Gamification, the use of game design elements in applications that are not games, has been developed to provide attractive environments and maintain user interest in several domains. In domains such as education, marketing and health, where gamification techniques are applied, user engagement in applications has increased. In these applications the protection of users’ privacy is an important aspect to consider, due to the applications obtaining a record of the personal information of their users. Thus, the purpose of this paper is to identify if applications where gamification is applied do respect users’ privacy. For the accomplishment of this aim, two main steps have been implemented. Since the main principle of gamification is the existence of game elements, the first step was to identify the set of game elements recorded in the literature that are commonly applied in various applications. Afterwards, an examination of the relationship between these elements and privacy requirements was implemented in order to identify which elements conflict with the privacy requirements leading to potential privacy violations and which elements do not. Α conceptual model according to the results of this examination was designed, which presents how elements conflict with requirements. Based on the results, there are indeed game elements which can lead to privacy violations. The results of this work provide valuable guidance to software developers, especially during the design stages of gamified applications since it helps them to consider the protection of users’ privacy in parallel from the early stages of the application development onwards.

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.

The rapid development of cloud computing environments has resulted in various advances and personalized services for users, raising thus several privacy issues. Towards this, research focused on privacy safeguard in the cloud, indicating solutions on the area of self-adaptive privacy. A detailed review is produced to bring forward the carried out work and to analyze it in terms of privacy interdisciplinary standards. In this regard, our work presents the existing self-adaptive privacy approaches and identifies the context for which they have been developed. Moreover, a corresponding classification scheme is provided. The findings give also insights on the proposed tools, which were critically analyzed. This review aims at indicating the developments and limitations of the area, providing potentials of future work in less discussed aspects of the self-adaptive privacy in cloud under an interdisciplinary point of view.

This study addresses the growing complexity of privacy protection in cloud computing environments (CCEs) by introducing a comprehensive socio-technical framework for self-adaptive privacy, complemented by an AI-driven beta tool designed for social media platforms. The framework’s three-stage structure—social, technical, and infrastructural—integrates context-aware privacy controls, dynamic risk assessments, and scalable implementation strategies. Key benefits include enhanced user-centric privacy management through customizable group settings and adaptive controls that respect diverse social identities. The beta tool operationalizes these features via a profile store for structured preference management and a recommendation engine that delivers real-time, AI-powered privacy suggestions tailored to individual contexts. Additionally, the tool’s safety scoring system (0–100) empowers developers and guides them in designing effective privacy solutions and mitigating risks. By bridging social context awareness with technical and infrastructural innovation, this framework significantly improves privacy adaptability, regulatory compliance, and user empowerment in CCEs. It provides a robust foundation for developing scalable and responsive privacy solutions tailored to evolving user needs.