Search Results (81)

Cloud data sharing allows users to outsource data storage and management to external servers, but it also raises privacy concerns when data must remain encrypted while still supporting efficient search and fine-grained access control. Attribute-based encryption with keyword search (ABE-EKS) addresses this problem by combining searchable encryption with attribute-based access policies. However, existing schemes often focus on separate aspects such as expressive search, online/offline computation, revocation, or policy protection, while controlled trapdoor delegation and inequality-based keyword predicates are less commonly considered together in an implementation-oriented Type-3 pairing setting. This paper revisits the ABE-EKS framework of Yang et al. and adapts it to the Type-3 pairing setting. The construction follows the main structure of Yang et al.’s ABE-EKS scheme, but places the group elements in the appropriate source groups for asymmetric pairings. It further supports expressive access-control and keyword-search policies, Boolean keyword predicates, inequality comparisons, and trapdoor key delegation. We also analyse its security under the honest-but-curious cloud model and implement the scheme using the Apache Milagro Cryptographic Library (AMCL). Experimental results on the Enron Email dataset demonstrate the feasibility of the proposed construction for outsourced encrypted data-sharing scenarios. Full article

The growing volume of electronic health records (EHRs) has made cloud-assisted storage increasingly important, but outsourced records require both encrypted storage and controlled retrieval. Attribute-based keyword searchable encryption (ABKS) combines search with fine-grained authorization; nevertheless, many existing constructions provide only restricted Boolean queries, particularly when negation is involved. This paper presents a ciphertext-policy ABKS search layer for EHR sharing. The proposed construction operates over a fixed application dictionary and assumes a non-colluding honest-but-curious cloud server. Each record is represented by one signed literal per dictionary term, where a positive literal denotes presence and a negated literal denotes absence. Before query evaluation, Boolean formulas involving AND, OR, and NOT are converted into negation normal form using De Morgan’s laws, so that NOT appears only at leaf nodes. The resulting query is then evaluated as a monotone threshold tree. An authorized user can generate a valid query token only when the user’s multi-valued attribute vector matches the policy selected by the data owner. Keyed pseudorandom tags hide literal and policy values from the cloud server while enabling efficient component lookup. We define a selective chosen-keyword security model, state the resulting leakage, and give a game-based proof under the decisional bilinear Diffie–Hellman and pseudorandom-function assumptions. Theoretical and implementation-based comparisons show that the pairing-dominant costs grow linearly with the attribute and dictionary sizes. The construction therefore improves Boolean expressiveness while retaining practical search-layer overhead under the stated assumptions. Full article

We set out to fix a “contamination” problem in reconstruction-based Temporal Convolutional Network VAEs (TCN-VAEs) for cross-dataset IoT flow anomaly detection: when attack flows share an encoder window with benign flows, the shared latent code is allegedly distorted, inflating benign reconstruction error and producing false positive rates (FPRs) of 22–65% despite an ROC-AUC above 0.93. Our proposed fix, TCN-Pred, excludes the target flow from the encoder and scores it by next-flow prediction error, reducing FPR to 0.65–13%. We subjected this causal explanation to a battery of controlled ablations, holding architecture, decoder, loss, and thresholding fixed while varying one factor at a time. Each one falsified the original hypothesis: target inclusion/masking changes FPR by at most 0.001; context shuffling/reversing/zeroing changes it by at most 0.003; a context-blind constant-output predictor matches TCN-Pred’s FPR and F1 to three decimal places on all three datasets. The actual cause, confirmed on the original trained models with no retraining, is a scoring-granularity mismatch: the TCN-VAE threshold is calibrated from per-window errors averaged over 20 flows but applied to per-flow errors at evaluation (standard deviation $\approx \sqrt{20}\times$ higher, measured ratio 4.46 against a predicted 4.47). Recalibrating the identical model at matching granularity drops FPR from 22.7/47.6/64.6% to 0.65/5.0/12.5% on BoT-IoT, IoT-23 and ToN-IoT, closing 89–97% of the reported FPR gap without changing a single model weight. We report this diagnostic chain, together with an attack-prevalence sensitivity analysis, sample-disjoint calibration, normality diagnostics, and label-free and redundancy-aware (mRMR) feature-selection benchmarks, as a methodology other work should apply before attributing fixed-threshold performance to architecture. The pipeline is supervised source-domain feature selection followed by benign-only detector training, not fully unsupervised, a distinction we quantify later in the paper. Investigating dataset representativeness, we found that all three provided files reduce to only ≈6000 genuinely distinct flows via an undocumented row-duplication procedure, causing 97.8% BoT-IoT train/test near-duplicate overlap; a leakage-free re-evaluation changes FPR by only 0.23 percentage points. We also found that the TLS-metadata columns are already transformed upstream of every available artefact, so the proportion of genuinely TLS-encrypted flows cannot be recovered, and we soften the paper’s encrypted-traffic framing accordingly. Full article

