Future Generation Computer Systems-The International Journal of Escience最新文献

英文中文

SNCD: A fast and scalable distributed near-miss code clone detector for big code based on partial index

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2025-02-06 DOI: 10.1016/j.future.2025.107743

Liming Yang , Jun Zhao , Rulin Xie, Yi Ren, Jianbo Guan, Bao Li, Jun Ma, Yusong Tan

A number of techniques have been proposed over the years to detect clones for improving software maintenance, reusability or security. However, there is still a lack of language agnostic approaches with code granularity flexibility for near-miss clone detection in big code in scale. It is challenging to detect near-miss clones in big code across large scale source repositories with hundreds of millions of lines of code (MLOC) or more. The main reason is that it requires more computing and memory resources as the scale of the source code increases. In particular, near-miss clone detection is more difficult and need more resources. In this paper, we present SNCD, a fast and scalable distributed clone detection approach. It overcomes single node CPU and memory resource limitation with MapReduce and HDFS by scalable distributed parallelization. Furthermore, it is partial index based and optimized with multi-threading strategy which further improve the efficiency. It can not only detect Type-1 and Type-2 clones but can also discover the most computationally expensive Type-3 clones for large repositories. Meanwhile, it works for both function and file granularities, and it supports many different programming languages. Experimental results show that SNCD scales better for big code with the size of code in terms of lines of code increases compared to existing clone detection techniques, with recall and precision comparable to state-of-art approaches. With BigCloneBench and the Mutation Framework, two recent and widely used benchmarks, SNCD achieves both high recall and precision, which is competitive with other existing tools.

{"title":"SNCD: A fast and scalable distributed near-miss code clone detector for big code based on partial index","authors":"Liming Yang , Jun Zhao , Rulin Xie, Yi Ren, Jianbo Guan, Bao Li, Jun Ma, Yusong Tan","doi":"10.1016/j.future.2025.107743","DOIUrl":"10.1016/j.future.2025.107743","url":null,"abstract":"<div><div>A number of techniques have been proposed over the years to detect clones for improving software maintenance, reusability or security. However, there is still a lack of language agnostic approaches with code granularity flexibility for near-miss clone detection in big code in scale. It is challenging to detect near-miss clones in big code across large scale source repositories with hundreds of millions of lines of code (MLOC) or more. The main reason is that it requires more computing and memory resources as the scale of the source code increases. In particular, near-miss clone detection is more difficult and need more resources. In this paper, we present SNCD, a fast and scalable distributed clone detection approach. It overcomes single node CPU and memory resource limitation with MapReduce and HDFS by scalable distributed parallelization. Furthermore, it is partial index based and optimized with multi-threading strategy which further improve the efficiency. It can not only detect Type-1 and Type-2 clones but can also discover the most computationally expensive Type-3 clones for large repositories. Meanwhile, it works for both function and file granularities, and it supports many different programming languages. Experimental results show that SNCD scales better for big code with the size of code in terms of lines of code increases compared to existing clone detection techniques, with recall and precision comparable to state-of-art approaches. With BigCloneBench and the Mutation Framework, two recent and widely used benchmarks, SNCD achieves both high recall and precision, which is competitive with other existing tools.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107743"},"PeriodicalIF":6.2,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A quantization-based technique for privacy preserving distributed learning

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2025-02-04 DOI: 10.1016/j.future.2025.107741

Maurizio Colombo , Rasool Asal , Ernesto Damiani , Lamees M. AlQassem , Al Anoud Almemari , Yousof Alhammadi

The distributed training of machine learning (ML) models presents significant challenges in ensuring data and parameter protection. Privacy-enhancing technologies (PETs) offer a promising initial step towards addressing these concerns, yet achieving confidentiality and differential privacy in distributed learning remains complex. This paper introduces a novel data protection technique tailored for the distributed training of ML models, ensuring compliance with regulatory standards. Our approach utilizes a quantized multi-hash data representation, known as Hash-Comb, combined with randomization to achieve Rényi differential privacy (RDP) for both training data and model parameters. The training protocol is designed to require only the common knowledge of a few hyper-parameters, which are securely shared using multi-party computation protocols. Experimental results demonstrate the effectiveness of our method in preserving both privacy and model accuracy.

引用次数: 0

Separation and optimization of encryption and erasure coding in decentralized storage systems

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2025-02-04 DOI: 10.1016/j.future.2025.107739

Marcell Szabó , Ákos Recse , Róbert Szabó , Dávid Balla , Markosz Maliosz

Entering the cloud storage market requires a high upfront investment, thus it is dominated by a few players with existing capacity. Decentralized cloud storage solutions can disrupt the status quo by allowing businesses and individuals to sell their unused storage capacity, reducing the need for large upfront investments in service infrastructure. We show that network operators providing such service can significantly decrease the traffic volume carried on the transport network, which is essential when serving mobile users, while maintaining high data security by implementing our proposed solution, of leveraging controlled replication inside the core network. Upon data uploads encryption and erasure encoding are separated, with the latter moved inside the network, enabling the arbitrary replication of storable data pieces without straining the access network. We present simulation results, showing that the proposed method reduces traffic by 20% compared to the out-of-the-box solution. Moreover, we elaborate on optimal multi-proxy placements and even optimal storage node choosings in complex ISP networks, where deep data penetration is desired, by giving ILP optimization methods and results, achieving minimal overall network load and maximum data security.

