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Online social networking sites enable users to create profiles that often contain a broad range of potentially sensitive information, including relationship status, sexual orientation, religion, location, occupation, and interests. Despite the privacy controls offered by most social media platforms, these measures are often insufficient to safeguard users against profile attribute inference attacks, where adversaries infer private information by exploiting publicly accessible data. In this paper, we introduce a novel Bayesian model designed to infer the attributes of users on online social networks by leveraging publicly available profile data and topological features from the target user’s ego graph. Traditional Bayesian inference models rely on the assumption of attribute independence, meaning they treat all profile attributes as equally important and independent of one another, given a class label. However, we contend that this assumption is impractical in the context of social networks, as it directly contradicts the principle of homophily, which posits that individuals tend to form connections with those who share similar characteristics. To address this limitation, we propose WAPITI (Weighted Bayesian Model for Private Information Inference), a weighted Bayesian model tailored for attribute inference in social ego networks. Our approach incorporates attribute weighting to account for the dependencies between attributes, thus providing a more accurate representation of real-world social networks. Through extensive experimental evaluations on real-world datasets of user profiles and ego graphs, we demonstrate that the integration of attribute weighting substantially improves the overall performance of our inference model, yielding higher success rates compared to traditional baseline models.

Regrettable disclosures (i.e., things people wish they had not posted/shared) on social media platforms have become a serious issue in recent years. When engaged in self-presentation and impression management on online social networks (OSN), users often make disclosures that they subsequently regret. Such regrets have been shown to typically revolve around disclosures on sensitive topics or sharing content with strong sentiment, lies, and secrets. As such, regrettable self-disclosures do not only jeopardize peoples’ privacy but are also damaging to their public reputation and private relationships. In this work, we present WallGuard, a system for nudging OSN users towards detecting and avoiding embarrassing, privacy sensitive and regrettable online disclosures. WallGuard’s key building block is a hierarchical machine learning framework which provides mechanisms to predict regret-specific labels associated with any given user-generated text. To achieve this goal, we designed and experimented with new deep learning models and propose Regret Embeddings. The latter are domain-specific pre-trained word embeddings for regrettable disclosures. Extensive evaluations of the proposed models demonstrate their high classification performances (with a weighted AUC score of up to 0.975) on a real-world corpus of annotated regrettable user-generated texts. WallGuard allows OSN users to specify individual preferences with respect to the types of topical content to be shared with specific audiences. Therefore, while content analysis is done in an objective manner, nudgy personalized disclosure recommendations are generated based on user’s privacy preferences. A proof-of-concept of our tool is available, currently as a Facebook thirdparty app yet easily deployable on other social media platforms.

Scenarios for scalable and robust service discovery in MANETs differ vastly with respect to their basic conditions. Basic conditions, for example, are field size, level of mobility and number of nodes. Depending on context and scenario, a number of mechanisms for scalable and robust service discovery in MANETs were and are in discussion. This paper assigns service discovery solutions to scenario requirements and presents the performance of the hybrid concept Hydra. Hydra uses multiple basic mechanisms for service discovery that were combined to simultaneously fulfill the requirements for robustness, scalability and high service dynamics. The paper finishes with the vision of a scenario-independent service discovery toolbox for MANETs: Context parameters are controlled automatically and basic mechanisms can be exchanged as needed.

German Red Cross mobile hospitals must be operational in areas of war or disaster within a few days. In the ARDOR project, we collaborate with the German Red Cross in order to accelerate the workflows in these hospitals by developing and deploying a configurationless distributed information technology on top of a mobile ad hoc network . The main applications are scheduling of resources and their allocation. The computer-supported scheduling must enable the planning staff to query the currently available resources and services and the respective booking information. To achieve this, we introduce service discovery on the middleware layer. However, service discovery has to handle higher order (medical or logistic) services, as an entry in the schedule typically consists of the simultaneous allocation of many resources. Furthermore, service discovery must be scalable as the mobile ad hoc network might be highly dynamic and large in size. This paper claims that our self-organizing and scalable service discovery approach provides a way to enable reliable access to higher order services.