Outsourcing Internet-of-Things (IoT) data and computation to cloud and fog infrastructure exposes both the data and the access-control process to integrity, confidentiality, and privacy risks. Attribute-based encryption (ABE) provides fine-grained access control but, as deployed today, suffers from single-authority bottlenecks, expensive policy updates, weak auditability, and exposure to secret-key leakage, classical primitives are additionally threatened by future quantum adversaries. This paper does not propose a new cryptographic scheme. Instead, it contributes a conceptual reference architecture that systematizes how a set of existing, standardized primitives can be composed into a single access-control framework for IoT outsourcing, and it makes the resulting design precise enough to reason about. Concretely, we (i) define a system model and a threat model covering passive, active, colluding, bounded-leakage, and harvest-now-decrypt-later quantum adversaries; (ii) instantiate each layer with a named construction decentralized multi-authority ABE, attribute-based proxy re-encryption for policy updates, a bounded leakage resilient key model, ASCON lightweight AEAD, and ML-KEM/ML-DSA post-quantum primitives, together with a permissioned, on-chain digest/off-chain payload logging layer; (iii) specify the end-to-end data flow and module interfaces; and (iv) give a goal-by-goal security rationale and an analytical evaluation based only on standardized parameter sizes and asymptotic complexity. We are explicit about what is inherited from prior work, what remains to be proven for the composed system, and that a measured prototype evaluation remains future work. The intended value of this paper is to provide a clear, composable, and honestly scoped design that subsequent implementation studies can build upon. Full article

The growing complexity of cryptocurrency ecosystems has created substantial difficulties for anti-money laundering (AML) enforcement across jurisdictions. Existing AML information-sharing mechanisms are fragmented, slow, and constrained by privacy regulations. This paper presents CAHE-AML, a risk-adaptive architecture built on a context-aware hybrid encryption framework designed to enable secure and privacy-preserving cross-border AML data exchange between regulatory authorities and virtual asset service providers (VASPs). CAHE-AML integrates symmetric encryption, attribute-based encryption (ABE), a decentralized key governance model, and heuristic risk-scoring mechanisms for dynamic policy adaptation. Using an Ethereum Fraud Dataset, we compute risk scores to dynamically assign context-aware access policies across three hierarchical tiers: local, regional, and global. Experimental evaluation on an enterprise-grade multi-core system demonstrates high computational efficiency, achieving substantial throughput and low processing latency. The system identifies integrity breaches with high accuracy while maintaining a manageable data expansion ratio. These results highlight the computational feasibility of CAHE-AML’s cryptographic layer for real-time, high-frequency AML data processing in decentralized financial systems. Full article

Internet of Things (IoT) devices are increasingly targeted by rapidly evolving malware, yet collaborative detection remains challenged by privacy leakage, noisy and imbalanced training data, and weak integrity guarantees when sharing model updates. This paper presents Mal-Fedchain, a secure and privacy-preserving framework for image-based IoT malware detection and prevention that couples federated learning with blockchain and honeypot-assisted behavioral monitoring, targeting Linux-capable IoT gateway devices. Portable Executable (PE) binaries are transformed into grayscale images using a corrected fixed-width byte-mapping pipeline stabilized by an information-maximizing GAN (IMGAN). A bi-level preprocessing pipeline applies two-sided weighted sparse representation (T-WSR) denoising—designed to selectively suppress zero-padding artifacts, high-entropy packed regions, and sparse opcode noise while preserving discriminative section-boundary texture—followed by geometric augmentation to mitigate class imbalance. Malware detection and family attribution are performed using a residual capsule-based network (RBCN) that fuses discriminative visual representations with PE-header features via concatenation, improving robustness against polymorphism and obfuscation. A formal threat model governs three adversary classes: a semi-honest aggregation server, a bounded fraction of malicious clients (up to 30%), and a passive eavesdropper. To enable collaboration without exposing raw data, clients train locally and share only MemCbar-encrypted updates; a permissioned Hyperledger Fabric blockchain ledger records hashed updates and security events to provide integrity, traceability, and tamper resistance. A file-system-integrated honeypot captures evasive behaviors and logs auditable evidence to strengthen prevention. Experiments on the Malimg dataset across five ablation configurations demonstrate that the corrected RBCN pipeline achieves $93.52\%$ accuracy, $92.40\%$ precision, $93.52\%$ recall, $92.52\%$ F-measure, MCC of $0.9245$, and AUC of $0.9976$ in its centralized configuration, and $65.62\%$ accuracy with AUC of $0.9840$ in the full federated configuration with five clients and eight communication rounds, substantially outperforming all baselines across all reported metrics. Full article

