Zero-shot Composed Image Retrieval (ZS-CIR) involves diverse tasks with varied visual manipulation intents across domains, scenes, objects, and attributes. A key challenge is that existing datasets contain limited intent-relevant annotations, making it hard for models to infer human intent from textual modifications. We introduce an intent-centric image–text dataset generated via reasoning by a Multimodal Large Language Model (MLLM) to better train ZS-CIR models for human manipulation intent understanding. Building on this dataset, we propose De-MINDS, a framework that distills the MLLM’s reasoning ability to capture manipulation intent and enhance models’ comprehension of modified text. A simple mapping network translates image information into language space and combines it with the manipulation text to form a query. De-MINDS then extracts intention-relevant information from this query and encodes it as pseudo-word tokens for accurate ZS-CIR. Across four ZS-CIR tasks, De-MINDS shows strong generalization and improves over existing methods by 2.15% to 4.05%, establishing new state-of-the-art results with comparable inference time.

Distributed Machine Learning (DML) enables parallel training but creates conflicts regarding model ownership determination, as verifying the specific contributions of individual clients during weight updates remains a challenge. To resolve ownership disputes and prevent malicious clients from forging data, verifying the integrity and effectiveness of each client's unique training history can fairly establish their partial ownership of the final DML model. In this paper, for the first time we propose a novel Matrix Commitment-based DML Ownership Verification (MAMMON) scheme that adopts a concise proof to ensure computational integrity and correctness within a limited computing cost. In contrast to computationally intensive SNARK-based approaches, MAMMON constructs a multi-linear tree structure to reduce proof update costs and avoids complex arithmetic circuit computations by standardizing the training process. Additionally, we watermark the weight proofs using client identity private keys to safeguard the commitments against tampering or unauthorized acquisition. Extensive experiments demonstrate MAMMON's superior performance in preserving computational integrity of DML training, covering both theoretical analysis and comparisons with other existing baselines.

Distributed Ledger Technology (DLT) has emerged as a promising solution for constructing decentralized and trustworthy platforms in recent years. Although blockchain, as a form of distributed ledger technology known for its tamper resistance and trustworthiness, seems to be an ideal solution for addressing trust issues, common blockchain systems face limitations due to the ever-growing volume of data. In this paper, we propose a dynamic storage scheme calledEfficiency-awareLow-storageScalability (ELS), which allocates variable data storage for the distributed ledgers. Our approach is designed to provide load balancing and security, allowing dynamic nodes to join or exit the network seamlessly. We utilize graph theory to group storage nodes in the blockchain by determining their network distances. These groups are then mapped into points in a two-dimensional virtual plane, meaning that block storage depends on the coordinates within the virtual plane. To balance storage performance with the evolving block allocation strategy, we develop a Voronoi diagram-based method to achieve near real-time adoptions that meet dynamic allocation requirements in blockchain systems. Experimental evaluations have demonstrated that our approach can reduce storage requirements by approximately 93% at each node, compared to full replication in 10000 block chains.

Privacy-preserving federated learning allows many clients to collaboratively train a machine learning model by sharing encrypted models. Among them, mask-based schemes have been widely applied due to their efficiency advantages. Unfortunately, as the invisibility of an individual local model, such schemes are vulnerable to poisoning attacks by Byzantine clients. Current work lacks a practical method to detect Byzantine clients within the mask-based scheme. We propose FBFL, a flexible Byzantine-robust federated learning scheme with privacy-preserving. While protecting the privacy of the client, we implement a defense against Byzantine clients without relying on an individual masked local model. Specifically, we design a secure distance computation method based on the Pedersen commitment. The exponential Manhattan distance we design is utilized to compute the malicious score of the client in order to distinguish the benign model from the abnormal model. Since the malicious score is obtained utilizing only the commitment value and not the masked local model, our Byzantine-robust method can be flexibly combined with other mask-based privacy-preserving methods. Security analysis shows that FBFL is Byzantine-robust and can ensure the data security of clients. Experimental evaluation shows that the robustness and efficiency of FBFL are great.

Recent booming development of Generative Artificial Intelligence (GenAI) has facilitated model commercialization to reinforce the model performance, including licensing or trading Deep Neural Network (DNN) models. However, DNN model trading may violate the benefit of the model owner due to unauthorized replications or misuse of the model. Model identity auditing is a challenging issue in protecting DNN model ownership, and verifying the integrity and ownership of models is one of the critical obstacles. In this paper, we focus on the above issue and propose anAccumulator-enabledAuditing forDecentralizedIdentity of DNNModel (A2-DIDM) that utilizes blockchain and zero-knowledge techniques to protect data and function privacy while ensuring the lightweight on-chain ownership verification. The proposed model presents a scheme of identity records via configuring model weight checkpoints with zero-knowledge proofs, which incorporates predicates to capture incremental state changes in model weight checkpoints. Our scheme ensures both computational integrity and programmability in DNN training process so that the uniqueness of the weight checkpoint sequence in a DNN model is preserved. A2-DIDM also addresses privacy protections in decentralized identity. We systematically analyze the security and robustness of our proposed model and further evaluate the effectiveness and usability of auditing DNN model identities. The code is available athttps://github.com/xtx123456/A2-DIDM.git.

Smart contracts play a pivotal role in blockchain ecosystems, and fuzzing remains a critical approach to securing them. However, existing smart contract fuzzers often optimize either seed generation or mutation scheduling in isolation and rely on narrow, fragmented feedback signals, leaving multi-transaction reasoning and stagnation recovery under-explored. In this work, we propose aLarge Language Models(LLMs)-based Multi-feedback Smart Contract Fuzzing framework (LLAMA). Key components of the proposed LLAMA include: (i) a hierarchical prompting strategy that guides LLMs to generate structurally valid, context-aware multi-transaction initial seeds, together with a lightweight pre-fuzzing phase that validates and prioritizes high-potential LLM-generated candidates; (ii) a multi-feedback-guided evolutionary optimization module that jointly optimizes seed selection and mutation scheduling by a group of constraints for driving an LLM-bootstrapped bandit scheduler. (iii) an LLM-guided hybrid fuzzing module that integrates evolutionary fuzzing with a dual-channel recovery mechanism, which concurrently employs asynchronous coverage-stagnation- based LLM reseeding and selective symbolic execution to resolve complex path constraints. Our extensive experiments demonstrate that LLAMA outperforms state-of-the-art fuzzers in both coverage and vulnerability detection. Specifically, it achieves 92% instruction coverage on small contracts and 81% on large contracts, while detecting 132 out of 148 known vulnerabilities across diverse categories. Ablation studies further evidence that the proposed multi-feedback and hybrid recovery strategies have strong impact on LLAMA’s performance. The results explain LLAMA’s effectiveness, adaptability, and practicality in complex smart contract scenarios.

Secure Aggregation (SA) is a fundamental privacy-preserving technique in Federated Learning (FL) that ensures the confidentiality of local model updates while enabling global model aggregation. Previous studies have implemented SA within the FL architecture that includes a central server. However, in a Device-to-Device (D2D) based FL, decentralized SA becomes challenging due to the lack of a central server, particularly in a zero-trust network vulnerable to Byzantine attacks. To address this issue, we present a novel Byzantine-robust decentralized SA protocol (DeSA) that guarantees the integrity of model training and aggregation while protecting the privacy of model updates. Specifically, we utilize an enhanced zk-SNARK proof system to verify the local model training process. Additionally, we propose a framework that embeds multiple zero-knowledge proofs to ensure the integrity of model aggregation, while maintaining succinct proofs and fast verification. Moreover, we present a Byzantine-robust D2D aggregation protocol that can withstand malicious nodes trying to disrupt model aggregation. To protect privacy, we develop a one-time masking method that eliminates aggregated masks through a dynamic aggregation strategy. This strategy takes into account the adjacency and trust relationships among nodes in evolving network topologies. Finally, we perform a theoretical analysis and evaluate DeSA on real-world datasets. Experimental results show that the time required to verify an embedded proof is significantly reduced compared to the time of verifying multiple proofs. Additionally, its accuracy remains robust against malicious nodes.

This editorial introduces the first part of the Special Issue on “Advanced Technologies in the Decentralized Web.” As the Internet evolves toward more user-centric and resilient architectures, concepts like Web3 and Web 3.0 have gained significant prominence. This issue explores key advancements including decentralized machine learning, AI-blockchain integration, identity management, and data sovereignty. We provide an overview of the selected papers, highlighting their contributions to creating a more transparent and secure decentralized digital future.

Multi-modal Federated Learning (MFL) is a distributed machine learning paradigm that enables multiple participants with multi-modal data to collaboratively train a global model for multi-modal tasks without sharing their local data. MFL typically deploys the trained global model as an Embedding-as-a-Service (EaaS), allowing participants to obtain embeddings for downstream tasks. However, it increases the risk of unauthorized copying and leakage of the model. Protecting the ownership of the MFL model while maintaining model performance is challenging. In this paper, we propose the first general model ownership protection framework for MFL, named MFL-Owner. MFL-Owner decouples the watermarking process from the model training process and addresses both ownership verification and traceability, effectively safeguarding the interests of the MFL collective. MFL-Owner leverages the concept of orthogonal transformations by incorporating a linear transformation matrix with orthogonal constraints into the model, achieving high-quality ownership verification and traceability with minimal impact on model performance. To enhance the practicality of the watermark and prevent conflicts among multiple clients during tracing, we propose a trigger dataset selection method based on out-of-distribution data combined with Gaussian noise perturbation. Our experiments on multiple datasets demonstrate that MFL-Owner is effective for model ownership verification and traceability for MFL.

Zero-Shot Composed Image Retrieval (ZS-CIR) involves diverse tasks with a broad range of visual content manipulation intent across domain, scene, object, and attribute. The key challenge for ZS-CIR tasks is to modify a reference image according to manipulation text to accurately retrieve a target image, especially when the reference image is missing essential target content. In this paper, we propose a novel prediction-based mapping network, named PrediCIR, to adaptively predict the missing target visual content in reference images in the latent space before mapping for accurate ZS-CIR. Specifically, a world view generation module first constructs a source view by omitting certain visual content of a target view, coupled with an action that includes the manipulation intent derived from existing image-caption pairs. Then, a target content prediction module trains a world model as a predictor to adaptively predict the missing visual information guided by user intention in manipulating text at the latent space. The two modules map an image with the predicted relevant information to a pseudo-word token without extra supervision. Our model shows strong generalization ability on six ZS-CIR tasks. It obtains consistent and significant performance boosts ranging from 1.73% to 4.45% over the best methods and achieves new state-of-the-art results on ZS-CIR. Our code is available at https://github.com/Pter61/predicir.

The emergence of blockchain-based data markets has revolutionized traditional centralized data trading paradigms. By leveraging immutable ledgers, transparent transactions, and peer-to-peer exchanges that eliminate intermediary fees, these markets progressively enhance transactional trust. Nevertheless, establishing efficient consensus on data value remains challenging for conventional approaches, particularly regarding high-value assets. While auctions offer viable solutions, they typically face dual dilemmas with such homogeneous and infinitely replicable goods as data, that is, ineffective trading mechanisms and inadequate evaluation methodologies. This motivates our work. In this paper, we propose a novel evaluation metric-Auction Utilitarian Welfare with Externalities (AUWE)-specifically designed for data markets. AUWE optimizes collective welfare by incorporating incentive compatibility and theoretically validating tripartite benefits (data owners, users, and externalities). To maximize AUWE, we also develop a Seller-directed Descending Price (SD-DP) auction mechanism tailored to blockchain's information-symmetric environments. This mechanism can achieve optimal resource allocation, minimize price volatility, and approximate incentive compatibility. Furthermore, we establish theoretical strategies for buyers and sellers to stabilize data prices. Through simulations, we compare SD-DP with uniform-price and discriminatory-price auctions in traceable blockchain networks, and analyze bidder strategies under perfect information using our AUWE metrics. The results demonstrate SD-DP Auction's superiority in welfare outcomes and price stability, especially for high-value data transactions.

Latent diffusion models have exhibited considerable potential in generative tasks. Watermarking is considered to be an alternative to safeguard the copyright of generative models and prevent their misuse. However, in the context of model distribution scenarios, the accessibility of models to large scale of model users brings new challenges to the security, efficiency and robustness of existing watermark solutions. To address these issues, we propose a secure and efficient watermarking solution. A new security mechanism is designed to prevent watermark leakage and watermark escape, which considers watermark randomness and watermark-model association as two constraints for mandatory watermark injection. To reduce the time cost of training the security module, watermark injection and the security mechanism are decoupled, ensuring that fine-tuning VAE only accomplishes the security mechanism without the burden of learning watermark patterns. A watermark distribution-based verification strategy is proposed to enhance the robustness against diverse attacks in the model distribution scenarios. Experimental results prove that our watermarking consistently outperforms existing six baselines on effectiveness and robustness against ten image processing attacks and adversarial attacks, while enhancing security in the distribution scenarios. The code is available at https://anonymous.4open.science/r/DistriMark-F11F/.

