## 1 Introduction
Self-Adaptive privacy aims at protecting users' privacy, through the development of holistic user models that pay attention to their socio-contextual and technological frames of action [18]. For instance, [19] developed the user-tailored privacy by design framework, aiming to address the types of privacy adaptations that should be implemented, in order for Facebook users with different privacy management strategies to be supported more personalized. However, as other previous ambitious self-adaptive privacy schemes for SNs [20; 21] that have been proposed, their work does not identify users' social categories and attributes in depth. These attributes affect their privacy norms, which is of great importance for the developers and the design of adequate privacy solutions [2], in order for instance to support users' authentication, authorization and confidentiality of personal information, which considered being of the most significant privacy challenges in Internet of Things environments [22]. In our previous work [23], we have shown that, in particular for self-adaptive privacy schemes, in order to be effectively designed and deployed, several criteria concerning users' social and technical context should be satisfied. Self-adaptive privacy systems should be able to protect users' privacy in changing contexts, either by providing users with recommendations or by proceeding automated actions based on users' decisions for personal information disclosure or not, within their context. In this regard, specific functions should be deployed, such as classified interaction strategies, which facilitate the connection of the system and the user, providing privacy awareness, justification for each privacy decision and control capabilities. To address that, monitoring, analysis, design, and implementation of self-adaptive privacy systems should be performed through framework and behavioral models, which identify user's environment and their interconnections with each system. Furthermore, the systems should be adapted to the interoperability of the used technologies and to the structure of the systems, such as SNs, so as to balance among systems' privacy choices automation and users' choices. This reveals a number of criteria that should be considered in order to reason about privacy under a socio-technical view. These concern: (a) the identification of users' privacy social needs in each context; (b) the identification of all stakeholders' privacy technical needs; (c) the identification of privacy risks and threats; (d) the indication of users' sensitive information in each context; (e) the systems' adaption to the interoperability of technologies; (f) the assessment of the best options among users' and systems' privacy choices; and (g) an effective decision-making procedure to be followed, which balances users' social and privacy needs. Among these criteria, the identification of users' social characteristics and privacy needs is crucial, since, in previous works regarding self-adaptive privacy schemes, these were part-substantially or limited addressed. This identification will provide further privacy protection within SNs, enabling for instance the satisfaction of the privacy criteria that [13] supported, such as the addition/removal of users from a SNs group, the efficiency of a user key revocation, the encryption/decryption efficiency, the encryption header overhead, the ability to encrypt for the conjunction/disjunction of SNs groups, the ability to encrypt for a SNs a user which is not a group member. Therefore, to support this aim, the investigation and the capturing of empirical data related to users' social characteristics specifically within SNs is required, since they affect their privacy management. For example, users' privacy safeguard, when they share information concerning multiple users, is of major importance [24].