Reviewerly/exHarmony huggingface dataset:
FLAP: Federated Learning Aggregation Scheme Based on Privileged Secret Sharing

Federated Learning (FL) is a privacy-preserving machine learning technique that trains models on client devices and only uploads new model gradients to servers for aggregation. However, transmitting the true gradients introduces the risk of reverse inference attack, which can compromise client privacy. To address this issue, we present the federated learning aggregation scheme based on privileged secret sharing (FLAP). Firstly, we present privileged secret sharing by combining the one-time pad with shamir secret sharing. Secondly, we introduce game theory to generate games between servers to resist collusion attacks. Finally, the analysis demonstrates that FLAP enables the server to successfully update the global gradient while ensuring the utmost protection of client privacy and FLAP achiev the effect of resisting collusion attack and reverse inference attack. We have proved through experiments that FLAP is 26.42% more efficient than the current scheme, and has strong robustness to drop out.
Covert Communication-Assisted Computation Offloading Schemes in Blockchain-Enabled IoT*

Blockchain-enabled Internet of Things (IoT) can ensure security and trust in distributed environments through consensus mechanisms, becoming important application scenarios for secure computation offloading of smart devices. In the IoT, smart devices usually upload complex computing tasks to the base station (BS), and then obtain corresponding mobile edge computing (MEC) servers to perform computing tasks. However, considering the broadcast characteristics of wireless links and the limited resources of smart devices, task upload links with weak security protection are easy to be monitored by adversaries. Since traditional information encryption technology and physical layer security (PLS) technology focus on protecting information content, the existence of upload links is difficult to be protected, which may attract potential adversaries to launch serious attacks after discovering links. Due to the fact that physical layer covert communications aim to complete information transmission without being discovered by adversaries, we propose covert communication-assisted computation offloading schemes in blockchain-enabled IoT for smart devices. The energy consumption of the computation offload schemes is optimized while ensuring the covertness of the task upload link, the matching satisfaction of sensors and MEC servers. Numerical results show that the proposed schemes can obtain the maximum effective covert rate while reducing the energy consumption of computation offloading.
A Detection Method for Alzheimer's Disease Based on Long-Term Visual Features

Alzheimer's disease (AD) is a neurodegenerative disease characterized by cognitive impairment. At present, there is no effective treatment for the disease, which has brought great harm to patients and society. Therefore, it is of great significance to explore the early detection method of Alzheimer's disease. In recent years, with the development of computer vision and deep neural networks, using deep neural networks to detect early Alzheimer's disease has attracted more and more researchers' attention. And gait abnormalities caused by Alzheimer's disease are gradually known. Based on this, this paper proposes a method to detect Alzheimer's disease using long-term visual features, and proves the effectiveness of the method through experiments. And discuss the characteristics of such tasks. It provides a basis for the development of deep neural network detection methods for cognitive disorders such as Alzheimer's disease in the future.
Scaling Blockchain via a Lightweight Tree-based Sharding System

The scalability issue in blockchain seriously restricts the performance of blockchain and limits the practical development of blockchain applications. Existing schemes are mainly divided into blockchain sharding and off-chain payment channels. Blockchain sharding technology splits the blockchain network into multiple sub-networks, each of which is called a shard. Each shard contains only some nodes, which can independently verify, store, and process transactions in parallel, thus improving the performance and handling capacity of the system. Most of the existing shard technologies start from the complete shard type, dividing each shard in terms of storage and communication, in order to maximize the system handling capacity. However, these approaches also bring with them the processing burden of cross-shard transactions. This paper proposes a sharding system with a tree-based structure, dividing the shard system into i-shards and b-shards according to their respective functions. Combining these two features with the consensus approach in design, we reduce the communication and consensus complexity of cross- shard transaction processing. In addition, we use a lightweight storage design, which reduces the storage pressure on the system. Finally, we analyze the safety of the system during operation and investigate the pressure advantage of utilizing this particular structure. With 6,400 nodes and 32 shards in the system, we come up with a pretty good security guarantee.
Privacy Enhanced Federated Learning via Privacy Masks and Additive Homomorphic Encryption

