Computer Standards & Interfaces最新文献

Electroencephalography (EEG) allows the investigation of brain activity. However, neural signals often contain artefacts, hindering signal analysis. For example, eye-blink artefacts are particularly challenging due to their frequency overlap with neural signals. Artificial intelligence, particularly Variational Autoencoders (VAE), has shown promise in EEG artefact removal. This research explores the design and application of Convolutional VAEs for automatically detecting and removing eye blinks in EEG signals. The latent space of CVAE, trained on EEG topographic maps, is used to identify latent components that are selective for eye blinks. Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) are employed to evaluate the discriminative performance of each latent component. The most discriminative component, determined by the highest AUC, is modified to eliminate eye blinks. The evaluation of artefact removal involves visual inspection and Pearson correlation index assessment of the original EEG signal and the reconstructed clean version, focusing on the $Fp1$ and $Fp2$ channels most affected by eye-blink artefacts. Results indicate that the proposed method effectively removes eye blinks without significant loss of information related to the neural signal, demonstrating Pearson correlation values around 0.60 for each subject. The contribution to the knowledge offered by this research study is the design and application of a novel offline pipeline for automatically detecting and removing eye blinks from multi-variate EEG signals without human intervention.

Technology has become ubiquitous, and humans are increasingly dependent on it. Concepts like Smart and Cyber–Physical Systems (CPS), and the Internet of Things (IoT), are frequently used to describe applications and systems that surround us. These concepts commonly encompass applications that are able to sense their environment, collect and process data, make inferences, and communicate with other applications. However, most of the systems that currently exist do not account for human actions and intents, treating humans as mere sources of data. Furthermore, even the few systems that consider the human factor only do so to a very limited extent. On the premise that technology is made by people for people, new human-centric paradigms are needed, in which emotions, human states, and actions can be conveyed into the system. In this paper, we delve into the concept of Human-in-the-Loop Cyber–Physical Systems (HiTLCPS) and the challenges it presents. Building upon this foundation, we propose a novel approach that integrates humans into all phases of the cyber–physical loop. This comprehensive integration entails influencing human emotions and states, and incorporating human actions into the system architecture and functionalities. Finally, to demonstrate the feasibility and effectiveness of our proposed model, we provide concrete implementation examples in this paper. These examples, along with associated case studies, offer insights into how our human-centric paradigm enhances system performance, user experience, and overall effectiveness.

Ring signatures are significant cryptographic primitives used for anonymous authentication due to their anonymity and spontaneity. However, in some scenarios, they may offer excessive anonymity to the signer. Revocable ring signatures aim to preserve the desirable properties of ring signatures while incorporating the accountability aspect of group signatures, leading a better trade-off between anonymity and accountability. Nevertheless, most revocable ring signature schemes only provide relatively weak CPA-Anonymity. In this paper, we present an instantiation of CCA-Anonymity from standard lattices, and prove its security under the random oracle model. Our construction achieves stronger anonymity and revocability, while relying on weaker assumptions than existing schemes from module lattices. Moreover, it boasts smaller sizes when the ring is large.

Blockchain technology, successful in cryptocurrency, holds transformative potential for various industries. In decentralized applications driven by blockchain, acquiring non-digitally verifiable data is crucial, particularly in smart contracts. To facilitate the transfer of off-chain data from websites to the blockchain, several studies have proposed various centralized and decentralized blockchain Oracles. However, centralized Oracles reintroduce central risks, such as the Single Point of Failure, while decentralized Oracles consistently rely on a voting mechanism, which incurs additional vulnerabilities and substantial costs. In this work, we uncover a misdirection attack in decentralized Oracles, resulting in a diminished security region in blockchain consensus. This manifests as prolonged settle times and reduced upper bounds for tolerance of adversarial consensus chip. By formalizing the voting process as a discrete-time Markov chain (DTMC), we further demonstrate that the maximum stake of the adversary for launching a misdirection attack is less than 50%. To counteract this threat, we introduce a novel voting model that relies on chain length rather than block data for voting. Formally, we propose the definition of our model named $VoteFork$. Following this, we present the specific consensus-based blockchain Oracle protocol, PSCBO. This protocol involves blockchain clients collecting and providing Oracle data, with an epoch-based voting mechanism to verify each set of Oracle information. We also analyze the security of the PSCBO backbone $VoteFork$, demonstrating its security region akin to a typical blockchain. Finally, we implement a proof-of-concept system to validate the security region and further underscore the practicality. Both security analysis and experiments affirm that PSCBO exhibits a higher adversarial fault tolerance and shorter waiting time to confirm transactions.

