a mobile health quiz provides added value for users within a hybrid service mix and whether it promotes long term health? We developed a hybrid health support intervention solution that uses a mix of electronic and physical support services for improving health behaviours, including a mobile micro-learning health quiz. This solution was evaluated in a multiple-case study at three work sites with 86 users. We find that both our mobile health quiz and the overall hybrid solution contributed to improvements in health readiness, -behaviour and -competence. Users indicated that the micro-learning health quiz courses provided new and relevant information. Relatively high utilization rates of the health quiz were observed. Participants indicated that health insights were given that directly influenced every day health perceptions, -choices, coping and goal achievement strategies, plus motivation and self-norms. This points to increased user health self-management competence. Moreover, even after 10 months they indicated to still have improved health awareness, -motivation and -behaviours (food, physical activity, mental recuperation). A design analysis was conducted regarding service mix efficacy; the mobile micro-learning health quiz helped fulfil a set of key requirements that exist for designing ICT-enabled lifestyle interventions, largely in the way it was anticipated.

Developing Privacy Solutions for Sharing and Analyzing Healthcare Data.

The extensive use of electronic health data has increased privacy concerns. While most healthcare organizations are conscientious in protecting their data in their databases, very few organizations take enough precautions to protect data that is shared with third party organizations. Recently the regulatory environment has tightened the laws to enforce privacy protection. The goal of this research is to explore the application of data masking solutions for protecting patient privacy when data is shared with external organizations for research, analysis and other similar purposes. Specifically, this research project develops a system that protects data without removing sensitive attributes. Our application allows high quality data analysis with the masked data. Dataset-level properties and statistics remain approximately the same after data masking; however, individual record-level values are altered to prevent privacy disclosure. A pilot evaluation study on large real-world healthcare data shows the effectiveness of our solution in privacy protection.