Federated learning has become an effective method to realize collaborative learning among multiple data centers. However, the problem of privacy leakage has not been perfectly solved. We proposed a scheme based on homomorphic encryption with random privacy masks to cope with the problem of private leakage caused by honest but curious participants. Through combining Paillier encryption scheme with privacy mask, security of the learning process is enhanced with a trivial computation and communication cost. Experimental results show that the decryption time is reduced by more than half after using CRT optimization, without degrading the training precision.
Attribute-based Verifiable Outsourcing Decryption Encryption Scheme in IIoT

With the continuous development of the Industrial Internet of Things (IIoT), more and more private data from IIoT devices is being outsourced to the cloud, and ensuring data privacy is becoming increasingly urgent. Attribute-based additive technology is gradually used in IIoT. However, attribute-based encryption technology will produce a lot of computing costs, which can not be satisfied by resource-constrained devices. Therefore, in the environment of edge support, some operations of encryption and decryption can be outsourced to edge nodes to reduce the computing cost of data owners. In this paper, we propose a verification-supported outsourcing attribute-based encryption scheme, which outsources part of the encryption operation and part of the decryption operation to the edge node, and adds the verifiable function to ensure that the ciphertext returned by the edge node is correct. And we analyze the security of the scheme. Experimental results show that the proposed scheme is more effective than the traditional scheme for resource-constrained IIoT devices.
Correction Algorithm of Multi-Person Pose Estimation Based on Improved YOLOv5

Since multi-person pose estimation in crowded scenes still suffers from problems such as small detection targets, resulting in low pose estimation accuracy, we propose a correction algorithm for multi-person pose estimation based on the improved YOLOv5. Firstly, in the backbone network of YOLOv5, the Jumping Attention Mechanism module is incorporated to help the networks find the region of interest in the images; Secondly, in the neck network, the joint use of the Jump Attention Mechanism module and the Transformer encoder allows the network to acquire global information and contextual information. Finally, the key point object information obtained from the network prediction is used to correct the pose object information to get the final multi-person pose estimation results. The experimental results show that the method in this paper improves AP50 by 2.2% and AP75 by 3.3% over YOLOv5 on the COCO dataset, which verifies the accuracy and robustness of the approach in this paper.
Privacy-Preserving Multi-User Joint Data Logistic Regression Inference Scheme

Nowadays, in the era of big data with rich sources of information, machine learning technologies are developing rapidly, and they are widely used in areas such as medical treatment, genomic prediction, spam detection, face recognition, and financial prediction. As a classification model in machine learning, the logistic regression model has become an industrial focus due to the simplicity and efficiency of algorithms. In this context, the “model as a service” business model has emerged. Specifically, companies with expertise and large-scale training data provide high-quality models to provide reasoning services for users. However, in machine learning classification models, it is important that feature data and inference models remain private. In view of this problem, the existing privacy inference technology considers that the single user has all the feature data, while the feature data of some scenarios exist between multiple users in the form of segmentation. In this paper, based on the fully homomorphic encryption CKKS scheme and the secure computing technology, this paper designs a privacy logic regression inference scheme suitable for multi-party data combination, which can simultaneously protect the model privacy of the server side, the data feature privacy of each user and the access rights of the inference results. Finally, experimental tests of the designed scheme are performed to verify its high performance.
A Survey of Edge Computing Resource Allocation Strategies Based on Federated Learning

In the edge computing environment, the data on edge computing is at risk of leakage due to the variety and wide distribution of nodes. As a new distributed machine learning framework, federated learning can effectively solve the privacy and security problems of users' information data in different fields. However, in federated learning, with the continuous landing of AI applications and the growing demand for model reasoning services, the resources consumed by federated learning will exceed the computing power of edge computing, so it is necessary to study the resource allocation strategy of edge computing for federated learning. This paper first introduces the concepts of federated learning and edge computing, and resource allocation strategies based on edge computing; Then it introduces the challenges faced by federated learning and the operating system framework of edge computing based on federated learning; Secondly, it combs the resource allocation strategy of edge computing based on federated learning; Finally, the paper summarizes the work of the full text and analyzes the future development trend of resource allocation under Federated learning.
Web Service Category Recommendation with Feature Word Semantic Enhancement and tag Co-occurrence