Blockchain-based decentralized identity (DID) typically employs identity aggregation techniques to support efficient and trustworthy identity authentication in order to meet the requirements of the high volume of service requests in Internet of Things (IoT). Due to the lack of effective mechanisms for heterogeneous DID (H-DID) aggregation, a complete aggregated identity authentication often requires multiple rounds of signature verification for different identity attributes. However, this setting brings trust and privacy issues, and one notable threat is the potential disclosure of secret identity information through the linkage of heterogeneous identity attributes when enormous IoT devices/accesses are involved. In this article, we focus on trustworthy authentication of DID and propose a novel anonymous verifiable credential-based aggregation for h-DID (AVCA-hDID). Our AVCA-hDID model supports anonymous ownership verification of DIDs through label randomization, thereby effectively safeguarding identity privacy in IoT. AVCA-hDID involves identifier aggregation and attribute aggregation for H-DIDs, ensuring both authentication efficiency and balancing trustworthiness and adoptability. We analyze the security and unlinkablility of our proposed model and further experiment evaluation demonstrates the efficiency and effectiveness of AVCA-hDID.

Sandwich arbitrage is a popular strategy for blockchain-based decentralized exchange (DEX) users to facilitate decentralized token exchanges and make profits. However, conventional Sandwich arbitrage only focuses on one big-deal transaction and executes arbitrary actions within an intrablock, leading to a huge loss of profit opportunities. In this article, we present Lasagne, a novel high-profit arbitrage strategy that allows the exploration of profit opportunities from multiple small-deal transactions across interblocks. Specifically, Lasagne involves a suite of novel strategies: fishing, bundle, and grid, each of which is tailored to a liquidity pool with diverse transaction frequencies and skewness. The experimental results demonstrate the superiority of Lasagne over a state-of-the-art sandwich in terms of feasibility and effectiveness.

Ensuring scalability in cryptocurrency systems is significant in guaranteeing real-world utility along with the remarkable increment of cryptographic currency. As an alternative in solving scalability issue, payment channel allows users to deliver extensive offline transactions without uploading massive transaction details to the blockchain, such that increasing efficiency can be achieved. However, the implementation of payment channel still encounters privacy concerns when considering the publicly available transaction amounts and the potentials in mining associations between transaction parties. In this paper, we propose a novel payment channel scheme, entitledCommitment-basedAnonymousPayment ChannEl (CAPE), to facilitate unlimited off-chain bidirectional payments while guaranteeing participants’ privacy. The proposed scheme adopts zero-knowledge proof (zk-SNARKs) and verifiable timed (VTD) commitments to ensure the anonymity of the relationship between on-chain and off-chain transactions, privacy of transaction amounts, and security of balances. We comprehensively formalize security definitions and present rigorous proofs for each security attribute. Experiment results further demonstrate the practical viability of CAPE.

Multi-Party Computation (MPC), as a distributed computing paradigm, is considered to be a potential solution for providing privacy-preserving for applications following the client-server model. However, traditional MPC solutions cannot satisfy the publicly verifiable requirement of the client-server model. In this paper, we propose a blockchain-based verifiable MPC solution using Pedersen's threshold secret sharing and Lifted ElGamal encryption. We first build a data distribution method using Pedersen's threshold secret sharing and symmetric encryption to protect the privacy of inputs while ensuring robustness. Then, we propose a result processing algorithm using Lifted ElGamal encryption to safeguard the privacy of the outputs. Finally, we employ non-interactive zero-knowledge proof and Pedersen commitment to publicly verify the correctness of the encrypted outputs in the smart contract, enabling the detection of malicious parties. Theoretical analysis indicates that the proposed method can publicly verify the correctness of outputs without revealing plain-text inputs and outputs, which satisfy the privacy-preserving requirements of the client-server model. Experimental evaluations have demonstrated that our proposed approach is efficient regarding computation overhead, communication overhead, and response time in the output verification phase while achieving stronger privacy and robustness.

Although consortium blockchain has an identification mechanism, the captured internal clients are potentially threatening internal blockchain nodes. Internal Distributed Denial-of-Service (DDoS) attacks threaten the specific nodes in consortium blockchain, e.g., the executor, consensus, and committer nodes. Typical attack methods may include SYN Flooding and ACK Flooding and deny normal transaction service by sending many invalid transactions and blocks. In this work, we have proposed an organization collaboration-based DDoS defense approach and a Deep Q-learning (DQN)-based Moving Target Defense (MTD) for changing attack surface of victims in consortium blockchain. On one hand, contracts are used to synchronize attack information obtained from organizations, e.g., bots’ IP addresses and public keys. On the other hand, we have developed a DQN-based MTD defense mechanism for organizations to change the attack surface of victims in order to mitigate the malicious traffic, in the case of missing detections of bots. Our approach applies a multi-stage game to reflect interactions between attackers and defenders. The evaluation results have demonstrated that our approach could effectively mitigate DDoS attacks in consortium blockchain.

With the development of information and communication technology, the Internet of Things (IoT) has gained significant popularity across various applications. As the number of devices surges and data generation accelerates, robust data security and accurate data retrieval have become increasingly important. Fuzzy keyword search provides an elegant way to allow retrieval over encrypted data. Privacy and accuracy are two important factors when applying fuzzy keyword search into cloud storage systems. State-of-the-art mechanisms cannot effectively balance privacy and accuracy, and they even compromise the access pattern. In this paper, we propose SeaPA, a fuzzy keyword search scheme with strong privacy and high accuracy. SeaPA is built on top of two non-colluding servers and integrated with secure multi-party computation. To construct SeaPA, we introduce a top-k document retrieval algorithm that conceals the similarity of search results from identical queries, preventing access pattern leakage. We implement a prototype system and conduct extensive experiments on the 20NewsGroups dataset. The results show that SeaPA achieves higher accuracy, with over$16\times$speedups in index encryption efficiency and over$9.3\times$speedups in search efficiency, compared to previous schemes.

Data integrity auditing technology serves as an essential tool to ensure the data's integrity with the popularity of remote storage. However, existing data integrity auditing models are unsuitable for a large number of files with interrelationships and heavily depend on a centralized Third-Party Auditor (TPA). To address these issues, in this paper we propose a blockchain-assisted searchable integrity auditing scheme for large-scale similarity data. To broaden the scope of the auditing model and enhance its ability to handle interconnected files, we utilize the keyword to design a search index and a trapdoor to achieve authenticator searchability for the interconnected files. The integrity of the searching result from the cloud side can be guaranteed at the same time. To reduce reliance on centralized TPA and enhance the credibility and transparency of auditing, we integrate blockchain technology along with smart contracts to replace TPA and achieve multitask auditing. We adopt a certificateless cryptosystem to generate the authenticator, while considering the cost reduction. Moreover, an arbitrator is proposed to achieve fairness judge. Theoretical and security analysis demonstrate that the proposed scheme is efficient and secure, making it a promising solution for data auditing in a wide range of applications.

As decentralized storage becomes more prevalent, ensuring data integrity within these systems has gained increasing importance. However, the introduction of a centralized and trustedCloud Combiner(CO) to manage auditing across distributedCloud Servers(CSs) presents several challenges, which not only increases communication costs with independent CSs but also diminishes the credibility of the auditing results. Additionally, traditional approaches often require either certificate and key management or the establishment of secure transmission channels, which incur substantial overhead. Furthermore, the computational cost for index generation to support searchable auditing is often high. To address these challenges, we propose a collaborative and searchable integrity auditing scheme for multi-replica data that eliminates the need for a CO. Specifically, we design a certificate-based authenticator for multi-copy data blocks, which simplifies certificate and key management while removing the need for secure channels. To enable efficient searchable auditing, we introduce a novel index tag that matches authenticators, thereby reducing the computational overhead. Moreover, we leverage blockchain technology to enhance the credibility of the auditing process and ensure the integrity of the search results. Both security and performance analyses demonstrate that the proposed scheme is secure and efficient.

Federated Learning (FL) realizes distributed machine learning training via sharing model updates rather than raw data, thus ensuring data privacy. However, an attacker may infer the client's local original data from the model parameter so that original data leakage can be caused. While Differential Privacy (DP) is designed to address data leakage issues in FL, injecting noises during training reduces model accuracy. To minimize the negative impact caused by noises on model accuracy while considering privacy protections, in this article we propose an adaptive FL model, entitledRDP-basedAdaptiveFederatedLearning inShuffle model (RAFLS). To ensure the dataset privacy of clients, we inject adaptive noises into the client's local model by leveraging the adaptive layer-wise adaptive sensitivity of the local model. Our approach shuffles all local model parameters in order to address privacy explosion concerns caused by high-dimensional aggregation and multiple iterations. We further propose a fine-grained model weight aggregation scheme to aggregate all local models and obtain a global model. Our experiment evaluations demonstrate the proposed RAFLS has a better performance than the state-of-the-art methods in reducing noise's impact on model accuracy while protecting data, i.e., showing that the accuracy of RAFLS increases by 1.54% than that of the baseline scheme when$\epsilon = 2.0$and FashionMNIST under IID setting.

Vertical Federated Learning(VFL) has gained widespread attention due to its ability of enabling collaborative model training among participants with diverse data features.Differential Privacy(DP) offers provable privacy guarantees for VFL, but existing DP-based methods typically compromise accuracy for privacy protection. To address this issue, we propose a novel scheme, called Adaptive Differential Privacy-based Vertical Federated Learning (Ada-VFed), that enhances privacy of data features and labels by adding Gaussian noise separately to the transmitted intermediate results and gradients. To improve model accuracy, we incorporate adaptive constraints through regularization terms in the objective function to mitigate the impact of clipping operations. In addition, we propose a dynamic noise injection mechanism that adjusts noise according to the importance of each dimension, thereby balancing privacy protection and model accuracy. Our theoretical analysis provides privacy guarantees and convergence insights. Extensive experiments demonstrated that our scheme significantly outperforms state-of-the-art DP-based VFL methods in terms of accuracy. Even with a small privacy budget (e.g.,ϵ = 0.5), our method improves the accuracy on MNIST, FashionMNIST, and CIFAR-10 by 13.01%, 10.08%, and 3.40%, respectively, compared to traditional DP-based VFL methods.

Federated Learning (FL) is an alternative approach that facilitates training machine learning models on distributed users’ data while preserving privacy. However, clients have different local model structures and most local data are non-independent and identically distributed, so that FL encounters heterogeneity and catastrophic forgetting issues when clients continuously accumulate new knowledge. In this work, we propose a scheme called MUFTI (Multi-Domain Distillation-based Heterogeneous Federated ConTInuous Learning). On one hand, we have extended domain adaptation to FL via extracting features to obtain feature representations on unlabeled public datasets for collaborative training, narrowing the distance between feature outputs of different models under the same sample. On the other hand, we propose a combining knowledge distillation method to solve the catastrophic forgetting issue. Within a single task, dual-domain distillation is used to avoid data forgetting between different domains; for cross task learning in task flow, the logits output of the previous model is used as the teacher to avoid forgetting old tasks. The experiment results showed that MUFTI had a better performance in accuracy and robustness comparing to state-of-the-art methods. The evaluation also demonstrated that MUFTI could perform well in handling task increment issues, reducing catastrophic forgetting, and achieving trade-offs between multiple objectives.

As a collaborative framework designed to safeguard privacy,Federated Learning(FL) seeks to protect participants’ data throughout the training process. However, the framework still faces security risks from poisoning attacks, arising from the unmonitored process of client-side model updates. Most existing solutions address scenarios where less than half of clients are malicious, i.e., which leaves a significant challenge to defend against attacks when more than half of participants are malicious. In this paper, we propose a FL scheme, named FedAMM, that resists backdoor attacks across various data distributions and malicious client ratios. We develop a novel backdoor defense mechanism to filter out malicious models, aiming to reduce the performance degradation of the model. The proposed scheme addresses the challenge of distance measurement in high-dimensional spaces by applyingPrincipal Component Analysis(PCA) to improve clustering effectiveness. We borrow the idea of critical parameter analysis to enhance discriminative ability in non-iid data scenarios, via assessing the benign or malicious nature of models by comparing the similarity of critical parameters across different models. Finally, our scheme employs a hierarchical noise perturbation to improve the backdoor mitigation rate, effectively eliminating the backdoor and reducing the adverse effects of noise on task accuracy. Through evaluations conducted on multiple datasets, we demonstrate that the proposed scheme achieves superior backdoor defense across diverse client data distributions and different ratios of malicious participants. With 80% malicious clients, FedAMM achieves low backdoor attack success rates of 1.14%, 0.28%, and 5.53% on MNIST, FMNIST, and CIFAR-10, respectively, demonstrating enhanced robustness of FL against backdoor attacks.

