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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.

In this paper, we present a framework and an architecture that aim to enable and manage trust in supply chains. Our architecture addresses the authenticity and integrity of devices and processes within heterogeneous system landscapes. We identify and discuss the current challenges in digital supply chains and lay out security, privacy, and interoperability requirements that must be met for successful implementation. We hypothesize that the overall perception of trust in a supply chain depends on the trustworthiness of all digital systems involved, including hardware, software, and information flow. Our proposed architecture helps enhance trustworthiness based on verifiable, indisputable, and believable digital evidence for devices and processes in supply chains, including the entire hardware and software lifecycles. We actively advocate for a mixed landscape of centralized and decentralized solutions for the storage of evidence and trust information. This can include traditional centralized databases and distributed ledger technologies. We discuss the auditability and accountability of digital evidence using trust-enabling technologies, and present a preliminary proof-of-concept (PoC) implementation in a real-world application scenario.

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.

We develop WallGuard for helping users in online social networks (OSNs) avoid regrettable posts and disclosure of sensitive information. Using WallGuard the users can control their posts and can (i) detect inappropriate, regrettable messages before they are posted, as well as (ii) identify already posted messages that could negatively impact user's reputation and life. WallGuard is based on deep learning architectures and NLP based methods. To evaluate the effectiveness of WallGuard, we developed a semi-supervised self-training methodology, which we use to create a new, large-scale corpus for regret detection with 4,7 million OSN messages. The corpus is generated by incrementally labelling messages from large OSN platforms relying on human-labelled and machine-labelled messages. Training Facebook's FastText word embeddings and Word2vec embeddings on our corpus, we created domain specific word embeddings, we referred to as regret embeddings. Our approach allows us to extract features that are discriminative/intrinsic for regrettable disclosures. Leveraging both regret embeddings and the new corpus, we successfully train and evaluate five new multi-label deep-learning based models for automatically classifying regrettable posts. Our evaluation of the proposed models demonstrate that we can detect messages with regrettable topics, achieving up to 0,975 weighted AUC, 82,2% precision and 74,6% recall. WallGuard is free and open-source.

Protecting energy consumers's data and privacy is a key factor for the further adoption and diffusion of smart grid technologies and applications. However, current smart grid initiatives and implementations around the globe tend to either focus on the need for technical security to the detriment of privacy or consider privacy as a feature to add after system design. This paper aims to contribute towards filling the gap between this fact and the accepted wisdom that privacy concerns should be addressed as early as possible (preferably when modeling system's requirements). We present a methodological framework for tackling privacy concerns throughout all phases of the smart grid system development process. We describe methods and guiding principles to help smart grid engineers to elicit and analyze privacy threats and requirements from the outset of the system development, and derive the best suitable countermeasures, i.e. privacy enhancing technologies (PETs), accordingly. The paper also provides a summary of modern PETs, and discusses their context of use and contributions with respect to the underlying privacy engineering challenges and the smart grid setting being considered.