The generation quality of service function vectors in the existing Web category recommendation methods is not high. Most methods lack the consideration of label association. It may lead to inaccurate classification. To solve the above problems, this paper proposes a Web service category recommendation method with keyword semantic enhancement and label co-occurrence. TextRank is used to extract the keywords in the service description. The keyword vector and the service description vector generated by Bert are concatenated to enhance the semantic features of the service function vectors. With the help of the co-occurrence relationship of labels, a label co-occurrence graph is constructed, and Node2Vec is used for node embedding representation. The rationality of multi-label classification combination is improved by incorporating the co-occurrence degree of label combination. Real data in Programmable is used for label recommendation. Experiments show that the proposed method is superior to the comparison methods in terms of precision and recall rate. Thus, the proposed method effectively improves the accuracy and rationality of Web service category recommendation.
PPKD: Privacy-preserving Knowledge Distillation for Large Model

With the development of deep learning technology and the application of large-scale models, high training costs and a large number of model parameters have become bottlenecks that limit technological development. To address these issues, existing model distillation techniques can transfer knowledge from large models to small models, reducing the model size and computational resource consumption while maintaining high performance. However, existing research has overlooked the privacy protection of input data and large models during the distillation process. In practical scenarios, the entities who train student models and own teacher models may be different institutions or countries, and how to protect the privacy of training data and teacher models is a challenge facing cross-institutional distillation learning. To solve this problem, this paper proposes a privacy-preserving model distillation method combining random masking and threshold encryption systems. We introduce noise based on random masking into training data and encrypt the output of the teacher model, ensuring the privacy of the input data and teacher model while ensuring the performance of the distilled student model. We rigorously prove the security and correctness of the scheme in theory and validate its effectiveness through experiments. The experimental results show that the student model trained after data protection has a similar classification performance to the student model in the original distillation learning.
Research on Privacy Preserving Computing Technology in Edge Computing

With the rapid development of mobile Internet and Internet of Things applications, the traditional cloud computing model can provide centralized remote services, but when the amount of data increases sharply, it has been unable to effectively process the data generated by edge devices. To address these challenges and efficiently utilize distributed computing resources, edge computing emerges. With the rise of edge computing, more and more attention has been paid to its security threats and countermeasures. This paper first introduces the basic definition, architectural features, and current security threats of edge computing, then discusses the privacy protection issues in edge computing, and systematically expounds the latest research results at home and abroad. The application of privacy-preserving computing technology in computing, discusses the development trend of edge computing, and proposes further research directions.
Challenges of Livecast Computing Network: A Contemporary Survey

With the rise of Internet services in recent years, especially the flourishing development of new social platforms such as Taobao Live, Tiktok, Huya, YouTube, and Twitch, they have led to a significant increase in Internet video services. For example, YouTube Live stats reveal that 2020 was the biggest year ever for Gaming Live, with 100 billion watch time hours. As a result, the entire network communication model has gradually evolved into a Livecast computing network (LCN) architecture, where various network units involved in Internet live video services, with their computing, storage, distribution, and transmission capabilities, form an architecture that can meet high concurrency and low latency requirements. This article investigates, sorts, and analyzes LCN architecture related standards, protocols, and network model solutions from the perspective of Internet live video services. Firstly, it outlines the standards, system architecture, and main streaming media protocols of Internet live video. Secondly, it analyzes the video algorithm network architecture models, including cloud-based, edge-based, and cloud-network-edge collaborative models. Finally, it summarizes the main challenges and research directions faced by video algorithm networks in the future.
Research on Secure Data Circulation and Sharing Protection Technology Framework for Power Monitoring System

With the increasing frequency of data circulation and sharing in power monitoring systems, traditional network-centric protection strategies are insufficient to resist the growing risk of data leakage. This article analyzes the security risks in existing typical data circulation and sharing scenarios based on the demand for data security sharing in power monitoring systems. A data security protection technology framework covering the source layer, interaction layer, and terminal layer has been proposed from the perspectives of basic protection capabilities and key protection technologies, as well as an evaluation method for the protection technology capabilities of data circulation and sharing. This provides a reference idea for effectively enhancing the data circulation and sharing protection capabilities of power monitoring systems.
Enhancing Robustness Against Heterogeneity via Class-Difficulty Based Weights