Provable Data Possession (PDP) is an alternative technique that guarantees the integrity of remote data. However, most current PDP schemes are inapplicable to similarity-like data checking with the same attribute, i.e., when there are numerous similar files to be checked by Data Owners (DOs). Some traditional models cannot resist the corrupt auditors who always generate biased challenge information. Besides, a copy-summation attack exists in some schemes, which means the Cloud Server (CS) can bypass the verification by storing the median value instead of initial data via summation operation. To address the issues above, in this work, we propose a keyword searchable PDP scheme for large similar data checking. To achieve searchability, we introduce the notion of a keyword in PDP and design a specific index structure to match the authenticator. The scheme enables all matched files to be auditable and verifiable, while guaranteeing privacy protections. Unlike existing methods, our Third Party Auditor (TPA) checks all similar data containing the same keyword simultaneously. We utilize unpredictable yet verifiable public information on the blockchain to generate challenge information, rather than relying on a centralized TPA. The proposed scheme can resist copy-summation attacks. Theoretical analysis demonstrates that the proposed scheme satisfies the security requirements, and our evaluations demonstrate its efficiency.

Vertical federated learning (VFL) provides a privacy-preserving method for machine learning, enabling collaborative training across multiple institutions with vertically distributed data. Existing VFL methods assume that participants passively gain local models of the same structure and communicate with active pary during each training batch. However, due to the heterogeneity of participating institutions, VFL with heterogeneous models for efficient communication is indispensable in real-life scenarios. To address this challenge, we propose a new VFL method called Practical Heterogeneous Vertical Federated Learning via Representation Learning (PraVFed) to support the training of parties with heterogeneous local models and reduce communication costs. Specifically, PraVFed employs weighted aggregation of local embedding values from the passive party to mitigate the influence of heterogeneous local model information on the global model. Furthermore, to safeguard the passive party’s local sample features, we utilize blinding factors to protect its local embedding values. To reduce communication costs, the passive party performs multiple rounds of local pre-model training while preserving label privacy. We conducted a comprehensive theoretical analysis and extensive experimentation to demonstrate that PraVFed reduces communication overhead under heterogeneous models and outperforms other approaches. For example, when the target accuracy is set at 60% under the CINIC10 dataset, the communication cost of PraVFed is reduced by 70.57% compared to the baseline method. Our code is available athttps://github.com/wangshuo105/PraVFed_main.

Secure Aggregation (SA), in the Federated Learning (FL) setting, enables distributed clients to collaboratively learn a shared global model while keeping their raw data and local gradients private. However, when SA is implemented in edge-intelligence-driven FL, the open and heterogeneous environments will hinder model aggregation, slow down model convergence speed, and decrease model generalization ability. To address these issues, we present a Robust and adaptive Secure Aggregation (RaSA) protocol to guarantee robustness and privacy in the presence of non-IID data, heterogeneous system, and malicious edge servers. Specifically, we first design an adaptive weights updating strategy to address the non-IID data issue by considering the impact of both gradient similarity and gradient diversity on the model aggregation. Meanwhile, we enhance privacy protection by preventing privacy leakage from both gradients and aggregation weights. Different from previous work, we address system heterogeneity in the case of malicious attacks, and the malicious behavior from edge servers can be detected by the proposed verifiable approach. Moreover, we eliminate the influence of straggling communication links and dropouts on the model convergence by combining efficient product-coded computing with repetition-based secret sharing. Finally, we perform a theoretical analysis that proves the security of RaSA. Extensive experimental results show that RaSA can ensure model convergence without affecting the generalization ability under non-IID scenarios. Moreover, the decoding efficiency of RaSA achieves 1.33× and 6.4× faster than the state-of-the-art product-coded and one-dimensional coded computing schemes.

Vertical Federated Learning (VFL) allows collaborative machine learning without sharing local data. However, existing VFL methods face challenges when dealing with heterogeneous local models among participants, which affects optimization convergence and generalization of participants' local knowledge aggregation. To address this challenge, this paper proposes a novel approach calledEmbeddingAggregation-based HeterogeneousModelsTraining in Vertical Federated Learning(EASTER). EASTER focuses on aggregating the local embeddings of each participant's knowledge during forward propagation. We propose an embedding protection method based on lightweight blinding factors, which injects the blinding factors into the local embedding of the passive party. However, the passive party does not own the sample labels, so the local model's gradient cannot be calculated locally. To overcome this limitation, we propose a new method in which the active party assists the passive party in computing its local heterogeneous model gradients. Theoretical analysis and extensive experiments demonstrate that EASTER can simultaneously train multiple heterogeneous models and outperform some recent methods in model performance. For example, compared with the state-of-the-art method, the model accuracy of EASTER was improved by 7.22% under the CIFAR-10 dataset.

Split Vertical Federated Learning (Split VFL) is an increasingly popular framework for collaborative machine learning on vertically partitioned data. However, it is vulnerable to various attacks, resulting in privacy leakage and robust aggregation issues. Recent works have explored the privacy protection of raw data samples and labels, neglecting malicious attacks launched by dishonest passive parties. Since they may deviate from the protocol and launch embedding poisoning attacks and free-riding attacks, it will inevitably result in model performance loss. To address this issue, we propose a Robust Privacy-preserving forward Aggregation (RoPA) protocol, which can resist embedding poisoning attacks and free-riding attacks and protect the privacy of embedding vectors. Specifically, we first present a modified Secret-shared Non-Interactive Proofs (SNIP) algorithm to guarantee the integrity verification of embedding vectors. To prevent free-riding attacks, we also give a validity verification protocol using matrix commitment. In particular, we utilize probability checking and batch verification to improve the verification efficiency of the protocol. Moreover, we adopt arithmetic secret sharing to protect data privacy. Finally, we conduct rigorous theoretical analysis to prove the security of RoPA and evaluate the performance of RoPA. The experimental results show that the proof verification overhead of RoPA is approximately 8× lower than the original SNIP, and the model accuracy is improved by ranging from 3% to 15% under the above two malicious attacks.

Different from the Composed Image Retrieval task that requires expensive labels for training task-specific models, Zero-Shot Composed Image Retrieval (ZS-CIR) involves diverse tasks with a broad range of visual content manipulation intent that could be related to domain, scene, object, and attribute. The key challenge for ZS-CIR tasks is to learn a more accurate image representation that has adaptive attention to the reference image for various manipulation descriptions. In this paper, we propose a novel context-dependent mapping network, named Context-I2W, for adaptively converting description-relevant Image information into a pseudo-word token composed of the description for accurate ZS-CIR. Specifically, an Intent View Selector first dynamically learns a rotation rule to map the identical image to a task-specific manipulation view. Then a Visual Target Extractor further captures local information covering the main targets in ZS-CIR tasks under the guidance of multiple learnable queries. The two complementary modules work together to map an image to a context-dependent pseudo-word token without extra supervision. Our model shows strong generalization ability on four ZS-CIR tasks, including domain conversion, object composition, object manipulation, and attribute manipulation. It obtains consistent and significant performance boosts ranging from 1.88% to 3.60% over the best methods and achieves new state-of-the-art results on ZS-CIR. Our code is available at https://anonymous.4open.science/r/Context-I2W-4224/.

Along with the development of the biological industry, the system of biological assets governance has a higher-level requirement in asset identity verification due to the demands of digitization and Financial Technology (FinTech). In order to achieve broad scalability and adaptability in the governance of biological assets' identities, this work proposes a Biological Assets Decentralized Identity Management (BA-DID) system that develops a Decentralized Identity (DID)-based solution to addressing the verification issues in biological assets while considering multi-dimensional requirements. Blockchain technology is the fundamental infrastructure of the system. Authenticated certificates of the asset owners verify identity attributes. Financial Service Institutions (FSI) and governance agencies act as issuers, providing Verifiable Credentials (VC) for biological assets by using a group of attributes over a consensus. The asset owners hold VCs and can be available to other organizations as proof of the corresponding asset attributes. Our evaluations have demonstrated that the proposed approach has superior performance in biological assets verification, security, and maintenance.

Federated learning realizes distributed machine learning training by sharing the model rather than sharing the local dataset. However, the local dataset may be leaked during model training. While differential privacy techniques can mitigate privacy leakage to some extent, the noise tends to have a significant negative impact on model accuracy. To minimize the impact of noise on model accuracy and protect the privacy of the original data, we propose an Layer-wise Relevance Propagation-based Adaptive Federated Learning (LRPAFL). To ensure local data privacy, we inject adaptive noises that satisfy DP into the training sample according to the correlation between local training data features and the model. Specifically, we set a correlation boundary ct. We only inject an adaptive amount of noise when the correlation between the feature and the model is greater than or equal to ct. Furthermore, to evaluate the performance of our approach, we propose a relationship between privacy budget and accuracy. We theoretically and experimentally analyze the performance of this model. Compared with the baseline method, our method has a better performance and the proposed model reduces the impact of noise on model accuracy while protecting data. For example, compared with the state-of-the-art scheme, the accuracy of RASFL is increased by 2% when$\epsilon=1$

The widespread developments of blockchain bring about diverse blockchain networks, where each stands as an isolated data island and operates independently. The need of interoperability and interconnection among kinds of blockchains inspires the emergence of the cross-chain technology. It enables the asset transfer and information interaction via crosschain transactions. The previous cross-chain transaction systems are almost implemented over the plain data, fully exposing the transaction-associated information. Cryptographic techniques such as non-interactive zero-knowledge proof can be used to protect the transaction privacy, while its high complexity incurs heavy running cost. The privacy of cross-chain transactions is still a challenging issue. In this paper, we propose a lightweight crosschain transaction privacy-preserving method named PTCross. It embeds Bulletproofs and Pedersen commitment to hide crosschain transaction information, meanwhile combining off-chain computation and on-chain contract verification. We instantiate and implement PTCross between ChainMaker and Bitcoin. The experimental results show that the proposed PTCross achieves both lightweight performance and strong privacy.

Current text-video retrieval methods mainly rely on cross-modal matching between queries and videos to calculate their similarity scores, which are then sorted to obtain retrieval results. This method considers the matching between each candidate video and the query, but it incurs a significant time cost and will increase notably with the increase of candidates. Generative models are common in natural language processing and computer vision, and have been successfully applied in document retrieval, but their application in multimodal retrieval remains unexplored. To enhance retrieval efficiency, in this paper, we introduce a model-based video indexer named T2VIndexer, which is a sequence-to-sequence generative model directly generating video identifiers and retrieving candidate videos with constant time complexity. T2VIndexer aims to reduce retrieval time while maintaining high accuracy. To achieve this goal, we propose video identifier encoding and query-identifier augmentation approaches to represent videos as short sequences while preserving their semantic information. Our method consistently enhances the retrieval efficiency of current state-of-the-art models on four standard datasets. It enables baselines with only 30%-50% of the original retrieval time to achieve better retrieval performance on MSR-VTT (+1.0%), MSVD (+1.8%), ActivityNet (+1.5%), and DiDeMo (+0.2%). The code is available at https://github.com/Lilidamowang/T2VIndexer-generativeSearch.

Recent work shows that documents from encyclopedias serve as helpful auxiliary information for zero-shot learning. Existing methods align the entire semantics of a document with corresponding images to transfer knowledge. However, they disregard that semantic information is not equivalent between them, resulting in a suboptimal alignment. In this work, we propose a novel network to extract multi-view semantic concepts from documents and images and align the matching rather than entire concepts. Specifically, we propose a semantic decomposition module to generate multi-view semantic embeddings from visual and textual sides, providing the basic concepts for partial alignment. To alleviate the issue of information redundancy among embeddings, we propose the local-to-semantic variance loss to capture distinct local details and multiple semantic diversity loss to enforce orthogonality among embeddings. Subsequently, two losses are introduced to partially align visual-semantic embedding pairs according to their semantic relevance at the view and word-to-patch levels. Consequently, we consistently outperform state-of-the-art methods under two document sources in three standard benchmarks for document-based zero-shot learning. Qualitatively, we show that our model learns the interpretable partial association. Code is available at https://github.com/MorningStarOvO/EmDepart.

