The Price of Privacy - An Evaluation of the Economic Value of Collecting Clickstream Data

The analysis of clickstream data facilitates the understanding and prediction of customer behavior in e-commerce. Companies can leverage such data to increase revenue. For customers and website users, on the other hand, the collection of behavioral data entails privacy invasion. The objective of the paper is to shed light on the trade-off between privacy and the business value of cus- tomer information. To that end, the authors review approaches to convert clickstream data into behavioral traits, which we call clickstream features, and propose a categorization of these features according to the potential threat they pose to user privacy. The authors then examine the extent to which different categories of clickstream features facilitate predictions of online user shopping pat- terns and approximate the marginal utility of using more privacy adverse information in behavioral prediction models. Thus, the paper links the literature on user privacy to that on e-commerce analytics and takes a step toward an economic analysis of privacy costs and benefits. In par- ticular, the results of empirical experimentation with large real-world e-commerce data suggest that the inclusion of short-term customer behavior based on session-related information leads to large gains in predictive accuracy and business performance, while storing and aggregating usage behavior over longer horizons has comparably less value

Sustainable Data Governance: A Systematic Review and a Conceptual Framework

We present a systematic literature review based on bibliometric analysis to clarify the role of data governance in sustainable development. We made a concept-centric review of 35 relevant papers (out of an initial set of 2214) selected from Scopus and Web of Science and classified them into (1) sector-specific, (2) causal relationships and approaches, (3) data accessibility for sustainable development, and (4) smart contexts. Our contribution includes a conceptual framework for sustainable data governance in product lifecycles. Pursuing data-driven sustainability requires actions in structure, processes, and relational mechanisms. Data attributes (e.g., privacy, immutability, permissions, fairness), scope of data to be covered, and supporting technology are increasingly important to reduce all forms of waste while ensuring a long-term strategy to generate sustainable value from data

MediBlock-A Privacy-aware Blockchain to store patients data and effective diagnosis methods

University of Technology Sydney. Faculty of Engineering and Information Technology.Blockchain offers a promising new distributed framework that can be leveraged to support the integration and sharing of the patient’s healthcare information between the relevant stakeholders, such as doctors, patients, insurance companies, pharmaceuticals, and researchers. The shared data is secure due to the inherent features provided by the Blockchain framework, such as smart contracts, consensus algorithms, and hashing. To identify the shortcomings of the existing approaches (in patient information sharing), we conducted a systematic literature review (SLR) and some of the key issues being identified are the need to identify a solution for data storage and management, data sharing and perform data analysis. Another important finding is to make the Blockchain solution privacy-aware because a data leak in this area results in many complications for the patient as well as the healthcare provider. This research presents a privacy-aware Blockchain for medical data sharing that proposes a reputation-based mechanism to quantify and measure the effectiveness of a medical diagnosis. This is the first study to take into account the users’ authority over data while sharing it with other stakeholders and first approach that proposes a privacy-aware data sharing for deriving value and insights for various stakeholders

Beyond the Privacy Calculus: Dynamics Behind Online Self-Disclosure

Self-disclosure is ubiquitous in today’s digitized world as Internet users are constantly sharing their personal information with other users and providers online, for example when communicating via social media or shopping online. Despite offering tremendous benefits (e.g., convenience, personalization, and other social rewards) to users, the act of self-disclosure also raises massive privacy concerns. In this regard, Internet users often feel they have lost control over their privacy because sophisticated technologies are monitoring, processing, and circulating their personal information in real-time. Thus, they are faced with the challenge of making intelligent privacy decisions about when, how, to whom, and to what extent they should divulge personal information. They feel the tension between being able to obtain benefits from online disclosure and wanting to protect their privacy. At the same time, firms rely on massive amounts of data divulged by their users to offer personalized services, perform data analytics, and pursue monetization. Traditionally, privacy research has applied the privacy calculus model when studying self-disclosure decisions online. It assumes that self-disclosure (or, sometimes, usage) is a result of a rational privacy risk–benefit analysis. Even though the privacy calculus is a plausible model that has been validated in many cases, it does not reflect the complex nuances of privacy-related judgments against the background of real-life behavior, which sometimes leads to paradoxical research results. This thesis seeks to understand and disentangle the complex nuances of Internet users’ privacy-related decision making to help firms designing data gathering processes, guide Internet users wishing to make sound privacy decisions given the background of their preferences, and lay the groundwork for future research in this field. Using six empirical studies and two literature reviews, this thesis presents additional factors that influence self-disclosure decisions beyond the well-established privacy risk–benefit analysis. All the studies have been published in peer-reviewed journals or conference proceedings. They focus on different contexts and are grouped into three parts accordingly: monetary valuation of privacy, biases in disclosure decisions, and social concerns when self-disclosing on social networking sites. The first part deals with the value Internet users place on their information privacy as a proxy for their perceived privacy risks when confronted with a decision to self-disclose. A structured literature review reveals that users’ monetary valuation of privacy is very context-dependent, which leads to scattered or occasionally even contradictory research results. A subsequent conjoint analysis supplemented by a qualitative pre-study shows that the amount of compensation, the type of data, and the origin of the platform are the major antecedents of Internet users’ willingness to sell their data on data selling platforms. Additionally, an experimental survey study contrasts the value users ascribe to divulging personal information (benefits minus risks) with the value the provider gets from personal information. Building on equity theory, the extent to which providers monetize the data needs to be taken into account apart from a fair data handling process. In other words, firms cannot monetize their collected user data indefinitely without compensating their users, because users might feel exploited and thus reject the service afterwards. The second part delineates the behavioral and cognitive biases overriding the rational tradeoff between benefits and privacy risks that has traditionally been assumed in privacy research. In particular, evaluability bias and overconfidence are identified as moderators of the link between privacy risks and self-disclosure intentions. In single evaluation mode (i.e., no reference information available) and when they are overconfident, Internet users do not take their perceived privacy risks into account when facing a self-disclosure decision. By contrast, in joint evaluation mode of two information systems and when users are realistic about their privacy-related knowledge, the privacy risks that they perceive play a major role. This proof that mental shortcuts interact with privacy-related judgments adds to studies that question the rational assumption of the privacy calculus. Moving beyond privacy risks, the third part examines the social factors influencing disclosure decisions. A structured literature review identifies privacy risks as the predominantly studied impediment to self-disclosure on social networking sites (SNS). However, a subsequent large scale survey study shows that on SNS, privacy risks play no role when users decide whether to self-disclose. It is rather the social aspects, such as the fear of receiving a negative evaluation from others, that inform disclosure decisions. Furthermore, based on a dyadic study among senders and receivers of messages on SNS, it is shown that senders are subject to a perspective-taking bias: They overestimate the hedonic and utilitarian value of their message for others. In this vein, these studies combine insights from social psychology literature with the uniqueness of online data disclosure and show that, beyond the potential misuse of personal information from providers, the risk of misperception in the eyes of other users is crucial when explaining self-disclosure decisions. All in all, this thesis draws from different perspectives – including value measuring approaches, behavioral economics, and social psychology – to explain self-disclosure decisions. Specifically, it shows that the privacy calculus is oversimplified and, ultimately, needs to be extended with other factors like mental shortcuts and social concerns to portray Internet users’ actual privacy decision making

Information Producers, Information Consumers : Location Data Privacy in Institutional Settings

Peer reviewedPreprin