Federated learning (FL) is a collaborative learning method on distributed data, which can protect data privacy, reduce server burden, and improve model performance. However, in practical applications, the data of different clients often do not satisfy the independent and identically distributed (IID) assumption, causing the instability, and non-convergence of model training. To solve this problem, some schemes have been proposed, such as personalized federated learning, data augmentation, model update and aggregation improvement, etc. However, these schemes do not fully consider the difficulty difference between client data, which may affect the robustness and generalization ability of the model on different clients. Therefore, in this paper, we propose an effective FL optimization method (CDB-W-FL) for non-IID data via Class-Difficulty Based Weights. We utilize the class-difficulty based weights to measure the difficulty level of each class sample on each client. Then, we apply the class difficulty weight to the model update and aggregation process, so that the model can pay more attention to the more difficult samples and balance the contributions of different clients. We conduct experiments on famous public datasets and show that the proposed CDB-W-FL achieves better performance than existing schemes in terms of accuracy, convergence speed, and robustness under non-IID data.
The Big data analysis on historical tweets on Indonesian Heroes' day

The changes in the historical text on Twitter are starting point of this research. The moment of the Heroes' Day commemoration on Twitter becomes the entry point to analyse these changes. Therefore, this study analyses historical text tweets at that moment by paying attention to the context surrounding the users. The study uses a content analysis method, emphasising a qualitative approach. Two data collection techniques are used to obtain accurate and faster details: manual observation and data crawling by Python program. This study found three dominant text variations. The first is a text containing a narrative about Bung Tomo, public information about the Hero, and the word "ex" (mantan -Indonesian) to commemorate users' relations. There are ten dominant words used in the three variations of the text.&#x0D;
Resisting Membership Inference Attacks by Dynamically Adjusting Loss Targets

Machine learning (ML) models are susceptible to membership inference attacks (MIAs), which aim to infer whether a particular sample was involved in model training. Previous research suggests that the difference in loss distribution between member and non-member sample is an essential factor of MIAs. In the latest mitigation strategies to reduce the loss distribution discrepancy, the model owner must manually set a loss target for the training task. However, this can be challenging due to differences in datasets and model structures. We propose a new mitigation strategy based on existing studies, which can dynamically adjust the loss target during the training process according to the model structure and dataset characteristics, to achieve a reduced loss gap. We extensively evaluated our strategy in white-box and black-box environments, respectively. Our experimental results show that our approach avoids the problem of setting loss targets and even improves the model's resistance to attacks in most cases. Specifically, the accuracy of the attacks is reduced by an average of 4.92% and 11.3% in the black-box and white-box environments, respectively.
Defense Against Adversarial Attacks via Adversarial Noise Denoising Networks in Image Recognition

Deep learning-based image recognition technology has significantly advanced the development of modern industrial intelligence. However, the issue of image adversarial examples that follows has gradually garnered the attention of researchers. By injecting adversarial disturbances that are difficult for humans to detect into the image, the deep learning model generates incorrect results, severely impacting the security of deep learning applications. To address this problem and improve the accuracy and robustness of deep learning image recognition models, a combined adversarial examples detection method based on residual learning and difference assessment has been proposed. To start, an image denoising module, based on residual learning and named ANDNet, is designed. This method incorporates a depth separable convolution structure and conducts step processing on the channel number of the hidden layer in ANDNet, which significantly reduces the model's memory consumption and improves its computational efficiency. In addition, the difference assessment involves computing the l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> norm distance between the original image and the denoised image softmax output of the classification model, to measure the disparity between the input image before and after denoising. The obtained detection threshold from the training dataset is integrated with this difference evaluation to accomplish the task of testing the input image for adversarial detection. Both theoretical analysis and experimental results confirm the effectiveness of the ANDNet noise reduction network, as well as the efficacy of the adversarial examples detection scheme in identifying adversarial examples. The model exhibits exceptional performance in terms of detection accuracy and F1 value.
E-PBFT: An Improved Consensus Mechanism Based on PBFT

To address the problems of high communication overhead and complexity of the practical Byzantine fault-tolerant (PBFT) consensus algorithm applied to federated chains, an efficient Byzantine fault-tolerant consensus algorithm E-PBFT is proposed based on the improved PBFT algorithm. First, the random matching rules are designed to design the all-nodes broadcast communication between nodes as a single communication of random nodes. Second, the random matching rules are embedded in the Commit phase of the PBFT algorithm. Finally, the progress of lagging nodes is synchronized using an acknowledgement mechanism. Experimental analysis shows that compared with the PBFT algorithm, the E-PBFT algorithm can significantly reduce the transaction latency and increase the throughput of the federated chain by more than 30% while having Byzantine fault tolerance.