Cryptojacking is a new type of Internet of Things (IoT) attack, where an attacker hijacks the computing power of IoT devices, such as wireless routers, smart TVs, set-top boxes, or cameras, to mine cryptocurrencies, e.g., PyRoMineIoT. The attackers launch selfish mining-like (SM-like) attacks to obtain lucrative mining rewards with the stolen computing power, once the power exceeds a threshold. Generally, a single deep learning (DL) model with a single feature (e.g., fork height) is trained to detect SM-like attacks. However, the existing model fails to detect every SM-like attack since the model training ignores other distinctive features (e.g., mining rewards and blocking rate) of SM-like attacks. In this article, SM-NEEDLE, an ensemble DL (NEEDLE) method is proposed to detect SM-like attacks. More specifically, the distinctive features are extracted from the blockchain system, where SM-like simulators emulate the strategies of SM-like attacks. Further, to circumvent the local optima problem caused by the single DL model (e.g., Back-Propagation Neural Network, BPNN), the SM-NEEDLE trains multiple BPNNs with these distinctive features. Evaluation results indicate the accuracy and false negative rate (FNR) of SM-NEEDLE for detecting SM-like attacks (including SM1 and its variants) are 98.9% and 1.48%, respectively. That is, 98.9% of SM-like attacks are correctly identified and only 1.48% of attacks are undetectable.

Data deduplication and integrity auditing based blockchain plays an important role in guaranteeing secure and efficient cloud storage services. However, existing data deduplication schemes support auditing either with the assistance of a trust center (key server or third-party auditor) or bear the waste of computation and storage resources caused by repetitive authenticators storage and key storage. In this paper, we propose a blockchain-based shared data integrity auditing and deduplication scheme. Specifically, we propose a deduplication protocol based on ID-based broadcast encryption without key servers and achieve key deduplication on the user side. Next, we propose a data integrity auditing protocol by using the characteristic of convergent encryption to achieve authenticator deduplication on the cloud service provider side. Besides, we achieve decentralized data integrity auditing based blockchain without relying on a single trusted third-party auditor and improve the credibility of the auditing result. On this basis, we propose two bath auditing protocols for different scenarios to improve efficiency. Security and performance analysis demonstrates that the authenticators' storage cost on the cloud storage provider side can be reduced from${\mathcal {O}}({\mathcal {F}})$to${\mathcal {O}}(1)$and the key storage cost on the user side can be reduced from${\mathcal {O}}({\mathcal {F}})$to${\mathcal {O}}(1)$as well.

In the context of consortium blockchain, consensus protocols set permission mechanisms to maintain a relatively fixed group of participants. They can easily use distributed consistent algorithms for achieving deterministic and efficient consensus and generate incessant blocks as the ledger. However, most of the existing consensus protocols do not sufficiently leverage the chain structure of blocks, and therefore leaving room for performance improvement. In this paper, we first propose a Hashchain-based Consensus Acceleration (HCA) protocol. The HCA protocol enables a leader to generate blocks that contain a quorum of votes on the previous block, and allow voters to re-vote for accelerating the block generation to Byzantine Fault Tolerance (BFT) consensus protocols. Then, we present a rolling-based leader selection (RLS) scheme to further optimize the HCA protocol. In the RLS scheme, the leader is changed in a round-robin fashion. Finally, theoretical analysis proves the safety, liveness and responsiveness of the optimized HCA protocol, while experimental evaluation shows that the optimized HCA protocol outperforms the existing BFT consensus protocols, from the viewpoint of efficiency.

The Payment Channel Network (PCN) has emerged as an extensively adopted solution to address the scalability issues of Bitcoin by efficient off-chain updates. However, conflicts arise while existing update protocols are pursuing multiple goals of security, privacy, and expressiveness. In this work, we propose a new off-chain update protocol, Secure and Anonymous Multi-Channel Updates (SAMCU), which is developed on the basis of Unspent Transaction Output (UTXO). SAMCU aims at achieving goals of internal anonymity, balance security, and multi-channel updates simultaneously, which has not been done before. To achieve these goals, we exploit the technique of updating graph splitting (UGS) to make participants aware of only the identities of their neighboring sub-graphs, thereby ensuring internal anonymity in multi-channel updates. Then, to avoid security issues arising from equal sub-graphs, we further propose an Enable Payment Transaction Tree (EPTT) to guarantee balance security for each honest protocol participant. Moreover, we optimize the performance of our solution, reducing transaction fees by splitting transactions and the number of communication connections by hierarchical communication. To evaluate the performance of the SAMCU, we implement a prototype involving up to 100 updating payment channels. Experimental results demonstrate that SAMCU outperforms the state-of-the-art, resulting in approximately 70% savings in communication connections and a 66% reduction in on-chain transaction fees when the number of updating payment channels is 100.

Blockchain is a revolutionary technology for constructing trustworthy communications for 6G multiaccess edge computing (6G-MEC). Designing a blockchain-based access control system for 6G-MEC is challenging due to the highly heterogeneous edge devices (EDs) in 6G-MEC. However, traditional blockchain-based access control methods cannot satisfy the heterogeneous device scenarios and are unable to assign voting weights based on the performance of EDs. In this article, we propose a trustworthy access control method for 6G-MEC networks and demonstrate how blockchain can be utilized in our proposed blockchain-assisted multiaccess control approach. Our approach develops an attribute validation and a validation weight method to offer strengthened access control in a decentralized context. We have proposed a trustworthy access control for multiaccess edge computing by using a multitier blockchain architecture and a reinforcement-learning-based weight determination algorithm for ED to eliminate the influence on blockchain consensus behavior. Experimental results demonstrate the effectiveness of our proposed approach.

The metaverse, as an emerging technical term, conceptually aims to construct a virtual digital space that runs parallel to the physical world. Due to human behaviors and interactions being represented in the virtual world, security in the metaverse is a challenging issue in which the traditional centralized service model is one of the threat sources. To conquer the obstacle caused by centralized computing, blockchain-based solutions are potential problem-solving methods. However, it is difficult for a single blockchain to support large-scale data and business services in the metaverse, due to the scalability restrictions. Moreover, multi-chain settings also encounter the interoperability issues. In this work, we propose a Relay chain and Asynchronous consensus-based Consortium blockchain cross-Chain model, which realizes message transmission and cross-chain transactions in multiple chains by adopting the relay chain and cross-chain gateways. All nodes of the application chains and the relay chain execute cross-chain transactions in sequence and reach a consensus on transactions at any transmission delay. Our experiment evaluations demonstrate that our approach performs well in atomicity, security, and functionality (cross-chain transactions), such that the performance of blockchain scalability in the metaverse can be improved, compared with the traditional relay chain schemes.

The privacy-preserving query is critical for modern blockchain systems, especially when supporting many crucial applications such as finance and healthcare. Recent advances in blockchain query schemes mainly focus on enhancing the traceability efficiency of integrity authentication. Despite these efforts, we argue that the exposure of retrieval information may result in privacy leakage, which inevitably poses an important yet unresolved challenge. In this paper, we introduce Cloak, a novel privacy-preserving blockchain query scheme with two notable features. First, it utilizes a two-phase distributed query requests technique, i.e., division and aggregation, to hide retrieval information based on the natural independent characteristic of blockchain. Second, we add noise to the sub-request set to avoid malicious attacks during transmission and adopt smart contract-based asymmetric encryption to guarantee the correctness of query results. Experimental results demonstrate that Cloak improves the query performance by up to 4× and reduces the storage overhead by 50% compared with the state-of-the-art Spiral.

Vertical Federated Learning (VFL) effectively addresses the issue of data isolation, which makes data mining secure. Most VFL implementations rely on a single server or third party for training, which will be terminated if the server or third party fails. In addition, the model accuracy trained by VFL depends on the quality of the client’s local features; nevertheless, the client’s local feature quality is difficult to verify. There exists a chance that the features owned by the client are irrelevant to the model or the intermediate results submitted by the client are inaccurate, such that the model’s accuracy will be seriously affected. In order to solve the single point failure and model accuracy issues in VFL, this paper first proposes a Blockchain – based Decentralized VFL (BDVFL) training model. With the integration of blockchain and the VFL training process, the nodes within the blockchain are categorized into non-training and training nodes. Our method focuses on the scenario in which all training nodes possess labeled data and actively engage in the training procedure of VFL. To be specific, first, each client utilizes local features and initial models to carry out forward activation and generate intermediate results. Second, we randomly choose a training node and combine it with the intermediate results from all clients to formulate the loss function. Finally, each client updates the local model by using the gradient. To protect the raw features, a blinding factor is utilized for safeguarding the intermediate results submitted by the client, such that the training nodes cannot infer the local features from intermediate results. To mitigate the interference of irrelevant training outcomes from clients on the model’s accuracy, we propose a verifiable aggregation method to assess the validity of the intermediate results submitted by the clients. We have conducted both theoretical and experimental analysis, and the results demonstrate the effectiveness of the proposed method.

As a new distributed machine learning (ML) framework for privacy protection, federated learning (FL) enables substantial Internet of Things (IoT) devices (e.g., mobile phones, tablets, etc.) to participate in collaborative training of an ML model. FL can protect the data privacy of IoT devices without exposing their raw data. However, the diversity of IoT devices may degrade the overall training process due to the straggler issue. To tackle this problem, we propose a gear-based asynchronous FL (AsyFed) architecture. It adds a gear layer between the clients and the FL server as a mediator to store the model parameters. The key insight is that we group these clients with similar training abilities into the same gear. The clients within the same gear conduct synchronous training. These gears then communicate with the global FL server asynchronously. Besides, we propose a T-step mechanism to reduce the weight from the slow gear when they are communicating with the FL server. The extensive experiment evaluations indicate that AsyFed outperforms FedAvg (baseline synchronous FL scheme) and some state-of-the-art asynchronous FL methods in terms of training accuracy or speed under different data distributions. The only negligible overhead is that we leverage the extra layer (gear layer) to preserve part of the model parameters.

Blockchain has been a promising infrastructure for enabling secure data sharing for the Internet of Things (IoT). With the widespread of IoT applications, security issues, such as data privacy, anonymity, and accountability become critical concerns for the users, which are essential principles for secure communication in those applications. However, the existing blockchain-based data-sharing schemes mainly consider data privacy. Only a few works can support anonymity with strong, trusted assumptions. Thus, there is a research gap on the anonymity of blockchain-based data sharing for IoT, which does not rely on any trusted party. In this article, we propose a blockchain-based anonymous data-sharing scheme (BA-DS) by adopting a novel public key encryption derived from a ring signature. In BA-DS, we remove the trusted party and ensure anonymity by using an unconditional linkable ring signature and Signature of Knowledge (SoK). During the revocation, we apply blockchain infrastructure to record the valid revocation list and generate a tag for data stored on the cloud, providing solid accountability. The formal security analysis shows that BA-DS is selective indistinguishable secure in the random oracle model. Additionally, we also prove that BA-DS holds anonymity, data privacy, accountability, and authenticity. The extensive experiments indicate that our proposed BA-DS achieves reasonable efficiency in terms of computational complexity, communication overhead, and consumption on the blockchain.

As a flourishing technical adoption boosted by 5G technology in recent years, Internet of Things (IoT) has become a common implementation in contemporary network-based solutions, such that tons of IoT devices, seen as nodes in the network, are employed. In order to meet the demands of the network edge, IoT devices mostly have limited computation and storage capabilities; however, this setting also introduces trust and security concerns. One of the noticeable threats is that malicious nodes forge or impersonate other legal nodes’ identities, which results in trustworthiness issues. In this article, we focus on the trust issue of IoT device identities and propose a cross-chain-based solution that applies a reply chain for identifying the legality of device nodes. Our proposed relay chain-based IoT model supports secure and reliable data transmission through the relay chain with the authorization and trust of all IoT blockchains. The relay chain realizes the multiparty verification of IoT devices; thus, addressing the trust problem of device identities. The consensus of the relay chain is executed by the arbitration committee nodes rather than all nodes. Considering the efficiency and adaptability, we also have proposed a novel optimal cross-chain decision-making algorithm to optimize the consensus for the identity-oriented relay chain. The proposed algorithm can minimize the cross-chain overhead while ensuring the fairness of the consensus. Our experiment evaluations have demonstrated that the proposed approach could increase the cross-chain efficiency under triple constraints.

Federated learning (FL) offers a promising approach to efficient machine learning with privacy protection in distributed environments, such as Internet of Things (IoT) and mobile-edge computing (MEC). The effectiveness of FL relies on a group of participant nodes that contribute their data and computing capacities to the collaborative training of a global model. Therefore, preventing malicious nodes from adversely affecting the model training while incentivizing credible nodes to contribute to the learning process plays a crucial role in enhancing FL security and performance. Seeking to contribute to the literature, we propose a blockchain-empowered secure and incentive FL (BESIFL) paradigm in this article. Specifically, BESIFL leverages blockchain to achieve a fully decentralized FL system, where effective mechanisms for malicious node detections and incentive management are fully integrated in a unified framework. The experimental results show that the proposed BESIFL is effective in improving FL performance through its protection against malicious nodes, incentive management, and selection of credible nodes.