Threshold Attribute-based Encryption has attracted significant attention due to its growing importance in practical applications, such as distributed cloud storage or anonymous access control. In a threshold attribute-based encryption scheme, a sender can select a set of attributes and a corresponding threshold t, which is referred to as an access policy, to encrypt a message. Decryption is successful if and only if a user possesses at least t attributes from the specified attribute set. Existing threshold attribute-based encryption schemes typically consider only the setting in which a single message is encrypted under a single access policy. However, in many practical applications, more flexible encryption scenarios are needed, such as encrypting a single message under multiple access policies or encrypting multiple messages under their corresponding access policies. In this work, we first formalize the notion of threshold attribute-based encryption supporting the encryption of multiple messages under multiple access policies. We then propose the first construction of a threshold attribute-based encryption scheme based on the Key Encapsulation Mechanism paradigm that supports such functionality while achieving constant-size ciphertext. Our proposed scheme relies on bilinear pairings and is proven secure in the Generic Bilinear Group Model. As a classical pairing-based construction, it does not provide post-quantum security and is therefore unsuitable for scenarios requiring long-term confidentiality or resilience against harvest-now, decrypt-later attacks. Full article

The rapid growth of the Internet of Things (IoT) has increased dependency on Transport Layer Security (TLS) for securing device communications, enhancing confidentiality while reducing the visibility required by traditional intrusion detection systems. As payload inspection becomes impractical in encrypted environments, anomaly detection must instead rely on flow-level statistics and TLS metadata. This is challenging because IoT traffic is heterogeneous, non-stationary, and distributionally inconsistent across datasets, while many existing studies rely on single-dataset evaluation and therefore provide limited evidence of real-world generalisation. We introduce a TLS-aware anomaly detection framework that combines a $\beta$-Variational Autoencoder ($\beta$-VAE) with a hybrid ANOVA–Mutual Information (ANOVA–MI) feature-selection pipeline. The incremental contribution lies not in the individual use of these components, but in their integrated application to encrypted IoT anomaly detection under strict cross-dataset evaluation, where feature filtering, probabilistic latent regularisation, and threshold transferability are jointly examined without retraining or recalibration on target datasets. The framework models benign encrypted IoT traffic using probabilistic latent representations and identifies anomalies through reconstruction-error-based scoring. Network flows from the BoT-IoT, IoT-23, and ToN-IoT datasets were processed using Zeek and CICFlowMeter to construct a unified metadata feature space incorporating flow statistics and TLS attributes such as JA3 and JA3S fingerprints. The model was trained on benign BoT-IoT traffic and evaluated in both in-dataset and cross-dataset scenarios. The model achieves strong in-dataset performance on BoT-IoT (ROC-AUC $\approx 0.9996$; F1 $\approx 0.9922$) and retains robust anomaly-ranking and threshold-based detection capability under cross-dataset domain shift (IoT-23: ROC-AUC $\approx 0.9882$, F1 $\approx 0.9422$; ToN-IoT: ROC-AUC $\approx 0.9465$, F1 $\approx 0.8732$). A comparative evaluation against deterministic autoencoders and classical baselines further indicates that the proposed $\beta$-VAE achieves stronger cross-dataset anomaly-ranking performance than the compared methods. These findings support the suitability of probabilistic latent modelling for privacy-preserving anomaly detection in encrypted IoT environments. Full article

