The increase in online social network (OSN) usage has led to personal details known as attributes being readily displayed in OSN profiles. This can lead to the profile owners being vulnerable to privacy and social engineering attacks which include identity theft, stalking and re identification by linking. Due to a need to address privacy in OSNs, this thesis presents a framework to quantify the vulnerability of a user¿s OSN profile. Vulnerability is defined as the likelihood that the personal details displayed on an OSN profile will spread due to the actions of the profile owner and their friends in regards to information disclosure. The vulnerability measure consists of three components. The individual vulnerability is calculated by allocating weights to profile attribute values disclosed and neighbourhood features which may contribute towards the personal vulnerability of the profile user. The relative vulnerability is the collective vulnerability of the profiles¿ friends. The absolute vulnerability is the overall profile vulnerability which considers the individual and relative vulnerabilities. The first part of the framework details a data retrieval approach to extract MySpace profile data to test the vulnerability algorithm using real cases. The profile structure presented significant extraction problems because of the dynamic nature of the OSN. Issues of the usability of a standard dataset including ethical concerns are discussed. Application of the vulnerability measure on extracted data emphasised how so called ¿private profiles¿ are not immune to vulnerability issues. This is because some profile details can still be displayed on private profiles. The second part of the framework presents the normalisation of the measure, in the context of a formal approach which includes the development of axioms and validation of the measure but with a larger dataset of profiles. The axioms highlight that changes in the presented list of profile attributes, and the attributes¿ weights in making the profile vulnerable, affect the individual vulnerability of a profile. iii Validation of the measure showed that vulnerability involving OSN profiles does occur and this provides a good basis for other researchers to build on the measure further. The novelty of this vulnerability measure is that it takes into account not just the attributes presented on each individual profile but features of the profiles¿ neighbourhood.

This thesis is set to investigate the factors that affect the intention to disclose personal information to SNS providers. The purpose of this study is to identify factors and how these factors affect the behavioral intention to disclose personal information to SNS from a new angle of analysis, namely through a user-to-organizational approach. By examining this phenomenon in a new context, this study seeks to provide an understanding of this phenomenon in a new setting and test if previous research can be used to explain the stated behavioral intention. With this argumentation in mind, the thesis aims to contribute to existing theory in consumer behavior, social networking sites, privacy concerns and personal information disclosure. In order to do so this paper is built on a quantitative research approach where the main research strategy is based on the results of a questionnaire. Through the questionnaire, a proposed framework was tested in order to identify factors affecting peoples’ intentions to disclose personal information to SNS providers. The model was built upon the structure of privacy calculation theory, which has been proven to identify intentions to disclose personal information in online settings. The results show that the factors that significantly affect behavioral intentions are trusting beliefs and self-presentations. They are factors leading to personal information disclosure to SNS providers. Limitations of this study is that it suffers a sampling restrictions due to its sampling of only Swedish inhabitants. This paper is the first to develop and empirically test an integrated model of personal information disclosure to SNS, taking on a user-to-organizational approach to privacy. Since SNS are financially evaluated by number of active users of their SNS it is crucial to understand the factors that affect consumers’ intention to disclose their personal information. From the empirical findings, this study proved that trust and self-presentation determine the intentions to disclose personal information to SNS providers. The results shows a discrepancy of existing literature, explaining differences between user-to-user and user-to-organizational context, and thus broadening the context of personal disclosure in the SNS realm. Practitioners can, by using the insights, tailor their strategies in order to maximize the efforts of affecting potential users’ to disclose their personal information, which SNS providers are dependent upon.

The increase in online social network (OSN) usage has led to personal details known as attributes being readily displayed in OSN profiles. This can lead to the profile owners being vulnerable to privacy and social engineering attacks which include identity theft, stalking and re identification by linking. Due to a need to address privacy in OSNs, this thesis presents a framework to quantify the vulnerability of a user's OSN profile. Vulnerability is defined as the likelihood that the personal details displayed on an OSN profile will spread due to the actions of the profile owner and their friends in regards to information disclosure. The vulnerability measure consists of three components. The individual vulnerability is calculated by allocating weights to profile attribute values disclosed and neighbourhood features which may contribute towards the personal vulnerability of the profile user. The relative vulnerability is the collective vulnerability of the profiles' friends. The absolute vulnerability is the overall profile vulnerability which considers the individual and relative vulnerabilities. The first part of the framework details a data retrieval approach to extract MySpace profile data to test the vulnerability algorithm using real cases. The profile structure presented significant extraction problems because of the dynamic nature of the OSN. Issues of the usability of a standard dataset including ethical concerns are discussed. Application of the vulnerability measure on extracted data emphasised how so called 'private profiles' are not immune to vulnerability issues. This is because some profile details can still be displayed on private profiles. The second part of the framework presents the normalisation of the measure, in the context of a formal approach which includes the development of axioms and validation of the measure but with a larger dataset of profiles. The axioms highlight that changes in the presented list of profile attributes, and the attributes' weights in making the profile vulnerable, affect the individual vulnerability of a profile. iii Validation of the measure showed that vulnerability involving OSN profiles does occur and this provides a good basis for other researchers to build on the measure further. The novelty of this vulnerability measure is that it takes into account not just the attributes presented on each individual profile but features of the profiles' neighbourhood.

Social Network Site (SNS) users’ disclosed personal information is beneficial for marketers, as targeted advertising can be provided accordingly. This study identifies the three privacy concepts of concerns, attitudes and intentions, along with culture (i.e. Hofstede’s masculinity) to be key drivers of users’ self-disclosure on SNSs. Hence, this study evaluates these antecedents and their potential effect on self-disclosure, considering Facebook as the SNS of choice. To account for potential cross-cultural differences, data has been gathered via two online questionnaires, resulting in one German and one Norwegian sample of respondents. Multiple regression analyses were carried to evaluate the antecedents of self-disclosure and one-way ANOVA to examine potential differences amongst Germans and Norwegians. Results indicate that privacy intention is the strongest predictor of self-disclosure on SNSs; whereas privacy concerns and attitudes only reveal an indirect effect on self-disclosure. In addition, significant differences have been found for all of the constructs, indicating that there exist fundamental differences between Germans and Norwegians with regards to their cognitive processing behind personal information disclosure on SNSs.