As an emerging digital concept offering interconnections across multiple platforms, the metaverse provides digital transformations for various aspects of the physical world, facilitated by a few novel technologies, for example, cloud computing offers data support for the digital world. Humans immersed in the metaverse are digital entities who communicate with others or objects, such that ubiquitous access controls (UACs) are indispensable sectors for multiple platforms. However, in the metaverse, UACs have opened a wide scope of bridges for individuals to shuttle the virtual world, which implies that numerous threats exist at the access layer due to a great pool of entries. In this paper, to solve security issues in the UAC setting of the metaverse, we propose a novel blockchain-based multisignature lock for UAC (BMSL-UAC) scheme. All data institutions reconstruct a consortium blockchain system. In addition, our proposed scheme ensures that only authorized users can access an institution’s data. Finally, we abstract the user’s data access behaviors into the transaction information of the consortium blockchain system to realize full life-cycle data management and traceability. To verify the performance of our scheme, a series of experiments are carried out on the Hyperledger, and evaluation results have demonstrated that the resource consumption, delay, and throughput of this scheme are all within a reasonable range.

Data-driven applications play an important role in modern-time maritime transportation systems, for instance in facilitating decision-making relating to communication and safety. One example application is position data sharing between vessels within the maritime Internet of Things (IoT)-enabled context. When designing such applications, we need to also consider how to ensure data accuracy as well as privacy in a large scale deployment. In this paper, we demonstrate the potential of using blockchain to facilitate privacy-preserving data sharing. Specifically, we develop a zero-knowledge proof-based scheme to protect vessel identities while allowing data sharing, and a commitment-based approach to ensure relationship-related privacy in data trading between participants. Our security and performance evaluations demonstrate the utility of the proposed approach.

Multi-organization data sharing is becoming increasingly prevalent due to the interconnectivity of systems and the need for collaboration across organizations (e.g., exchange of data in a supply chain involving multiple upstream and downstream vendors). There are, however, data security concerns due to lack of trust between organizations that may be located in jurisdictions with varying security and privacy legislation and culture (also referred to as a zero trust environment). Hence, in such a zero trust setting, one should introduce strengthened, yet efficient, access control mechanisms to facilitate cross-organizational data access and exchange requests. Contemporary access control schemes generally focus on protecting a single objective rather than multiple parties, due to higher security costs. In this article, we propose a blockchain-based access control scheme, designed to facilitate lightweight data sharing among different organizations. Specifically, our approach utilizes the consortium blockchain to establish a trustworthy environment, in which a Role-Based Access Control (RBAC) model is then deployed using our proposed multi-signature protocol and smart contract methods. Evaluation of our proposed approach is performed on the HyperLedger Fabric consortium blockchain platform using both Caliper and BFT-SMaRT benchmarks, and the findings demonstrate the utility of our approach.

Digital twin is considered an alternative for optimizing real-world performance within virtual context, which also applies to the optimization ofSupply Chain Management(SCM). Blockchain, which facilitates data secure storage and trusted tracking, is deemed to be a proper assistant technology for achieving digital twin implementation. In this work, we propose a blockchain-based digital twin solution to reengineer SCM system, which promotes the digitization and intelligence of SCM to fit in massive service volumes in complex-intercrossed industry system. A strong-weak consensus mode is developed to achieve energy and time savings. We also design intelligent switch-based algorithms to generate time-saving consensus plans under energy constraints. Finally, we set up multiple experiments to compare our algorithm with three baseline algorithms, includingEffective Iterative Greedy(EIG),Two Dimensional Genetic(TDG), andHigh-level Task Scheduling Dynamic Programming(HTSDP). Findings from evaluation demonstrate the potential of our proposed model. Specifically, our algorithm reduces time and energy consumption of EIG algorithm in consensus by 46.84% and 16.25%, respectively. Compared with TDG algorithm, consensus time and energy consumption of our algorithm are reduced by 50.05% and 48.46%. Our algorithm cuts down time spent of HTSDP algorithm in generating consensus plan by a factor of 9.88.

As technology advances, critical infrastructure sectors are increasingly reliant on digital control systems and networking systems. Attacks targeting systems in critical infrastructure sectors are a matter of when and not if, as such systems are attractive to both financially motivated cyber criminals and state/politically motivated malicious actors. In this workshop, we will present findings that explore and demonstrate the various applications of blockchain in critical infrastructure sectors.

Fruit-80, an ultra-lightweight stream cipher with 80-bit secret key, is oriented toward resource constrained devices in the Internet of Things. In this paper, we propose area and speed optimization architectures of Fruit-80 on FPGAs. The area optimization architecture reuses NFSR&LFSR feedback functions and achieves the most suitable ratio of look-up-tables and flip-flops. The speed optimization architecture adopts a hybrid approach for parallelization and reduces the latency of long data paths by pre-generating primary feedback and inserting flip-flops. In conclusion, the optimal throughput-to-area ratio of the speed optimization architecture is better than that of Grain v1. The area optimization architecture occupies only 35 slices on Xilinx Spartan-3 FPGA, smaller than that of Grain and other common stream ciphers. To the best of our knowledge, this result sets a new record of the minimum area in lightweight cipher implementations on FPGA.

Summary With the development and popularization of Internet technology, network security has become the focus of attention. Vulnerabilities and back doors are regarded as two of the main reasons of network security problems. Dynamic heterogeneous redundant (DHR) architecture is a typical framework of cyberspace mimic defense. It can make use of untrusted hardware and software components to construct a high reliable and high security information system. In this article, a mathematical model is set up for the DHR architecture, and the system security is characterized by vulnerability consistency rate and success rate of system attack. The antiattack ability of the DHR system is analyzed using the mathematical model.

The concepts between Internet of Things (IoT) and edge computing are increasingly intertwined, as an edge-computing architecture generally comprises a (large) number of diverse IoT devices. This, however, increases the potential attack vectors since any one of these connected IoT devices can be targeted to facilitate other malicious cyber activities. Physical attacks are generally harder to mitigate and less studied, in comparison to their cyber counterparts. Thus, in this article we present an attack framework targeting true random number generators (TRNGs), which are a key component in cryptosystems for edge devices. We then demonstrate how such a framework can guide our investigation of a commercial ASIC chip that runs ring-oscillator-based TRNG. Specifically, we show that our template power attack, low voltage fault attack, and voltage glitch fault attack do not require prior knowledge of the TRNG implementation.

The recent COVID-19 pandemic has highlighted the importance of food safety and supply chain governance. In other words, we need to ensure traceability along the supply chain and support high-frequency transactions, effective data collections, etc. Thus, we posit the potential of using a lightning network, which is a decentralized traceable paradigm for achieving high-frequency transactions in blockchain-based systems. In addition, we also utilize edge computing to help facilitate data collection. However, a key challenge in securing food supplies is determining the optimal global transaction path in the lightning network while achieving efficiency and meeting the dynamic nature of food supply management. Thus, we propose a blockchain-edge scheme that utilizes our proposed dynamic programming to produce optimal solutions for selecting global transaction paths. Specifically, our scheme optimizes routing fees under existing constraints (e.g., transmission cost, computing resource consumption, and lightning network balance). The findings from our evaluations demonstrate the utility of our proposed approach in facilitating food safety management.

Sharing data across various Internet of Things (IoT) devices has been a common challenge due to efficiency, security, and stability issues. Blockchain, with security features, is considered to be a potential solution for data sharing in IoT settings. However, traditional blockchain-based solutions cannot satisfy the efficiency requirement of high-frequency data sharing among IoT devices. In this paper, we propose an efficient IoT data sharing approach by adopting the Payment Channel Network (PCN)-extended blockchain. Besides, we develop a homomorphic hashing-based transaction segmentation scheme to solve the issue of low transaction success ratio caused by channel deposit restrictions in PCN. In addition, a Multi-point Relay (MPR)-based multi-path routing scheme has been developed to ensure high-frequency transaction forwarding. The communication overhead of maintaining the routing table is reduced by our proposed Multi-point Relay Selection algorithm, and multiple alternate paths generated by Multiple Routing Path algorithm can improve the transaction success rate. Experiment evaluations have demonstrated that that our proposed approach outperforms the baseline approaches in terms of the transaction efficiency and success ratio.

The interest in fog computing is growing, including in traditionally conservative and sensitive areas such as military and governments. This is partly driven by the interconnectivity of our society, and advances in technologies such as Internet-of-Things (IoT). However, protecting against privacy leakage is one of several key considerations in fog computing deployment. Therefore, in this paper, we present a privacy-preserving multi-layer access filtering model, designed for a fog computing environment; hence, coined fog-based access filter (FAF). FAF comprises three key algorithms, namely: access filter initialization algorithm, optimal privacy-energy-time algorithm, and tuple reduction algorithm. Also, a hierarchical classification is used to distinguish the protection objectives. Findings from our experimental evaluation demonstrate that FAF allows one to achieve an optimal balance between privacy protection and computational costs.

Artificial Intelligence (AI) technology has been empowered to be a significant driven force within the edge context for powering up contemporary complex systems, such as smart critical infrastructure. Interconnectivity between physical and cyber spaces further introduces the needs of digital twin, which allows AI-based solutions to optimize various tasks in physical operations. However, due to the complexity of the setting in digital twin, task allocation is encountering multiple challenges, such as concurrent meeting the requirements of energy saving, efficiency, and accuracy. In this work, we propose a Digital Twin-Enabled Edge AI (DTE2AI), supported by our Energy-aware High Accuracy Strategy (EAHAS), which focuses on optimizing the training accuracy of AI tasks under the limits of training time and energy consumption. The average of the training accuracy was enhanced 12% based on our experiment evaluations.

The edge-cloud hybrid environment requires complex deployment strategies to enable the smart Internet-of-Things (IoT) system. However, current service deployment strategies use simple, generalized heuristics and ignore the heterogeneous characteristics in the edge-cloud hybrid environment. In this article, we devise a method to find a microservice-based service deployment strategy that can reduce the average waiting time of IoT devices in the hybrid environment. For this purpose, we first propose a microservice-based deployment problem (MSDP) based on the heterogeneous and dynamic characteristics in the edge-cloud hybrid environment, including heterogeneity of edge server capacities, dynamic geographical information of IoT devices, and changing device preference for applications and complex application structures. We then propose a multiple buffer deep deterministic policy gradient (MB_DDPG) to provide more preferable service deployment solutions. Our algorithm leverages reinforcement learning and neural network to learn a deployment strategy without any human instruction. Therefore, the service provider can make full use of limited resources to improve the Quality of Service (QoS). Finally, we implement MB_DDPG based on real-world data sets and some synthetic data, and we also implement another two algorithms, genetic algorithm and random algorithm, as a contrast. The experimental results demonstrate that MB_DDPG is able to learn a preferable strategy which, in terms of average waiting time, outperforms genetic algorithm and the random algorithm by 32% and 44%, respectively.

Internet of Things (IoT) makes great development and gains popularity with a mature combination with cloud computing. Plaintext data can be encrypted when data owners try to secure the confidentiality, while the encrypted data retrieval can be achieved by a cryptographic primitive named searchable encryption. Public-key encryption with keyword search (PEKS) is built on the asymmetric setting, however, the “honest-but-curious” assumption in PEKS creates challenges on the search reliability when malicious behaviors happen. It is nontrivial to enable the reliable search while preserving keyword privacy, as users who have paid need to receive either correct-and-wanted results or compensations. In this work, we apply blockchain to resolve this problem and design SearchBC, a blockchain-based PEKS framework supporting private, reliable, and fair encrypted search over the asymmetric setting. SearchBC is built on top of the blockchain and a keyword server to allow fairness in transactions and keyword preprocessing. We present a SearchBC instantiation and formally prove its security under the newly defined security model. SearchBC guarantees that search operations are fair and reliable and that the used keyword keeps privacy. The implementation results show that SearchBC provides a feasible means with reasonable communication and computation costs.

Privacy has become a considerable issue when the applications of big data are dramatically growing in cloud computing. The benefits of the implementation for these emerging technologies have improved or changed service models and improve application performances in various perspectives. However, the remarkably growing volume of data sizes has also resulted in many challenges in practice. The execution time of the data encryption is one of the serious issues during the data processing and transmissions. Many current applications abandon data encryptions in order to reach an adoptive performance level companioning with privacy concerns. In this paper, we concentrate on privacy and propose a novel data encryption approach, which is called Dynamic Data Encryption Strategy (D2ES). Our proposed approach aims to selectively encrypt data and use privacy classification methods under timing constraints. This approach is designed to maximize the privacy protection scope by using a selective encryption strategy within the required execution time requirements. The performance of D2ES has been evaluated in our experiments, which provides the proof of the privacy enhancement.

