Ransomware has evolved into a complex, adaptive, and fast–moving adversary category in which static signatures and monolithic classifiers fail to generalise under concept drift, evasion, and behavioural polymorphism. In this paper we present Agentic SABRE (Semantic–Behavioural Arbitration for Ransomware Evaluation) : an uncertainty–aware, neuro–symbolic, multi–agent framework for adaptive ransomware detection. SABRE fuses semantic (representation–based) and behavioural (time–window forensic telemetry) evidence, and employs Monte Carlo Dropout inference to quantify epistemic uncertainty for each agent. We introduce a decision–layer orchestrator that performs risk– and uncertainty–aware triage via two interpretable thresholds: a risk score τ and an uncertainty budget κ . High–confidence, high–risk samples are automatically contained, while uncertain or borderline cases are escalated to human analysts, establishing a flexible computational contract between autonomous response and analyst oversight. To support auditability and trust, SABRE integrates post–hoc explainability mechanisms including gradient saliency, permutation importance, and counterfactual analysis, enabling both local and global interpretation of agent decisions. Extensive evaluation on RDset and RanSMAP demonstrates that Agentic SABRE preserves perfect discrimination on saturated semantic datasets (AUC = 1.0 ) while improving robustness under weak behavioural signals, achieving up to a 4.9% relative reduction in false escalations at equal recall and maintaining calibrated predictive uncertainty. Counterfactual analysis further shows that semantic and behavioural decisions can be flipped with bounded perturbation cost, indicating stable and interpretable decision boundaries. Overall, Agentic SABRE is not merely a higher–accuracy detector but an agentic cyber–defence system that combines uncertainty–aware automation, explainable reasoning, and adaptive triage under evolving ransomware threats.

Current mobile payment schemes gather detailed information about purchases customers make. This data can then be used to infer a customer’s spending behaviour, potentially violating their privacy. To tackle this problem, we propose an untraceable mobile payment scheme that strikes a better balance, preserving user privacy while allowing the Third-Party Service Provider (TPSP) to collect necessary information such as card details and transaction amount for regulatory compliance. Our scheme offers untraceability for legitimate users from malicious adversaries and curious TPSPs using cryptographic primitives such as partially blind signatures, zero-knowledge proofs, and identity-based signatures. It also guarantees that only authorised TPSPs can issue valid payment tokens, and even with limited data, the TPSP can still prevent dishonest customers/merchants from double-spending a payment token. We also propose a comprehensive evaluation framework to assess the untraceable payment schemes against seven key criteria such as untraceability, exculpability—merchant double-spending, exculpability—customer double-spending, unforgeability, confidentiality, message authenticity, efficiency, and regulatory compliance. We rigorously benchmark the security and privacy of our proposed payment scheme against this framework and other established schemes. Furthermore, we formally verify these properties using complexity-based analysis and Proverif modelling.

Recommendation systems rely heavily on behavioural and preferential data (e.g., ratings and likes) of a user to produce accurate recommendations. However, such unethical data aggregation and analytical practices of Service Providers (SP) causes privacy concerns among users. Local differential privacy (LDP) based perturbation mechanisms address this concern by adding noise to users’ data at the user-side before sending it to the SP. The SP then uses the perturbed data to perform recommendations. Although LDP protects the privacy of users from SP, it causes a substantial decline in recommendation accuracy. We propose an LDP-based Matrix Factorization (MF) with a Gaussian Mixture Model (MoG) to address this problem. The LDP perturbation mechanism, i.e., Bounded Laplace (BLP), regulates the effect of noise by confining the perturbed ratings to a predetermined domain. We derive a sufficient condition of the scale parameter for BLP to satisfy$\varepsilon$-LDP. We use the MoG model at the SP to estimate the noise added locally to the ratings and the MF algorithm to predict missing ratings. Our LDP based recommendation system improves the predictive accuracy without violating LDP principles. We demonstrate that our method offers a substantial increase in recommendation accuracy under a strong privacy guarantee through empirical evaluations on three real-world datasets, i.e., Movielens, Libimseti and Jester.

Many works have proposed integrating sentiment analysis with collaborative filtering algorithms to improve the accuracy of recommendation systems. As a result, service providers collect both reviews and ratings, which is increasingly causing privacy concerns among users. Several works have used the Local Differential Privacy (LDP) based input perturbation mechanism to address privacy concerns related to the aggregation of ratings. However, researchers have failed to address whether perturbing just ratings can protect the privacy of users when both reviews and ratings are collected. We answer this question in this paper by applying an LDP based perturbation mechanism in a recommendation system that integrates collaborative filtering with a sentiment analysis model. On the user-side, we use the Bounded Laplace mechanism (BLP) as the input rating perturbation method and Bidirectional Encoder Representations from Transformers (BERT) to tokenize the reviews. At the service provider’s side, we use Matrix Factorization (MF) with Mixture of Gaussian (MoG) as our collaborative filtering algorithm and Convolutional Neural Network (CNN) as the sentiment classification model. We demonstrate that our proposed recommendation system model produces adequate recommendation accuracy under strong privacy protection using Amazon’s review and rating datasets.