The Substitution box (S-box) is the main nonlinear component responsible for the cryptographic strength of any Substitution-Permutation Network (SPN) based block cipher. Generating the S-box with optimal cryptographic properties is one of cryptography's most challenging combinatorial problems because of its enormous search space, lack of guidance, and conflicting performance criteria. This paper introduces a novel Chaotic Opposition-based Learning Initialized Hybrid Algebraic-Heuristic (COBLAH) algorithm, combining the favorable traits of Algebraic and heuristics methods based on Galois field inversion, affine mapping, and Genetic Algorithm (GA). The Galois field inversion and affine mapping are used to construct the S-box, while the GA guides the algebraic construction to find the best bit-matrix and additive vector based on any irreducible polynomial for GF(2⁸). GA initializes with a random population generated using a newly constructed cosine-cubic map incorporated with binarization and Opposition-based Learning (OBL). Further, Multi-Objective Optimization Ratio Analysis (MOORA) is utilized to identify the best S-box from the final optimized population. The performance of the proposed algorithm is evaluated by comparing the generated COBLAH S-box with more than twenty state-of-the-art S-boxes, including Advanced Encryption Standard (AES), Skipjack, Gray, and Affine Power Affine (APA). The COBLAH S-box has nonlinearity 112, Strict Avalanche Criterion (SAC) offset 0.0202, Distance to SAC (DSAC) 332, Differential Approximation Probability (DP) 0.0625, Linear Approximation Probability (LP) 0.0156, Bit Independence Criterion-Strict Avalanche Criterion (BIC-SAC) 0.50006, and Bit Independence Criterion-Nonlinearity (BIC-NL) 112, which stands as the optimal observed thus far. The absence of fixed and opposite fixed points and the fact that it adheres to a single cycle aligns the COBLAH S-box with an ideal S-box. In addition, an image encryption mechanism is utilized to encrypt and decrypt the different images sourced from the standard USC-SIPI image dataset using COBLAH S-box and compared against different state-of-the-art S-boxes based on various image characteristics.

This study contributes to the Health 4.0 paradigm by enhancing the precision of cell nuclei detection in histopathological images, a critical step in digital pathology. The presented approach is characterized by the combination of deep learning with traditional analytic classifiers.

Traditional methods in histopathology rely heavily on manual inspection by expert histopathologists. While deep learning has revolutionized this process by offering rapid and accurate detections, its black-box nature often results in a lack of interpretability. This can be a significant hindrance in clinical settings where understanding the rationale behind predictions is crucial for decision-making and quality assurance.

Our research addresses this gap by employing the YOLOv5 framework for initial nuclei detection, followed by an analysis phase where poorly performing cases are isolated and retrained to enhance model robustness. Furthermore, we introduce a logistic regression classifier that uses a combination of color and textural features to discriminate between satisfactorily and unsatisfactorily analyzed images. This dual approach not only improves detection accuracy but also provides insights into model performance variations, fostering a layer of interpretability absent in most deep learning applications.

By integrating these advanced analytical techniques, our work aligns with the Health 4.0 initiative’s goals of leveraging digital innovations to elevate healthcare quality. This study paves the way for more transparent, efficient, and reliable digital pathology practices, underscoring the potential of smart technologies in enhancing diagnostic processes within the Health 4.0 framework.