Combining the Internet-of-Things (IoT) technology with cloud computing is a significant alternative for powering the utilization of computing resources in a connected environment. A grand challenge in communications is raised by the emergence of big data, due to the large-sized data transmissions and frequent data exchanges. Applying fog computing is considered an option for resolving the communication challenge. However, a high extent of available heterogeneous computing attached to fog computing servers leads to a restriction of the resource management. This Article addresses the resource management issue by proposing a novel approach - named Energy-aware Fog Resource Optimization (EFRO) model- to optimizing the utilization of connected devices in fog computing. We develop a heuristic algorithm minimizing both energy cost and time consumption in a holistic way. A salient feature of EFRO lies in the integration of the standardization and smart shift operations fueled by a hill-climbing mechanism to produce near-optimal resource allocation solutions. Experimental results demonstrate that our EFRO is adroit at making near-optimal decisions in managing resources in fog computing environments. In particular, EFRO boosts the energy efficiency of the existing MESF and RR schemes by 54.83 and 71.28 percent, respectively. EFRO shortens DECM's allocation-generation time by up to a factor of 507.

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in networked environments, such as places of mass gatherings, smart cities and smart nations. For example, UAVs can be deployed to detect violations of lockdown, stay-at-home or social / physical distancing directives during pandemics (e.g. COVID-19). There are, however, security and privacy considerations in such deployments. To achieve secure and efficient authentication of UAVs, solutions such as those based on Point-to-Point (P2P) or a Point- to-Multipoint (P2M) communications have been proposed in the literature. In this article, we present a novel blockchain-based technique to support multi-party authentication to facilitate trustworthy group communications. Specifically, this allows us to provide secure P2P wireless communications and trusted group communication management for UAV networks, while ensuring service efficiency. Evaluation findings from both real-world implementation and simulations demonstrate the utility of the proposed approach.

In existing ensemble learning algorithms (e.g., random forest), each base learner’s model needs the entire dataset for sampling and training. However, this may not be practical in many real-world applications, and it incurs additional computational costs. To achieve better efficiency, we propose a decentralized framework:Multi-Agent Ensemble.The framework leverages edge computing to facilitate ensemble learning techniques by focusing on the balancing of access restrictions (small sub-dataset) and accuracy enhancement. Specifically, network edge nodes (learners) are utilized to model classifications and predictions in our framework. Data is then distributed to multiple base learners who exchange data via an interaction mechanism to achieve improved prediction. The proposed approach relies on a training model rather than conventional centralized learning. Findings from the experimental evaluations using 20 real-world datasets suggest that Multi-Agent Ensemble outperforms other ensemble approaches in terms of accuracy even though the base learners require fewer samples (i.e., significant reduction in computation costs).

Side-channel analysis achieves key recovery by analyzing physical signals generated during the operation of cryptographic devices. Power consumption is one kind of these signals and can be regarded as a multimedia form. In recent years, many artificial intelligence technologies have been combined with classical side-channel analysis methods to improve the efficiency and accuracy. A simple genetic algorithm was employed in Correlation Power Analysis (CPA) when apply to cryptographic algorithms implemented in parallel. However, premature convergence caused failure in recovering the whole key, especially when plenty of large S-boxes were employed in the target primitive, such as in the case of AES. In this article, we investigate the reason of premature convergence and propose a Multiple Sieve Method (MS-CPA), which overcomes this problem and reduces the number of traces required in correlation power analysis. Our method can be adjusted to combine with key enumeration algorithms and further improves the efficiency. Simulation experimental results depict that our method reduces the required number of traces by and , compared to classic CPA and the Simple-Genetic-Algorithm-based CPA (SGA-CPA), respectively, when the success rate is fixed to . Real experiments performed on SAKURA-G confirm that the number of traces required for recovering the correct key in our method is almost equal to the minimum number that makes the correlation coefficients of correct keys stand out from the wrong ones and is much less than the numbers of traces required in CPA and SGA-CPA. When combining with key enumeration algorithms, our method has better performance. For the traces number being 200 (noise standard deviation ), the attacks success rate of our method is , which is much higher than the classic CPA with key enumeration ( success rate). Moreover, we adjust our method to work on that DPA contest v1 dataset and achieve a better result (40.04 traces) than the winning proposal (42.42 traces).

The deployment of datasets in the heterogeneous edge-cloud computing paradigm has received increasing attention in state-of-the-art research. However, due to their large sizes and the existence of private scientific datasets, finding an optimal data placement strategy that can minimize data transmission as well as improve performance, remains a persistent problem. In this study, the advantages of both edge and cloud computing are combined to construct a data placement model that works for multiple scientific workflows. Apparently, the most difficult research challenge is to provide a data placement strategy to consider shared datasets, both within individual and among multiple workflows, across various geographically distributed environments. According to the constructed model, not only the storage capacity of edge micro-datacenters, but also the data transfer between multiple clouds across regions must be considered. To address this issue, we considered the characteristics of this model and identified the factors that are causing the transmission delay. The authors propose using a discrete particle swarm optimization algorithm with differential evolution (DE-DPSO) to distribute dataset during workflow execution. Based on this, a new data placement strategy named DE-DPSO-DPS is proposed. DE-DPSO-DPS is evaluated using several experiments designed in simulated heterogeneous edge-cloud computing environments. The results demonstrate that our data placement strategy can effectively reduce the data transmission time and achieve superior performance as compared to traditional strategies for data-sharing scientific workflows.

WiFi access points are sources of considerable security risks as the wireless signals have the potential to leak important private information such as passwords. This article examines the security issues posed by point-of-sale (POS) terminals which are widely used in WiFi-covered environments, such as restaurants, banks, and libraries. In particular, we envisage an attack model on passwords entered on POS terminals. We put forward the WiPOS, a password inference system based on wireless signals. Specifically, the WiPOS is a device-free system that uses two commercial off-the-shelf (COTS) devices to collect WiFi signals. Implementing a new keystroke segmentation algorithm and adopting support vector machine (SVM) classifiers with global alignment kernel (GAK), the WiPOS achieves improvement on both keystroke recognition and password prediction. The experimental results show that the WiPOS can achieve more than 73% accuracy for 6-digit password with the top 100 candidates. This article calls the community to take a closer look at the risks posed by the current ubiquitous WiFi devices.

Recent expansions of Internet-of-Things (IoT) applying cloud computing have been growing at a phenomenal rate. As one of the developments, heterogeneous cloud computing has enabled a variety of cloud-based infrastructure solutions, such as multimedia big data. Numerous prior researches have explored the optimizations of on-premise heterogeneous memories. However, the heterogeneous cloud memories are facing constraints due to the performance limitations and cost concerns caused by the hardware distributions and manipulative mechanisms. Assigning data tasks to distributed memories with various capacities is a combinatorial NP-hard problem. This paper focuses on this issue and proposes a novel approach, Cost-Aware Heterogeneous Cloud Memory Model (CAHCM), aiming to provision a high performance cloud-based heterogeneous memory service offerings. The main algorithm supporting CAHCM is Dynamic Data Allocation Advance (2DA) Algorithm that uses genetic programming to determine the data allocations on the cloud-based memories. In our proposed approach, we consider a set of crucial factors impacting the performance of the cloud memories, such as communication costs, data move operating costs, energy performance, and time constraints. Finally, we implement experimental evaluations to examine our proposed model. The experimental results have shown that our approach is adoptable and feasible for being a cost-aware cloud-based solution.

Contemporarily, two emerging techniques, blockchain and edge computing, are driving a dramatical rapid growth in the field of Internet-of-Things (IoT). Benefits of applying edge computing is an adoptable complementarity for cloud computing; blockchain is an alternative for constructing transparent secure environment for data storage/governance. Instead of using these two techniques independently, in this article, we propose a novel approach that integrates IoT with edge computing and blockchain, which is called blockchain-based Internet of Edge model. The proposed model, designed for a scalable and controllable IoT system, sufficiently exploits advantages of edge computing and blockchain to establish a privacy-preserving mechanism while considering other constraints, such as energy cost. We implement experiment evaluations running on Ethereum. According to our data collections, the proposed model improves privacy protections without lowering down the performance in an energy-efficient manner.

The blooming trend of smart grid deployment is engaged by the evolution of the network technology, as the connected environment offers various alternatives for electrical data collections. Having diverse data sharing/transfer means is deemed an important aspect in enabling intelligent controls/governance in smart grid. However, security and privacy concerns also are introduced while flexible communication services are provided, such as energy depletion and infrastructure mapping attacks. This paper proposes a model permissioned blockchain edge model for smart grid network (PBEM-SGN) to address the two significant issues in smart grid, privacy protections, and energy security, by means of combining blockchain and edge computing techniques. We use group signatures and covert channel authorization techniques to guarantee users' validity. An optimal security-aware strategy is constructed by smart contracts running on the blockchain. Our experiments have evaluated the effectiveness of the proposed approach.

Nonintrusive load monitoring (NILM) has attracted tremendous attention owing to its cost efficiency in electricity and sustainable development. NILM aims at acquiring individual appliance power consumption rates using an aggregated power smart meter reading. Each individual appliance's power consumption enables users to monitor their electricity usage habits for rational saving strategies. This is also a valuable tool for detecting failure in appliances. However, the major barriers facing NILM schemes are issues of accurately capturing the features of each appliance and decreasing the computing time. Motivated by these challenges, we propose a new, efficient, and accurate NILM scheme, consisting of a learning step and a decomposing step. In the learning step, we propose the fast search-and-find of density peaks (FSFDPs) clustering algorithm aimed at capturing the features of the power consumption patterns of appliances. In the decomposing step, we propose a genetic algorithm (GA)-based matching algorithm to estimate the power consumption of each individual appliance using the aggregated power reading. Using elitist and catastrophic strategies, this step reduces the searching space to achieve considerable efficiency. Experimental results using the reference energy disaggregation dataset (REDD) indicate that our proposed scheme promotes accuracy by 10% and reduces the decomposing time by half.

Implementing blockchain techniques has enabled secure smart trading in many realms, e.g. neighboring energy trading. However, trading information recorded on the blockchain also brings privacy concerns. Attackers can utilize data mining algorithms to obtain users' privacy, specially, when the user group is located in nearby geographic positions. In this paper, we present a consortium blockchain-oriented approach to solve the problem of privacy leakage without restricting trading functions. The proposed approach mainly addresses energy trading users' privacy in smart grid and screens the distribution of energy sale of sellers deriving from the fact that various energy trading volumes can be mined to detect its relationships with other information, such as physical location and energy usage. Experiment evaluations have demonstrated the effectiveness of the proposed approach.

Summary The recent booming development of cloud computing has enabled a dramatical revolution for current enterprises to create values. Numerous benefits of utilizing cloud services are enhancing the efficiency of improving or creating new businesses. This trend is also associated with the rapid growth of the wireless networks, such as 5G. However, the great increase of deploying clouds also leads to new concerns in data security. One of the major concerns is private leakage that derives from insiders' malicious operations or attacks. This problem has raised a great restriction for financial firms to execute cloud applications. Focusing on this issue, we propose a new solution, entitled Privacy‐Preserving Smart Storage (PS2) model that targets at solving the privacy leakage problems within the existing threat models. The proposed approach uses a novel distributed data storage method to prevent financial enterprises from insiders' massive data mining‐based attacks.

With mobile devices (e.g., Android and iOS devices) and other resource constrained Internet-connected devices (e.g., sensors) becoming the norm in our digitalized society, the capacity to ensure security of data-in-transit and at-rest without incurring unrealistic performance overheads is crucial. Rather than using conventional encryption, in this paper we proposethe dynamic privacy protectionmodel. The model is designed for ensuring mobile device user privacy even in large volume of data transmissions, which uses dynamic programming to produce an optimal solution of maximizing privacy protection levels even for resource constrained devices. We then develop an Android app and use it to evaluate the effectiveness of the model. The findings suggest that the proposed model allows us to achieve improved privacy protection.

Recent booming growth of networking-based solutions have brought numerous challenges to security and privacy from both perspectives of insider and outsider threats. The encrypted data are relatively considered a safe storage status; however, the process of encrypting data is still facing adversarial actions and data process generally is inapplicable over ciphertexts. As a type of the encryption approach allowing computations over ciphertexts, a fully homomorphic encryption (FHE) can concurrently deal with the adversarial hazards and support computations on ciphertexts. This paper focuses on the issue of blend arithmetic operations over real numbers and proposes a novel tensor-based FHE solution. The proposed approach is called a FHE for blend operations model that uses tensor laws to carry the computations of blend arithmetic operations over real numbers. In our paper, we provide both theoretical proof and experimental evaluations in order to evince the adoptability of the proposed approach.

