Evaluations of advice on online security, safety, and privacy given to end-users focus mostly on adults whereas it is unclear how these evaluations apply to advice given to children. To address this, we gathered and analyzed such advice with the help of handcrafted risk scenarios through an in-person study. Our scenarios are designed to cover multiple categories of online risks that threaten the security, safety, and privacy of children. This allows us to explore a broad spectrum of advice instead of focusing deeply on single technologies. Each scenario describes a child in a risky scenario and asks a study participant to provide advice to the child. We used these scenarios to collect advice from 287 children (aged 10–13), 195 parents, and 26 teachers. Our qualitative analysis of the resulting pieces of advice shows that participants generally recognize risks and provide useful preventive advice in scenarios in which an incident has not yet occurred. However, participants often misjudge the severity of digital threats and do not provide adequate advice in post-incident scenarios. Based on these findings, we propose avenues for further research and discussion, such as enhancing children’s agency in risk management through targeted education, developing practical response toolkits, and fostering community support among families.
Trigger-based watermarking aims to protect the intellectual property of machine learning models by embedding abnormal behavior that is activated only on specific trigger inputs, while preserving performance on standard test data. Although widely studied, current approaches to trigger-based watermarking remain largely informal and empirical. In this work, we introduce a formal framework for reasoning about the security of trigger-based watermarking, and we propose rigorous security definitions to evaluate existing schemes. Our analysis reveals that many schemes rely on underspecified parameters and flawed design assumptions. Notably, we demonstrate that different security goals in watermarking can be in tension with one another, and achieving a balance among them requires careful design. Our principled analysis offers a way to resolve this tension in practice. Experiments on existing schemes and public datasets corroborate the relevance of our theoretical findings.
Federated Recommender Systems (FRSs) enable on-device personalization while preserving user privacy, yet the gradients shared during model training can still leak sensitive information. Local differential privacy (LDP) offers a formal defense, but classic mechanisms—randomized response and Laplace perturbation—inject substantial noise into the high-dimensional gradient vectors of latent-factor models, severely degrading recommendation accuracy. In this paper, we systematically evaluate these traditional LDP strategies in federated matrix factorization under both explicit (MovieLens 100K and 1M) and implicit (Steam, LastFM) feedback benchmarks. We then propose a novel directional noise mechanism based on the von Mises–Fisher distribution, which preserves gradient magnitudes while randomizing directions. Through extensive experiments across a wide range of privacy budgets, we show that our vMF mechanism consistently outperforms Laplace and binary randomized-response, recovering up to more than 100 % of the non-private utility at ε = 1 and delivering higher ranking accuracy under moderate privacy levels (ε ≤ 2). We further demonstrate that directional perturbation reduces variance and remains robust under partial client participation. Our findings highlight the importance of data geometry in privacy–utility trade-offs and provide practical guidance for deploying LDP in high-dimensional federated recommendation settings.
Account ecosystems comprise a set of resources, including online accounts, devices, and credentials, that jointly impact a user’s or organisation’s security. Prior work shows that users frequently overlook subtle dependencies within these ecosystems, leading to security breaches that are difficult to detect using analysis techniques focused on isolated components.We introduce resource access graphs (RAGs), a formal model for the automated analysis of dynamic account ecosystems. RAGs extend previous work on account access graphs (AAGs) with partially ordered labels for vertices and edges, enabling both general and resource-specific actions to be modelled. To capture the dynamic nature of account ecosystems, we define a transition system based on graph rewriting with RAGs as states in which both user actions and adversarial capabilities are modelled as graph transformation rules. Security properties are specified in computation tree logic (CTL) allowing analysis using existing graph-rewriting based model checkers, such as GROOVE.We demonstrate our approach on a simple Apple account ecosystem with the recent security feature Stolen Device Protection (SDP). Our analysis reveals limitations in SDP, affecting both the integrity and availability of a user’s Apple Account and we propose and verify an improvement to enhance account integrity.
