On online social networks such as Facebook, massive self-disclosure by users has attracted the attention of Industry players and policymakers worldwide. Despite the Impressive scope of this phenomenon, very little Is understood about what motivates users to disclose personal Information. Integrating focus group results Into a theoretical privacy calculus framework, we develop and empirically test a Structural Equation Model of self-disclosure with 259 subjects. We find that users are primarily motivated to disclose Information because of the convenience of maintaining and developing relationships and platform enjoyment. Countervailing these benefits, privacy risks represent a critical barrier to information disclosure. However, users’ perception of risk can be mitigated by their trust in the network provider and availability of control options. Based on these findings, we offer recommendations for network providers.

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Driven by privacy-related fears, users of Online Social Networks may start to reduce their network activities. This trend can have a negative impact on network sustainability and its business value. Nevertheless, very little is understood about the privacy-related concerns of users and the impact of those concerns on identity performance. To close this gap, we take a systematic view of user privacy concerns on such platforms. Based on insights from focus groups and an empirical study with 210 subjects, we find that (i) Organizational Threats and (ii) Social Threats stemming from the user environment constitute two underlying dimensions of the construct "Privacy Concerns in Online Social Networks". Using a Structural Equation Model, we examine the impact of the identified dimensions of concern on the Amount, Honesty, and Conscious Control of individual self-disclosure on these sites. We find that users tend to reduce the Amount of information disclosed as a response to their concerns regarding Organizational Threats. Additionally, users become more conscious about the information they reveal as a result of Social Threats. Network providers may want to develop specific mechanisms to alleviate identified user concerns and thereby ensure network sustainability.

A systematic methodology for privacy impact assessments: a design science approach Article (Accepted for Publication) (Refereed) AbstractFor companies that develop and operate IT applications that process the personal data of customers and employees, a major problem is protecting this data and preventing privacy breaches. Failure to adequately address this problem can result in considerable damage to the company's reputation and finances, as well as negative affects for customers or employees (data subjects). We address this problem by proposing a methodology that systematically considers privacy issues by using a step-bystep privacy impact assessment (so called 'PIA'). Existing PIA approaches lack easy applicability because they are either insufficiently structured or imprecise and lengthy. We argue that companies that employ the PIA proposed in this article can achieve 'privacy-by-design', which is widely heralded by data protection authorities. In fact, the German Federal Office for Information Security (BSI) ratified the approach we present in this article for the technical field of RFID and published it as a guideline in November 2011. The contribution of the artefacts we created is twofold: First, we provide a formal problem representation structure for the analysis of privacy requirements. Second, we reduce the complexity of the privacy regulation landscape for practitioners who need to make privacy management decisions for their IT applications.

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Heralded by regulators, Privacy by Design holds the promise to solve the digital world's privacy problems. But there are immense challenges, including management commitment and step-by-step methods to integrate privacy into systems.

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