The substantial growth of the distributed computing using heterogeneous computing has enabled great expansions inCyber Physical Systems(CPS). Combining CPS with heterogeneous cloud computing is an alternative approach for increasing sustainability of the system. However, execution of resource management in cloud systems is still encountering a few challenges, including the bottlenecks of the Web server capacities and task assignments in the heterogeneous cloud. The unstable service demands often result in service delays, which embarrasses the competitiveness of the enterprises. This paper addresses the problem of the task assignment in heterogeneous clouds, which is proved as a NP-hard problem. The proposed approach is calledSmart Cloud-based Optimizing Workload (SCOW) Modelthat uses predictive cloud capacities and considers sustainable factors to assign tasks to heterogeneous clouds. To reach the optimization objective, we propose a few algorithms, which includeWorkload Resource Minimization Algorithm(WRM),Smart Task Assignment (STA) Algorithm, andTask Mapping Algorithm(TMA). Our experimental evaluations have examined the performance of the proposed scheme.

The broad implementation of cloud computing have led to a dramatically growing in exchanging and using data throughout multiple parties. The main problem restricting the implementation of cloud computing is that users lack controls in cloud systems, from which security and privacy concerns become a major issue for cloud users. Logically, an applicable Fully Homomorphic Encryption (FHE) scheme is an effective solution to protecting data throughout the data usage lifecycle in the cloud system, due to the full control on users' own. However, there is no efficacious FHE scheme developed yet for meeting practical demands by the reason of either unqualified accuracy rate or intolerable latency time. Focus on this issue, we propose an advanced FHE scheme designed for operating real numbers, which is named as Full Homomorphic Encryption over Real Numbers (FHE-RN). Our approach has superb performances in both accuracy and efficiency, which has been proved by our experimental evaluations.

Virtual Reality (VR) research is accelerating the development of inexpensive real-time Brain Computer Interface (BCI). Hardware improvements that increase the capability of Virtual Reality displays and Brain Computer wearable sensors have made possible several new software frameworks for developers to use and create applications combining BCI and VR. It also enables multiple sensory pathways for communications with a larger sized data to users' brains. The intersections of these two research paths are accelerating both fields and will drive the needs for an energy-aware infrastructure to support the wider local bandwidth demands in the mobile cloud. In this paper, we complete a survey on BCI in VR from various perspectives, including Electroencephalogram (EEG)-based BCI models, machine learning, and current active platforms. Based on our investigations, the main findings of this survey highlights three major development trends of BCI, which are entertainment, VR, and cloud computing.

In order to explain the geological structure accurately and quickly, we analyze the gas mixture gathered from the well by Infrared Spectroscopy Fourier Transform Spectrometer instead of gas chromatograph. In the process of the spectrum analysis, the reduction of the spectrum data dimention is very necessary to perform. In this paper, we propose a feature extraction method is based on waveband selections using genetic algorithm, which is named FEWSGA. This approach can directly selecte eigenvalues from the limited waveband spectrum data instead of using mathematical transformation, such as the PCA (principal component analysis) and PLS (partial least squares) algorithm. Experiments results show that our method can reduce the spectrum data dimention from 1866 to 317, and the mean relative error (MRE) of the analysis model decrease from 34.68% to 26.59%. Moreover, the feature extraction from the whole waveband spectrum data using GA only reduce the data dimention from 1866 to 937. The MRE of the analysis model only reduces from 34.68% to 32.97%. Our approach has a better performance.

Summary The remarkable increasing demands of mitigating losses from cyber incidents for financial firms have been driving the rapid development of theCybersecurity Insurance(CI). The implementations of CI have covered a variety of aspects in cyber incidents, from hacking to frauds. However, CI is still at its exploring stage so that there are a number of dimensions that are uncovered by the current applications. The cyber attack on critical infrastructure is one of the serious issues that prevents the expansions of CI. This paper addresses CI implementations focusing on cloud‐based service offerings and proposes a secure cyber incident analytics framework using big data, named asCost‐Aware Hierarchical Cyber Incident Analytics(CA‐HCIA). The approach is designed for matching different cyber risk scenarios, which uses repository data. We use Monte Carlo simulations for extracting the incident features based on the training datasets. The main algorithms in CA‐HCIA includeMonte Carlo Cyber Feature Extraction (MC2FE)andOptimal Cost Balance (OCA) Algorithms. Our experimental evaluation has provided the theoretical proof of the adoptability and feasibility. Results show that our proposal improves the cost of existing techniques in 7.98% and 15.39%. Copyright © 2016 John Wiley & Sons, Ltd.

Summary Currently, the rapid increment of human detections has been changing people's daily life, However, the implementation is still facing the challenges caused by the restrictions of power and hardware. The computation process results in a heavy workload even though prior researches had made efforts to reduce the complexity. We focus on this issue and propose a new approach that employs the binarization‐based optimization algorithm to simplify the computation processes. Our approach is designed to minimize the necessary calculations and memory space. Moreover, we have developed the field‐programmable gate array‐based parallel architecture to apply partial dynamic reconfiguration, which enables the remote configurations for the number of processing units. An experimental evaluation is completed in order to examine our proposed approach. According to our experimental results, the detection precision can reach a miss rate less than 1.97% and a false positive rate of 1%. The energy cost is also reduced up to 36% comparing with the prior methods. Copyright © 2016 John Wiley & Sons, Ltd.

Summary Current rapid increasing implementations in data diversity, autonomy, and dynamic privilege management, fine‐grained access controls in social networks have resulted in various challenges in applying existing access control models. The intercrossing relations lead to the complex access control system, which often brings risks when the system is updated or expanded. The implementations of cloud computing has further complicate the access controls due to multiple tenancies and service providers. We focus on this issue and propose a new social network access control model using logical authorization language, named as RuleSN, which can be efficiently used in cloud systems. This model provides high performance of authorization expressiveness and flexibility that can effectively describe relations of User to User (U2U), User to Resource (U2R), Resource to Resource (R2R) and attributes of users and resources. First, this paper elaborates the formal definitions of the RuleSN model. Second, we describe the model's authorization specification and verification policies and explain the syntax and semantics of the authorization language. Finally, the implementation, application, and expressiveness of the model discussed by examples. Copyright © 2016 John Wiley & Sons, Ltd.

The expected advanced network explorations and the growing demand for mobile data sharing and transferring have driven numerous novel applications in Cyber-Physical Systems (CPSs), such as Intelligent Transportation Systems (ITSs). However, current ITS implementations are restricted by the conflicts between security and communication efficiency. Focusing on this issue, this article proposes a Security-Aware Efficient Data Sharing and Transferring (SA-EAST) model, which is designed for securing cloud-based ITS implementations. In applying this approach, we aim to obtain secure real-time multimedia data sharing and transferring. Our experimental evaluation has shown that our proposed model provides an effective performance in securing communications for ITS.

Different companies in the same line of business can have similar computer systems with built-in diagnostic routines, and the ability to regularly send error-driven or event-driven environmental diagnostic messages in XML backto the system manufacturer. The system manufacturer typically uses these to determine faults in the system. The outcome of this troubleshooting can also assist end-users and clients in solving problems, and provide the production team valuable information that can be used to improve future versions of the product. A Company merger could lead to the same team processing diagnostic messages from similar but different products, in different syntax, leading to complexity explosion of specifying and maintaining diagnostic message pattern specification and recognition for many different syntaxes. This research reduces the above complexity by extending ISO Schematron, the industry standard language for XML semantic constraints specification and validation, with conceptual rules. Pace University Knowledge Graphs are used to describe the concepts or classes relevant to the diagnostic messages of a system, and the new conceptual Schematron rules are introduced to specify diagnostic patterns on these concepts. Such conceptual diagnostic patterns are then converted automatically into concrete Schematron rules based on the syntax of the specific diagnostic messages. A complete prototype was designed and implemented to validate this new methodology.

Cyber Security breaches and attacks are on the ascendancy as corporations, governments, universities, and private individuals are conducting their business and personal transactions on the web. This increasing participating on the web necessitates that robust and efficient cyber security systems need to be put in place by these entities to safeguard their cyber assets. Intelligent Systems needs to be employed to buttress the cyber security protocols established in cloud computing for proper decision-making, which may depend on the effective knowledge representation. However, as one of the dominant industry standards for knowledge representation, Web Ontology Language (OWL) has limitations, such as the lack of support for custom relations. Pace University has extended OWL to support Knowledge Graph as a replacement to better support knowledge representation and decision making. This paper examines using KG as the basis in the design of a knowledge-representation system that drives the filtration process of a company's cyber security ecosystem in cloud computing by employing a use case of cyber security communications in-order to identify the entity relations of threat types for the filtration process.

In the past decade, the role of data has increased exponentially from something that is queried or reported on, to becoming a true corporate asset. The same time period has also seen marked growth in corporate structural complexity. This combination has lead to information management challenges, as the data moving across a multitude of systems lends itself to a higher likelihood of impacting dependent processes and systems, should something go wrong or be changed. Many enterprise data projects are faced with low success rates and consequently subject to high amounts of scrutiny as senior leadership struggles to identify return on investment. While there are many tools and methods to increase a companies' ability to govern data, this research is based on the premise that you can not govern what you do not know. This lack of awareness of the corporate data landscape impacts the ability to govern data, which in turn impacts overall data quality within organizations. This paper seeks to propose a tools and techniques for companies to better gain an awareness of the landscape of their data, processes, and organizational attributes through the use of linked data, via the Resource Description Framework (RDF) and ontology. The outcome of adopting such techniques is an increased level of data awareness within the organization, resulting in improved ability to govern corporate data assets, and in turn increased data quality.

The dramatical development of Web-based technology has been empowering enormous change in various domains. Cloud-based solutions have remarkably widened business models with multiple value creation channels. The financial industry is a major beneficiary of leveraging these emerging new technologies, such as big data and cloud-related services. This great changing trend has also led to a great concern in cybersecurity. Under this background, cybersecurity insurance is a growing domain in the financial industry. However, cybersecurity insurance industry also encounters a variety of cyber concerns while the Web-based approaches are applied. This paper focuses on this issue and review a broad scope of materials to gain a deep understanding of taxonomy of cyber security risks for cybersecurity insurance. The findings of this work can guide the cybersecurity insurance practitioners to avoid as much risk as possible as well as create potential solutions to the possible risks.

As a recent emerging industry, cybersecurity insurance has been growing ambitiously fast, which mainly serves the financial industry and assists financial firms to reduce cybersecurity risks. Understanding the risk classification is an important hemisphere for operating cybersecurity insurance. However, the classification representation will be complicated when the service system becomes large. Improper presentation of the risks can result in financial loss or operational mistakes. This paper addresses this concern and proposes an approach using ontology-based knowledge representation for cybersecurity insurance. The approach is named as Semantic Cyber Incident Classification (SCIC) model, which uses knowledge representation deriving from semantic techniques. Our approach is specifically designed for targeting at cybersecurity insurance domain, which has been assessed by our experiments.

Recent quick expansions of mobile heterogeneous embedded systems have led to a remarkable hardware upgrade that support multiple core processors. The energy consumption is becoming greater along with the computation capacity grows. Cloud computing is considered one of the solutions to mitigating energy costs. However, the simply offloading the computations to the remote side cannot efficiently reduce the energy consumptions when the energy costs caused by wireless communications are greater than it is on mobile devices. In this paper, we focus on the problem of energy wastes when tasks are assigned to remote cloud servers or heterogeneous core processors. Our solution aims to minimize the total energy cost of the mobile heterogeneous embedded systems by using an optimal task assignment to heterogeneous cores and mobile clouds. The propose model is named as Energy-Aware Heterogeneous Resource Management Model (EA-HRM2), which is supported by a main algorithm Optimal Heterogeneous Task Assignment (OHTA) algorithm. Our experimental evaluations have proved our approach is effective to save energy when deploying heterogenous embedded systems in mobile cloud systems.

Privacy issues have become a considerable issue while the applications of big data are growing dramatically fast in cloud computing. The benefits us implementing these emerging technologies have improved or changed service models and improve application performances in various perspectives. However, the remarkably growing volume of data sizes has also resulted in many challenges in practice. The time execution of encrypting data is one of the serious issues during the processes of data processing and transmissions. Many current applications abandon data encryptions in order to reach an adoptive performance level, companions with privacy concerns. In this paper, we concentrate on privacy issue and propose a novel data encryption approach, named as Dynamic Data Encryption Strategy (D2ES). Our proposed approach aims to selectively encrypt data using privacy classification methods under timing constraints. This approach is designed to maximize the privacy protection scope by using a selective encryption strategy within the required execution time requirements. The performance of D2ES has been evaluated in our experiments, which provides the proof of the privacy enhancement.