As cloud computing proliferates, outsourced data faces severe security threats, yet existing searchable encryption (SE) schemes rely on classical hardness assumptions, centralized trust authorities, and static access control, leaving critical gaps in quantum resistance, single-point-of-failure prevention, and dynamic permission management. To address these limitations, we propose BL-ABSE, a blockchain-enhanced, lattice-based attribute-based searchable encryption framework. BL-ABSE employs the Ring Learning With Errors (RLWE) problem as its security foundation and applies the Number Theoretic Transform (NTT) to reduce polynomial multiplication from $O\left(n^2\right)$ to $O(nlogn)$. To eliminate single-point trust risks, the framework further integrates a $(t,n)$ threshold key protocol across an edge-node consortium governed by Practical Byzantine Fault Tolerance (PBFT) consensus. A smart-contract-maintained on-chain revocation list enables permission withdrawal via a single blockchain transaction without re-encryption. Experimental evaluation demonstrates that commitment generation requires approximately 23 ms at $n=1024$, search latency scales linearly at roughly 29 µs per record, and revocation completes in approximately 2 s regardless of system scale. Formal security proofs under the quantum polynomial-time (QPT) adversary model reduce six security properties—index indistinguishability, query privacy, threshold key security, Byzantine fault tolerance, audit immutability, and revocation immediacy—to the hardness of RLWE and the Short Integer Solution (SIS) problems. To the best of our knowledge, BL-ABSE is the first framework to simultaneously achieve post-quantum security, attribute-based access control, decentralized key management, instant revocation, and immutable auditing within a single unified framework. We further conduct threshold parameter verification, end-to-end revocation latency decomposition, blockchain throughput stress testing, search-pattern leakage quantification, and communication/storage overhead analysis, providing a comprehensive evaluation of both performance and security trade-offs. We explicitly characterize the search-pattern leakage inherent in the deterministic commitment design as a correctness–privacy trade-off and discuss mitigation directions. Full article

Encrypted traffic classification is essential for network management and security, yet payload inspection is ineffective under modern protocols such as Transport Layer Security (TLS) and Quick UDP Internet Connections (QUIC). Existing metadata-based methods perform well for coarse-grained tasks but often fail to distinguish structurally similar applications because they model temporal behavior only implicitly or coarsely. We propose the Bi-Directional Directed Temporal Graph (BiDT), a framework based on a Directed Temporal Interaction Graph (DTIG) and a Bi-Directional GraphSAGE (BiGraphSAGE). The DTIG represents packets as nodes and explicitly encodes inter-arrival times (IATs) as directed edge attributes, preserving both causal structure and communication rhythm. The BiGraphSAGE then aggregates temporal interaction features from forward and backward perspectives. We evaluated the BiDT on the VNAT benchmark and validated it on ISCX-VPN. On the challenging 10-class VNAT dataset, the BiDT achieves 98.57% accuracy and outperforms strong baselines, including complete separation of easily confused protocols such as SCP and SFTP. The results on ISCX-VPN further confirm the effectiveness of the proposed design. These findings show that explicit temporal edge modeling is effective for fine-grained encrypted traffic classification. Full article

With the rapid development of cloud-based data sharing technologies, enterprises and organizations tend to outsource their local data to cloud servers. They adopt searchable encryption (SE) techniques to access and search encrypted data. However, most existing SE schemes use static ranking strategies based on query–index similarity. These strategies fail to capture users’ personalized retrieval preferences and often result in suboptimal search performance. In this article, we present a privacy-preserving data sharing framework with personalized encrypted retrieval (PP-PER) that combines SE technology with federated learning. PP-PER trains user interest models locally on user devices by utilizing historical query behavior. Only encrypted model parameters are uploaded for aggregation, which avoids the centralized collection of users’ private data. In addition, we design an attention-based user query update algorithm. The learned personalized features are integrated into the ciphertext query process. This design enables personalized ranking results and improves the user retrieval experience. Furthermore, PP-PER combines matrix factorization with ciphertext-policy attribute-based encryption (CP-ABE). This mechanism ensures secure document key distribution and supports fine-grained access control. Finally, we formalize the security model under a practical threat and leakage setting and provide a theoretical analysis of the proposed scheme. Experimental results on real-world datasets further validated its practicality and effectiveness. Full article

Attribute-based encryption (ABE) provides a robust mechanism for fine-grained access control, making it an ideal candidate for secure cloud data sharing. However, existing schemes often incur significant computational overhead, hindering their large-scale deployment, especially on resource-constrained nodes. In this work, we propose a practical ABE scheme that simultaneously simplifies access policy structures and enhances overall efficiency. By introducing a weighted access policy, our scheme achieves rich expressiveness while maintaining a compact logic structure, offering enhanced flexibility through the redefinability of attribute weights. Notably, the proposed construction is pairing-free and yields small-size ciphertexts and private keys compared to traditional tree-based models. Security analysis demonstrates that our scheme is selectively secure against chosen-ciphertext attacks. Extensive simulation results show that encryption and decryption latency is reduced to nearly 10 ms when 20 attributes are involved, which is a typical requirement in cloud data sharing scenarios. This validates the efficiency of our scheme in resource-constrained environments. Full article

