Secure cloud storage, an emerging cloud service, guarantees the confidentiality of outsourced data while providing flexible data access control for cloud users whose data are out of their physical control. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is one of the promising secure mechanisms to support fine-grained access control on encrypted data in cloud settings. However, due to its inherent "all-or-nothing" decryption control characteristic, there is a risk for the misuse of access credentials. In this paper, we consider the two main types of access credential misuse, namely: semi-trusted authority's illegal access credential (re-)distribution, and cloud user's illegal access credential leakage. To mitigate these two types of access credential misuse, we propose the first accountable authority revokable CP-ABE based cloud storage system with white-box traceability and auditing, referred to as CryptCloud+. We also prove the security of our system and present the experimental results to demonstrate the utility of our system.

The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system, as well as to enforce customer confidence in digital payment systems. Historically, credit card companies have used rule-based approaches to detect fraudulent transactions, but these have proven inadequate due to the complexity of fraud strategies and have been replaced by much more powerful solutions based on machine learning or deep learning algorithms. Despite significant progress, the current approaches to fraud detection suffer from a number of limitations: for example, it is unclear whether some transaction features are more effective than others in discriminating fraudulent transactions, and they often neglect possible correlations among transactions, even though they could reveal illicit behaviour. In this paper, we propose a novel credit card fraud detection (CCFD) method based on a transaction behaviour-based hierarchical gated network. First, we introduce a feature-oriented extraction module capable of identifying key features from original transactions, and such analysis is effective in revealing the behavioural characteristics of fraudsters. Second, we design a transaction-oriented extraction module capable of capturing the correlation between users' historical and current transactional behaviour. Such information is crucial for revealing users' sequential behaviour patterns. Our approach, called transactional-behaviour-based hierarchical gated network model (TbHGN), extracts two types of new transactional features, which are then combined in a feature interaction module to learn the final transactional representations used for CCFD. We have conducted extensive experiments on a real-world credit card transaction dataset with an increase in average F1 between 1.42% and 6.53% and an improvement in average AUC between 0.63% and 2.78% over the state of the art.

In the digital era, effective Transaction Fraud Detection (TFD) is essential to ensuring financial security. The considerable class imbalance, with legitimate transactions vastly outnumbering fraudulent ones, presents a significant challenge for TFD models to accurately identify fraudulent patterns. While existing sample-balancing strategies address class imbalance effectively in many contexts, they often fall short in TFD due to fraudsters’ sophisticated concealment tactics, which lead to pronounced behavioral overlap between fraudulent and legitimate transactions. In this paper, we introduce a novel Generative Adversarial Network-based Hybrid Sampling method (GANHS) to effectively address the class imbalance issue. GANHS employs a dual-discriminator generative adversarial network to generate synthetic samples that accurately reflect the characteristics of fraudulent activity, while an adaptive neighborhood-based undersampling technique refines these samples to minimize overlap with legitimate ones. This hybrid approach not only enhances the model’s ability to learn fraud patterns by generating high-quality samples but also improves its resilience against highly concealed fraudulent activities. Experiments on real-world and public datasets demonstrate that GANHS outperforms its competitive peers, with gains of 0.5%–8.7% in average $F_{1}$-Score and 1.0%–7.0% in G-mean, highlighting its strong potential for improving the reliability and effectiveness of TFD systems in complex, high-risk financial scenarios.

Secure cloud storage offers encrypted databases outsourcing service for resource-constrained clients, containing numerous tables with certain relations. Searchable symmetric encryption enables a client to search over its encrypted database on the cloud, while rarely considering queries over joins of tables. Join Cross-Tags (JXT) protocol (ASIACRYPT 2022) is thence presented that enables conjunctive queries over joins of tables, while neglecting arbitrary Boolean queries with disjunctive and conjunctive normal forms (DNF/CNF) in TWINSSE (PETS 2023). However, trivially combining JXT and TWINSSE for arbitrary DNF/CNF boolean queries over joins of tables seems infeasible due to: (i) no support for dis/conjunctive query with the same meta-keyword; (ii) returning inaccurate search results; (iii) incurring costly storage overhead. Therefore, we introduce TNT-QJ, a practical TwiN cross-Tag protocol for arbitrary boolean Query-Join over multi-tables. The result is technically obtained from revisiting TWINSSE’s framework via using s -term (the least frequent keyword) for the relation between a keyword and its meta-keyword, and non-trivially combined with JXT’s query-join approach for introducing a connective attributed in encryption tuples. In addition, we present a semi-full multi-fork searchable tree to store keyword information and reveal keyword containment relations, where the storage consumption is reduced from O ( n ³ ) to O ( n ² ). Finally, to clarify practical performance, we conduct extensive experiments on JXT and TNT-QJ using an open database in the HUAWEI cloud. Besides enabling disjunctive queries over joins of tables, TNT-QJ also runs 1.2× faster for conjunctive queries than JXT (with #keywords=2), which confirms rich features and practical efficiency.