Privacy by Design Through a Social Requirements Analysis of Social Network Sites form a User Perspective

Wolf, Ralf De; Heyman, Rob; Pierson, Jos

doi:10.1007/978-94-007-5170-5_11

Cited by 13 publications

(8 citation statements)

References 16 publications

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“…In this regard, since self-adaptive privacy protection schemes should have the ability to maintain users' privacy in changing contexts [77], they should provide users with recommendations about their own decisions or of their family and friends group members specifically to disclose or not information. The specification of these groups is of great importance, since [78] in a previous work, attempting to identify distinct social requirements of privacy issues within SNs by interviewing 15 adolescents (14-18) regarding their online behaviour on Facebook, indicated the need to make more explicit which are the "real generalized others". The dominance of Family, Friends and Companionships groups characterizes the frequency of participants' communication within SNs, while it is quite interesting that a great amount of communication takes place among the members of political groups.…”

Section: Discussionmentioning

confidence: 99%

Identifying Privacy Related Requirements for the Design of Self-Adaptive Privacy Protections Schemes in Social Networks

et al. 2021

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Social Networks (SNs) bring new types of privacy risks threats for users; which developers should be aware of when designing respective services. Aiming at safeguarding users’ privacy more effectively within SNs, self-adaptive privacy preserving schemes have been developed, considered the importance of users’ social and technological context and specific privacy criteria that should be satisfied. However, under the current self-adaptive privacy approaches, the examination of users’ social landscape interrelated with their privacy perceptions and practices, is not thoroughly considered, especially as far as users’ social attributes concern. This study, aimed at elaborating this examination in depth, in order as to identify the users’ social characteristics and privacy perceptions that can affect self-adaptive privacy design, as well as to indicate self-adaptive privacy related requirements that should be satisfied for users’ protection in SNs. The study was based on an interdisciplinary research instrument, adopting constructs and metrics from both sociological and privacy literature. The results of the survey lead to a pilot taxonomic analysis for self-adaptive privacy within SNs and to the proposal of specific privacy related requirements that should be considered for this domain. For further establishing of our interdisciplinary approach, a case study scenario was formulated, which underlines the importance of the identified self-adaptive privacy related requirements. In this regard, the study provides further insight for the development of the behavioral models that will enhance the optimal design of self-adaptive privacy preserving schemes in SNs, as well as designers to support the principle of PbD from a technical perspective.

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Section: Discussionmentioning

confidence: 99%

Identifying Privacy Related Requirements for the Design of Self-Adaptive Privacy Protections Schemes in Social Networks

et al. 2021

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“…We consider the user as an active subject defining who can and cannot see his personal information flow. It is not just about the information flow an sich, but also about using this information for creating meaning in a social context (De Wolf et al, 2013).…”

Section: Privacy As Subjectmentioning

confidence: 99%

Evaluating social media privacy settings for personal and advertising purposes

Heyman

Wolf

Pierson

2014

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Purpose – The purpose of this paper is to define two types of privacy, which are distinct but often reduced to each other. It also investigates which form of privacy is most prominent in privacy settings of online social networks (OSN). Privacy between users is different from privacy between a user and a third party. OSN, and to a lesser extent researchers, often reduce the former to the latter, which results in misleading users and public debate about privacy. Design/methodology/approach – The authors define two types of privacy that account for the difference between interpersonal and third-party disclosure. The first definition draws on symbolic interactionist accounts of privacy, wherein users are performing dramaturgically for an intended audience. Third-party privacy is based on the data that represent the user in data mining and knowledge discovery processes, which ultimately manipulate users into audience commodities. This typology was applied to the privacy settings of Facebook, LinkedIn and Twitter. The results are presented as a flowchart. Findings – The research indicates that users are granted more options in controlling their interpersonal information flow towards other users than third parties or service providers. Research limitations/implications – This distinction needs to be furthered empirically, by comparing user’s privacy expectations in both situations. On more theoretical grounds, this typology could also be linked to Habermas’ system and life-world. Originality/value – A typology has been provided to compare the relative autonomy users receive for settings that drive revenue and settings, which are independent from revenue.

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“…Social privacy concerns how and when personal information is shared with others within an OSN (e.g. [2], [3], [4]), whereas instrumental privacy concerns the personal data access by service providers, governments or other corporations (e.g. [5], [6]).…”

Section: Introductionmentioning

confidence: 99%

“…The source code and the datasets can be found at the author's website: http://i.stanford.edu/ ∼ julian/ 4. The edge density in a circle should be larger than that on average in the whole graph.…”

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confidence: 99%

Circles, posts and privacy in egocentric social networks

Gao

Berendt

2013

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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The users in Online Social Networks (OSN) may share private information with wrong friends. One approach to tackle this issue is by applying community discovery methods in egocentric networks to automatically generate friend circles for the user. There is however a discrepancy between the predicted circles and the circles that the user has in mind. A deep rooted reason is that it only makes sense when the circles are considered under certain usage. We designed and implemented an exploratory visualization tool that can help users determine the visibilities of their online posts. More specifically, we first examined the state-of-the-art community discovery methods for egocentric networks, then proposed a new visualization design with fine-grained control for the user to interact with the circles and make visibility decisions. Finally, we conducted an experimental user study evaluating the usefulness of this design.

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Privacy by Design Through a Social Requirements Analysis of Social Network Sites form a User Perspective

Cited by 13 publications

References 16 publications

Identifying Privacy Related Requirements for the Design of Self-Adaptive Privacy Protections Schemes in Social Networks

Identifying Privacy Related Requirements for the Design of Self-Adaptive Privacy Protections Schemes in Social Networks

Evaluating social media privacy settings for personal and advertising purposes

Circles, posts and privacy in egocentric social networks

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