Drawing on digital identity theories, social software engineering theory (SSE), and the Privacy Safeguard (PriS) methodology, we examined the way that personal information uploaded on social media (SM) imposes privacy issues. Throughout a review on users’ self-representation on SM, we examined the impact of self-determination and self-disclosure on users’ privacy, and we identified the social attributes (SA) that cause privacy implications. This paper specifies 18 SA that users employ to achieve their optimal level of representation while summarizing possible ways that these attributes provoke users’ identification. In particular, our research has shown that SM users represent their personas by unveiling SA to construct popular, representative, and conversational profiles. As disclosing SA increases privacy implications, we intend to help users build profiles that respect their privacy. Examining users’ SA deepens our understanding of disclosing personal information on SM while leading to a better quantification of identity attributes; furthermore, users’ top five most revealing attributes were summarized. Considering that SSE addresses users’ privacy implications from an early stage of systems designing, our research, identifying the SA, will be helpful in addressing privacy from a socio-technical aspect, aiming at bridging the socio-technical gap by drawing designers’ attention to users’ social aspects.

Nowadays, location-sharing applications (LSA) within social media enable users to share their location information at different levels of precision. Users on their side are willing to disclose this kind of information in order to represent themselves in a socially acceptable online way. However, they express privacy concerns regarding potential malware location-sharing applications, since users’ geolocation information can provide affiliations with their social identity attributes that enable the specification of their behavioral normativity, leading to sensitive information disclosure and privacy leaks. This paper, after a systematic review on previous social and privacy location research, explores the overlapping of these fields in identifying users’ social attributes through examining location attributes while online, and proposes a targeted set of location privacy attributes related to users’ socio-spatial characteristics within social media.

Purpose: The purpose of this paper is to establish reciprocity among socio-location attributes while underlining the additional users’ privacy implications on social media (SM). Design/methodology/approach: Digital identity theories, social software engineering theory and the Privacy Safeguard (PriS) methodology were considered while reviewing 32 papers for identifying users’ SM attributes. After proposing interrelations among socio-location attributes, the PriS method was used to match social aspects of privacy in designing case studies to illustrate the associations through potential users’ privacy implications. Findings: Eighteen users’ SM attributes were collected and correlated to the Face, Frame, Activity, Time and Stage (FFrATS) 4 W (socio-location attributes), which provoke further privacy implications due to the notions of self-determination and self-disclosure on SM. The authors draw on the PriS methodology to address privacy’s multidimensionality while creating case studies to examine privacy issues arising due to socio-location attribute disclosure and users’ trajectories and normativity lines. Research limitations/implications: Supplementary case studies and research are needed to enable the design of a socio-spatially and privacy-aware designing methodology. Practical implications: Designing proper methodologies and techniques to address users’ privacy implications deriving from socio-location attributes can provide designers with a technical solution to SM platforms. Social implications: Socio-location attribute disclosure constructs representative SM profiles; however, the revelation of attributes and their interrelations create additional privacy implications for SM users. Originality/value: Deepening the understanding of disclosing socio-location attributes on SM while bridging the socio-technical gap will provide the necessary background for proposing technical solutions to protecting users’ privacy.

This paper presents a novel approach to address the challenges of self-adaptive privacy in cloud computing environments (CCE). Under the Cloud-InSPiRe project, the aim is to provide an interdisciplinary framework and a beta-version tool for self-adaptive privacy design, effectively focusing on the integration of technical measures with social needs. To address that, a pilot taxonomy that aligns technical, infrastructural, and social requirements is proposed after two supplementary surveys that have been conducted, focusing on users’ privacy needs and developers’ perspectives on self-adaptive privacy. Through the integration of users’ social identity-based practices and developers’ insights, the taxonomy aims to provide clear guidance for developers, ensuring compliance with regulatory standards and fostering a user-centric approach to self-adaptive privacy design tailored to diverse user groups, ultimately enhancing satisfaction and confidence in cloud services.