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Online social networks (OSNs) have become an integral part of social interaction and communication between people. Reasons include the ubiquity of OSNs that is offered through mobile devices and the possibility to bridge spatial and temporal communication boundaries. However, several researchers have raised privacy concerns due to the large amount of user data shared on OSNs. Yet, despite the large body of research addressing OSN privacy issues, little differentiation of data types on social network sites is made and a generally accepted classification and terminology for such data is missing. The lack of a terminology impedes comparability of related work and discussions among researchers, especially in the case of privacy implications of different data types. To overcome these shortcomings, this paper develops a well-founded terminology based on a thorough literature analysis and a conceptualization of typical OSN user activities. The terminology is organized hierarchically resulting in a taxonomy of data types. The paper furthermore discusses and develops a metric to assess the privacy relevance of different data types. Finally, the taxonomy is applied to the five major OSNs to evaluate its generalizability.

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Abstract-The growing relevance and usage intensity of Online Social Networks (OSNs) along with the accumulation of a large amount of user data has led to privacy concerns among researchers and end users. Despite a large body of research addressing OSN privacy issues, little differentiation of data types on social network sites is made and a generally accepted classification and terminology for such data is missing, hence leading to confusion in related discussions. This paper proposes a taxonomy for data types on OSNs based on a thorough literature analysis and a conceptualization of typical OSN user activities. It aims at clarifying discussions among researchers, benefiting comparisons of data types within and across OSNs and at educating the end user about characteristics and implications of OSN data types. The taxonomy is evaluated by applying it to four major OSNs.

The rising pervasiveness of social networking sites (SNS) poses new privacy risks and prompts adjusting one's online appearance to the current context of usage. This includes the deliberate and targeted disclosure of selected information to a subset of one's contacts, also known as social identity management (SIdM). Yet, when performing SIdM across multiple SNS, it is difficult to maintain a consistent representation of oneself. We introduce a global model to perform SIdM which is independent of particular SNS. Then we show how to apply modifications of the global model to particular sites. We evaluate the feasibility of the approach by surveying currently available settings and APIs in the most important SNS.

Keywords:Social identity management

Abstract-To address privacy threats stemming from interacting with other users on Social Networking Sites (SNS), effective Social Identity Management (SIdM) is a key requirement. SIdM refers to the deliberate and targeted disclosure of personal attribute values to a subset of one's contacts on SNS. While a variety of privacy-enhancing approaches have been proposed, these are often isolated solutions that lack integration into a reference framework that states the requirements for successfully managing one's identity. In this paper, a reference framework of existing and desired SIdM settings is derived from identity theory, literature analysis, and existing SNS. Based thereupon, we examine the SIdM capabilities of prevalent SNS and highlight possible improvements.

Online social networks (OSNs) have become an integral part of social interaction and communication between people. Reasons include the ubiquity of OSNs that is offered through mobile devices and the possibility to bridge spatial and temporal communication boundaries. However, several researchers have raised privacy concerns due to the large amount of user data shared on OSNs. Yet, despite the large body of research addressing OSN privacy issues, little differentiation of data types on social network sites is made and a generally accepted classification and terminology for such data is missing. The lack of a terminology impedes comparability of related work and discussions among researchers, especially in the case of privacy implications of different data types. To overcome these shortcomings, this paper develops a well-founded terminology based on a thorough literature analysis and a conceptualization of typical OSN user activities. The terminology is organized hierarchically resulting in a taxonomy of data types. The paper furthermore discusses and develops a metric to assess the privacy relevance of different data types. Finally, the taxonomy is applied to the five major OSNs to evaluate its generalizability.