In modern communication networks, particularly satellite-based systems, data security faces significant challenges from vulnerabilities such as signal interception, jamming, and latency during long distance transmissions. Traditional cryptographic methods are increasingly vulnerable to quantum computing threats, underscoring the need for advanced solutions to protect data integrity, confidentiality, and availability. This research investigates the fusion of quantum cryptography and Machine Learning (ML) to improve security in satellite communication. The Quantum Key Distribution (QKD), which is grounded in quantum mechanics, enables unbreakable encryption by detecting eavesdropping via quantum state disturbances. The CatBoost ML algorithm is applied to a dataset of 10,000 records featuring categorical attributes for prioritizing security elements such as anomaly detection, encryption types, and access controls. The model yields an accuracy of 89.23% and Area under Curve the Receiver Operating Characteristic (AUC-ROC) score of 94.56%, effectively predicting threat levels. Feature importance reveals anomaly detection (28.5%) and quantum encryption (22.3%) as primary contributors. While hurdles such as high implementation costs and transmission range limitations persist, this quantum ML synergy provides a proactive, adaptive framework for resilient, future-ready communication networks. Full article

Due to the rapid proliferation and evolution of the Internet of Things (IoT) in industrial and smart city applications, concerns over sensitive data security have become increasingly prominent. This is especially true in resource-constrained “cloud–terminal” centralized architectures, where ensuring privacy protection for downlink data and implementing fine-grained access control have become critical. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) serves as an effective solution due to its fine-grained access control capability. Nevertheless, conventional CP-ABE approaches face notable limitations when deployed in these practical settings, including the lack of an efficient and lightweight client-side revocation mechanism, excessive decryption overhead on terminal devices, and the practical difficulty in balancing security with performance. To address these issues, this paper proposes LOR-A2ABE, a Lightweight, Outsourced, and Revocable Anonymous Attribute-Based Encryption scheme. The scheme achieves lightweight client-side revocation through partial updates by embedding version numbers and timestamps into keys and ciphertexts via hash mapping. Furthermore, it employs outsourcing to offload the majority of computations to the cloud, allowing client-side decryption with only constant, low-complexity operations, thereby significantly reducing the computational burden on resource-constrained terminals. Considering the practical context where client devices are typically resource-limited sensors or microcontrollers and downlink data often require real-time processing, our scheme adopts a practical security model optimized for IoT constraints. This model prioritizes forward security and efficient revocation—the most critical requirements for operational IoT systems—while maintaining provable security under the Decisional Linear (DLIN) assumption within a bounded collusion model, achieving IND-CPA security and anonymity. Theoretical analysis and experimental simulations show that LOR-A2ABE incurs acceptable and controllable overhead in the key issuance and encryption phases, while outperforming most existing schemes in decryption and revocation efficiency, making it particularly suitable for “cloud–terminal” centralized IoT environments where terminal devices are resource-constrained and require frequent decryption operations. Full article

With the rapid development of online aquatic product trading, traditional centralized platforms are facing increasing pressure in terms of data security, privacy protection, and trust. Problems such as tampering with transaction records, weak identity authentication, privacy leakage, and the difficulty of balancing matching efficiency with security limit the further development of these platforms. To address these issues, this paper proposes a blockchain-based identity authentication and access control scheme for online aquatic product trading. The scheme first introduces a dual authentication mechanism that combines a verifiable random function with a Schnorr-based zero-knowledge proof, providing strong decentralized identity verification and resistance to replay attacks. It then designs a dynamic access control strategy based on a multi-dimensional reputation model, which converts user behavior, attributes, and historical transaction performance into a comprehensive trust score used to determine fine-grained access rights. In addition, an AES-PEKS hybrid encryption method is employed to support encrypted keyword search and order matching while protecting the confidentiality of order data. This paper implements a multi-channel architecture for aquatic product trading prototype system on Hyperledger Fabric. This system separates registration, order processing, and reputation management into different channels to improve concurrency and enhance privacy protection. Security analysis shows that the proposed solution effectively defends against replay attacks, key leaks, data tampering, and privacy theft. Performance evaluation further demonstrates that, compared to a single-chain architecture, the multi-channel design, while increasing security mechanisms, maintains a stable throughput of approximately 223 tx/s even when concurrency reaches 600–800 tx/s, ensuring normal operation of the trading system. These results indicate that this solution provides a practical technical approach and system-level reference for building secure, reliable, and efficient online aquatic product trading platforms. Full article