The port supply chain can provide strong data support for smart port decision-making. However, the traditional port supply chain suffers from poor flexibility of centralized authority management, data asymmetry, low efficiency of encrypted data retrieval and high delay of data update. Therefore, a secure storage and sharing scheme for port supply chain data based on blockchain assistance is proposed in this work. First, a fine-grained access control model for the port supply chain is designed in the smart contract to achieve auditable and decentralized management of user privileges. Second, the whole process of dynamic searchable encrypted data storage and access is realized in the smart contract, which ensures data security and encrypted data access efficiency. Moreover, an off-chain auxiliary storage method is designed to alleviate the storage pressure of the blockchain. Moreover, a reliable smart contract interface is provided for user operation, which can effectively regulate the access and storage of data. In addition, a consensus mechanism for the port supply chain is proposed, which takes the credibility and seniority of the operators at all levels as an indicator of the consensus agreement rights and interests. It improves the consensus efficiency under the premise of guaranteeing the security of the consensus process and meets the high data throughput demand of the supply chain system. Finally, the proposed scheme is implemented in Hyperledger Fabric. The security analysis and experimental results show that our scheme is secure and efficient in large-scale port supply chain data scenarios, and the system is scalable.

The primitive of vector commitment scheme allows a user to commit to an ordered sequence of messages (i.e., a vector) and later open the commitment at any position subset of the vector. The most important and desirable feature of vector commitment schemes is that the size of the opening proof is sublinear in the length of the committed vector. The original vector commitment scheme has now been extended to support several new functionalities like aggregation, updatability and homomorphism, and has applications ranging from verifiable data streaming to stateless cryptocurrency. Among these extensions, the linear-map vector commitment (LVC) scheme enables a user to open a general linear map evaluated on the committed vector, rather than those messages of the committed vector as in the original vector commitment scheme. However, the existing LVC schemes are only proved to be secure under the idealized assumptions, i.e., using the algebraic group model, which might be unpractical in the real world. To this end, we eliminate the use of algebraic group model, and propose a practically secure LVC construction. Our construction achieves practical security by additionally generating degree proofs for polynomials that enable a verifier to check the degree of polynomials publicly. We prove the security of the proposed LVC construction in the standard model under a $q$-type complexity assumption over bilinear groups. Moreover, we demonstrate how to use the proposed LVC scheme to construct maintainable vector commitments and verifiable data streaming protocols. The theoretical comparison and experimental results indicate that our proposal provides stronger security guarantee, while being competitive in terms of efficiency.

Camenisch–Lysyanskaya signature scheme with randomizability, namely CL signatures, at CRYPTO’04 has been well adopted for many privacy-preserving constructions, especially in the context of anonymous credential systems. Unfortunately, CL signatures suffer from linear size drawbacks. The signature size grows linearly based on the signing messages, which decreases the interest in practice, as each user may have multiple attributes (messages). Its standard EUF-CMA security was first proven under an interactive assumption. While the interactive assumption is not desirable in cryptography, Fuchsbauer et al. revisited its security at CRYPTO’18 by proving the scheme under the discrete logarithm (Dlog) assumption in the algebraic group model (AGM) that idealizes the adversary’s computation to be algebraic, yet the reduction loss is non-tight. In this work, we propose a new variant of CL signatures, namely CL+ signatures, that improves efficiency and security. The proposed CL+ signatures possess randomizability without the linear size drawback, such that signature size is a constant of three group elements. Besides, we prove the security of CL+ signatures can be tightly reduced to the DLog problem in AGM with only a loss factor of 3. Lastly, we show how CL+ signatures can also be instantiated to anonymous credential systems.

Private Set Intersection (PSI) technology is a cryptographic tool that allows the parties holding private data to determine the intersection of sets in a joint computation without revealing any additional privacy information. As a critical component of secure multi-party computation, this technology has been widely used in the security domain of artificial intelligence and data mining. With the emergence of the era of multi-source data sharing, protocols for private set intersection computation applicable to multiple participants have also emerged. However, the performance of existing multi-party private set intersection (MPSI) protocols is suboptimal when some participants use devices with limited communication or computational capabilities, such as mobile devices. To overcome the above issues, we design a cloud-aided multi-party private set intersection protocol (Cloud-Aided-MPSI) based on oblivious programmable pseudorandom functions (OPPRF) and oblivious key–value stores (OKVS). Due to the protocol efficiently outsourcing partial computational and communication tasks to cloud servers, our performance has been further enhanced compared to existing work. Through the Cloud-Aided-MPSI protocol, we propose a port scheduling protocol for coordinated management scenarios of ships arriving and departing from ports. The protocol effectively addresses the privacy protection concerns of port management when scheduling the arrival and departure of ships. We analyze the performance of this protocol.