Drug side effects data contains important constraints about side-effects and conflict avoidance of component and compound drug. These are critically important in checking out prescriptions to avoid complications. Current drug data side effect representations in XML does not have a proper knowledge representation mechanism to clearly specify all kinds of dependencies among the drug components and drugs. Therefore Doctors and caregivers often rely on human interpretation to check prescriptions which can be error-prone. The recently introduced Web Ontology Language (OWL) based approach for medical drug side effects data representation still suffers from several shortcomings inherent to the OWL restrictions like using "is-a" relationship and usage of object property based workarounds losing the clarity and dynamic relationship building expected by domain experts to represent knowledge. The proposed model Drug-Side Effects Representation And Inferencing (D-SERI) built using Knowledge Graph (KG) and enhanced PaceJena shows that the proposed model allowsthe doctors and caregivers to derive dynamic information about side effects avoiding costly errors caused by human interpretation.

Cyberlearning is challenged by the lack of personal and assessment-driven learning, and students are often puzzled by the lack of instructor guidance and feedback, the huge volume and diversity of the learning materials, and thelack of the ability to zoom in from the general concepts to the more specific ones, or the opposite. Intelligent tutoring systems are needed to improve the cyberlearning quality. One of the major difficulties is knowledge representation. The current industry standard is to use Web Ontology Language (OWL) for representing knowledge structure. But OWL only supports one "first-class" relation, "is-a", between the concepts, and different knowledge areas usually need different custom relations to describe the relations among the concepts. For example "part-of" and time dependency are important relations torepresent most engineering knowledge bodies. OWL has to use object properties to emulate such custom relations, leading to awkward knowledge representation hard for domain experts to code, validate and use such knowledge bases. This research uses Pace University's extension to OWL, named Knowledge Graph (KG), to support knowledge representation with custom relations. The instructors can use Pace University extended Protege IDE to declare and apply custom relations in a single document. The instructor teaching experience is also coded in the KG to better support custom learning order by students with different backgrounds. The prototype of a knowledge-driven tutoring system was designed and implemented to illustrate how the KG supports integrated assessments, using assessment results to custom student learning order or material, and let the students freely navigate in the knowledge space from general to specific or the opposite, and following various custom relations. A web technology tutorial is used to validate the design and effectiveness of this approach.

Mobile cloud computing is applied in multiple industries to obtain cloud-based services by leveraging mobile technologies. With the development of the wireless networks, defending threats from wireless communications have been playing a remarkable role in the Web security domain. Intrusion detection system IDS is an efficient approach for protecting wireless communications in the Fifth Generation 5G context. In this paper, we identify and summarize the main techniques being implemented in IDSs and mobile cloud computing with an analysis of the challenges for each technique. Addressing the security issue, we propose a higher level framework of implementing secure mobile cloud computing by adopting IDS techniques for applying mobile cloud-based solutions in 5G networks. On the basis of the reviews and synthesis, we conclude that the implementation of mobile cloud computing can be secured by the proposed framework because it will provide well-protected Web services and adaptable IDSs in the complicated heterogeneous 5G environment. Copyright © 2015John Wiley & Sons, Ltd.

The dramatically growing demand of Cyber Physical and Social Computing (CPSC) has enabled a variety of novel channels to reach services in the financial industry. Combining cloud systems with multimedia big data is a novel approach for Financial Service Institutions (FSIs) to diversify service offerings in an efficient manner. However, the security issue is still a great issue in which the service availability often conflicts with the security constraints when the service media channels are varied. This paper focuses on this problem and proposes a novel approach using the Semantic-Based Access Control (SBAC) techniques for acquiring secure financial services on multimedia big data in cloud computing. The proposed approach is entitled IntercroSsed Secure Big Multimedia Model (2SBM), which is designed to secure accesses between various media through the multiple cloud platforms. The main algorithms supporting the proposed model include the Ontology-Based Access Recognition (OBAR) Algorithm and the Semantic Information Matching (SIM) Algorithm . We implement an experimental evaluation to prove the correctness and adoptability of our proposed scheme.

The City of White Plains has a residential population of 57,000 people, which increases every day by 225,000 people who come in from the outlying suburbs to work and shop in the city. Due to the traffic created and the large number of spread-out parking locations, there is an urgent demand of creating an mobile app allowing drivers to find available parking spaces easily. However, the solution specifically focusing on this issue has been rarely investigated by the prior research. In this paper, we represent our case study of a smart city web application using semantic design for a governmental organization's parking app. The app navigates customers to parking structures in conjunction with their destination. The app's user interface was constructed using HTML, CSS, and JavaScript, and a tag-based network structure to facilitate exploratory behavior. The work culminated in a completed user interface, a report detailing its perceived benefits, and an evaluation of its application. Use cases are described for three applications-the Passport app currently employed by the City of White Plains, the app developed in this study, and a future app using Internet of Things (IoT) technology.

Recent growth of cloud computing has driven the development of Heterogeneous Cloud Federation (HCF) that has been considered an approach of improving the diversity of cloud systems by enable cloud users to select resources provided by different service providers. Using this mechanism can divide a complicate task into a group of sub-tasks in order to increase the efficiency of application executions, which can support simultaneously executing different cloud systems. One of the resource allocation mechanisms is to schedule tasks among cloud providers. However, current challenge of implementing this mechanism is to generate an efficient task scheduling. We consider mobile cloud computing an adaptive approach for increasing the utilizations of mobile devices. Addressing this dimension, we propose our solution named Mobile Cloud-based Heterogeneous Resource Allocation Model (MC-HRAM), which is designed to minimize the execution time. The main algorithm used in the proposed model is Dynamic Heterogeneous Task Assignments Algorithm (DHTA). Our experimental evaluation has proved that our proposed schema has an advantage of saving execution time.

The broad implementation of cloud computing has brought a dramatic change to multiple industries, which derives from the development of the Internet-related technologies. This trend has enabled global enterprises to apply distributed computing techniques to reach many benefits. An effective risk management approach is required for service deliveries and a capacity planning is considered one of the convincing methods for financial industry. However, executing a capacity planning is still encountering a great challenge from bottlenecks of the Web server capacities. The unstable service demands often result in service delays, which embarrasses the competitivenesses of the enterprises. This paper addresses this issue and proposes an approach, named Efficiency-aware Cloud-based Workload Optimization (ECWO) Model, using greedy programming to predict server workloads of heterogeneous cloud computing in financial industry. The main algorithms used in the proposed model are Task Mapping Algorithm (TMA) and Efficiency-Aware Task Assignment (EATA) Algorithm. Our experimental evaluations have examined the performance of the proposed scheme.

The implementation of cloud computing in tele-health has enabled enormous benefits of improving health-care services as well as active health approaches, such as Patient-Centered Telehealth (PCT). Applying cloud systems enables healthcare users to obtain medical information from multiple cloud-based platforms or sources. Various service deployments meet different customers' needs. As one of the popular information sharing manners on clouds, Online Self-Diagnosis (OSD) has been widely implemented for recommending medicines or treatment plans in the varied medical fields. However, there is a great risk for users when executing OSD since lack of professional pharmacological knowledge. This paper concentrates on this issue and proposes an ontology-based approach representing the medical knowledge for alarming potential risks. The proposed mechanism is named Secure Ontology-based Self-diagnosis (SOS) Model that is designed to generate knowledge of medical treatments or incompatibility of medicines to avoid improper behaviors of diagnoses. Based on the proposed model, our algorithm uses structure-level technique applying semantic schemas, which is Propositional Satisfiability Matching Algorithm (PSMA). Our experiment has proved the feasibility of our proposed mechanism.

E-commerce using cloud-based trading platforms has become a popular approach with the growth of global development in recent years. However, the existence of counterfeits on the platform has threatened the benefits of all stakeholders. This paper proposes a novel scheme named Anti-Counterfeit Deterministic Prediction Model (ADPM), which is designed for detecting counterfeits by using Monte Carlo Model (MCM) to predict the potential malicious information in e-commerce. We consider the discriminations of the fake merchandises a crucial issue in preventing counterfeits on the online business platforms. The proposed mechanism provides a paradigm of machine-learning with using a novel algorithm that derives from MCM. The main algorithm used in our proposed mechanism is Monte Carlo Model-based Prediction Analysis Algorithm (M-PAA). Our experiment has evaluated that the proposed approach can provision the predictions of the insecure information in e-commerce.

The growing trend of the e-banking has driven the implementations of big data in financial industry. Data analytic is considered one of the most critical aspects in current economic development, which is broadly accepted in various financial domains, such as risk forecast and risk management. However, gaining an accurate risk prediction is still a challenging issue for current financial service institutions and the hazards can be caused in various perspectives. This paper proposes an approach using meta meta-analytics for risks forecast in big data. The proposed model is Meta Meta-Analytics Risk Forecast Model (MMA-RFM) with a crucial algorithm Regression with Meta Meta-Analytics Algorithm (RMMA). The proposed schema has been examined by the experimental evaluation in which it performs an optimized performance.

Building an effective Model Risk Management (MRM) system requires advanced skills in mutliple fields, such as technology, econometrics, and business. It is a dilemma that MRM practitioners are struggling with. Very often the enterprises are seeking someone who can understand their business ideas and transform them to technology solutions or come up with technology solutions to support their MRM ideas. However, many quantitative analysts fail in the required software development skills, while they are critical in this type of the project. We are witnesses these struggles where some model risk leaders fail in finding competent technologists or econometricians to implement their risk management philosophy. Also we have cases where technologists or econometricians cannot receive proper requirements to implement the functionality that the business needs. In this paper, we propose a MRM framework based on a review of MRM systems. Also we present the methodology of producing an effective MRM system.

Building a good financial Model Risk Management (MRM) system requires advanced skills in technology, econometrics and financial businesses. Very often we have businesses that have different models with similar functionality. The question that we are trying to answer in the paper is how to rationalize different models within an Enterprise Risk Management (ERM) of in organization. It is a dilemma that Risk Management Practitioners are struggling with. Very often they are looking for someone who can understand their business ideas and transform them to technology solutions or come up with technology solutions to support their MRM ideas. While quantitative Analysts are very critical in this type of project, very often they fail in the software development skills that are required. We are witnesses these struggles where some model risk leaders fail to find some good technologists or econometricians to implement their risk management philosophy. Also we have cases where technologists or econometricians cannot receive proper requirements to implement the functionality that the business needs. Here we are trying to understand how we can rationalize on different models that have some common functionality. For example how to look at or consolidate the functionality of the following models, such as Probability of Default (PD), Loss Given Default (LGD), Allowance for Loan and Lease Losses (ALLL), Stress Testing (ST) and others Financial Models. In this paper, we will present a framework for Model Rationalization or how to consolidate models. Also we will present a methodology that can be used to test a model that has been consolidated through our framework, Model Rationalization.

Cyber security has been used interchangeably for information security, where later considers the role of the human in the security process while former consider this as an additional dimension and also, focus person has a potential target. However, such discussion on cyber security has important implication as it focuses on the ethical part of the society as a whole. To address the issue of cyber security, various frameworks and models have been developed. It also introduces the concepts of cyber security in terms of its framework, workforces and information related to protecting personal information in the computer. This paper reviews these models along with their limitations and review the past techniques used to mitigate these threats. Furthermore, the report also provides recommendations for future research.

As a mobile operating system framework, Android plays a significant role in supporting mobile apps. Using cloud-based approaches have further enabled the implementations of mobile apps. However, current Android application model is not efficient by using current common approaches. In this paper, we proposed a new mobile app model using precache technology to overcome the current existing obstacles. The results of experiments show that our model can reduce network traffic of Android apps efficiently. At the same time, using our proposed model can reduce the data redundancy and improve maintainability of Android apps.

Green cloud is an emerging new technology in the computing world in which memory is a critical component. Phase-change memory (PCM) is one of the most promising alternative techniques to the dynamic random access memory (DRAM) that faces the scalability wall. Recent research has been focusing on the multi-level cell (MLC) of PCM. By precisely arranging multiple levels of resistance inside a PCM cell, more than one bit of data can be stored in one single PCM cell. However, the MLC PCM suffers from the degradation of performance compared to the single-level cell(SLC) PCM, due to the longer memory access time. In this paper, we present a genetic-based optimization algorithm for chip multiprocessor (CMP) equipped with PCM memory in green clouds. The proposed genetic-based algorithm not only schedules and assigns tasks to cores in the CMP system, but also provides a PCM MLC configuration that balances the PCM memory performance as well as the efficiency. The experimental results show that our genetic-based algorithm can significantly reduce the maximum memory usage by 76.8 percent comparing with the uniform SLC configuration, and improve the efficiency of memory usage by 127 percent comparing with the uniform 4 bits/cell MLC configuration. Moreover, the performance of the system is also improved by 24.5 percent comparing with the uniform 4 bits/cell MLC configuration in terms of total execution time.