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引用次数: 0

Review on LoRa backscatter technology

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2025-02-03 DOI: 10.1016/j.future.2025.107742

Siaka Konaté , Changli Li , Lizhong Xu

In recent years, LoRa backscatter has been seen as a promising technology to enable long-range communication among low-power IoT devices. Several designs and potential applications of LoRa backscatter have been proposed in the literature. This paper aims to provide a fundamental background for general readers to understand the basic concepts, operation methods, and mechanisms and discusses future potential applications of LoRa backscatter as well as research issues related to such applications.

引用次数: 0

UFIDSF: An undersampling approach based on feature importance and double side filter for imbalanced data classification

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2025-02-02 DOI: 10.1016/j.future.2025.107750

Ming Zheng , Fei Wang , Xiaowen Hu , Liangchen Hu , Qingying Yu , Xiaoyao Zheng

Imbalanced data has the potential to detrimentally impact the efficacy of machine learning algorithms. If imbalanced data is not effectively processed, it will have a great impact on the classification results and reduce the reliability and practicability of modeling, so it has received widespread attention. From the past few decades to the present, various methods have emerged to solve the problem of imbalance data classification. The most common method is to start from the data level and realize data balance by resampling method. However, it remains a challenge to ensure that more valuable data is learned during the resampling process. Therefore, this study proposes an undersampling framework (UFIDSF) based on feature importance and double side filter. The first novelty of this framework is the use of double side filter to filter noise data in both majority and minority class samples. The second novelty is the projection of data samples into one dimension. UFIDSF is realized by calculating the distance between the feature of each dimension of the sample and its nearest neighbor and combining the feature importance. Experiments were conducted on 30 common benchmark imbalanced datasets, comparing the performance of 10 methods across four classifiers. Experimental results show that the proposed UFIDSF is effective and stable, and can significantly improve the adverse effects of machine learning algorithms on imbalanced data.

{"title":"UFIDSF: An undersampling approach based on feature importance and double side filter for imbalanced data classification","authors":"Ming Zheng , Fei Wang , Xiaowen Hu , Liangchen Hu , Qingying Yu , Xiaoyao Zheng","doi":"10.1016/j.future.2025.107750","DOIUrl":"10.1016/j.future.2025.107750","url":null,"abstract":"<div><div>Imbalanced data has the potential to detrimentally impact the efficacy of machine learning algorithms. If imbalanced data is not effectively processed, it will have a great impact on the classification results and reduce the reliability and practicability of modeling, so it has received widespread attention. From the past few decades to the present, various methods have emerged to solve the problem of imbalance data classification. The most common method is to start from the data level and realize data balance by resampling method. However, it remains a challenge to ensure that more valuable data is learned during the resampling process. Therefore, this study proposes an undersampling framework (UFIDSF) based on feature importance and double side filter. The first novelty of this framework is the use of double side filter to filter noise data in both majority and minority class samples. The second novelty is the projection of data samples into one dimension. UFIDSF is realized by calculating the distance between the feature of each dimension of the sample and its nearest neighbor and combining the feature importance. Experiments were conducted on 30 common benchmark imbalanced datasets, comparing the performance of 10 methods across four classifiers. Experimental results show that the proposed UFIDSF is effective and stable, and can significantly improve the adverse effects of machine learning algorithms on imbalanced data.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107750"},"PeriodicalIF":6.2,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143321950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Edge-cloud solutions for big data analysis and distributed machine learning - 2

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2025-02-01 DOI: 10.1016/j.future.2025.107745

Loris Belcastro , Jesus Carretero , Domenico Talia

In recent years, edge-cloud solutions have gained widespread adoption for efficiently collecting and analyzing IoT-generated data across various domains like urban mobility, healthcare, and smart cities. These solutions integrate resources from edge to cloud to support real-time processing and analysis tasks, reducing latency and network congestion. Big data analysis within this paradigm involves sophisticated techniques for distributed data processing, enabling applications such as predictive maintenance and smart grid management. Nevertheless, carrying out big data analysis within the edge-cloud presents several challenges, including data privacy and security, interoperability, scalability, and energy efficiency. Addressing these challenges is imperative for providing efficient and scalable solutions for data-intensive applications like federated learning, social data analysis, smart city services, and text mining. The special issue concludes with 27 scientific papers, divided into two parts for a streamlined editorial process. This editorial, as part two, presents 12 rigorously peer-reviewed papers, complementing the 15 papers covered in the previous editorial.

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引用次数: 0

P2-TaskMP: Privacy-Preserving Task Allocation Optimization Based on Mobility Prediction

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2025-02-01 DOI: 10.1016/j.future.2025.107720

Zhidong Xie , Tao Peng , Wei You , Guojun Wang , Qin Liu , Entao Luo

The emergence of Mobile Crowd Sensing (MCS) has provided a new paradigm for data sensing. An effective task allocation can ensure the stability and efficiency of the system in MCS. In this paper, we propose a privacy-preserving multi-objective, multi-task allocation optimization scheme, P2-TaskMP (Privacy-Preserving Task Allocation Optimization based on Mobility Prediction), to solve the multi-objective optimization task assignment problem while preserving users’ privacy. The scheme evaluates participants’ task completion capabilities by introducing mobility prediction based on fuzzy logic, which then informs task pre-allocation to form the initial population, unlike traditional methods that initialize populations randomly. To address potential privacy leaks of participants’ spatiotemporal information during mobility prediction, we employ differential privacy techniques to add Laplace noise to participants’ historical trajectory records, achieving adequate privacy protection. P2-TaskMP achieves Pareto-optimal solutions using the NSGA-II-DE (Non-dominated Sorting Genetic Algorithm II with Differential Evolution) algorithm and realizes satisfactory results with fast solution speed for large-scale task allocation problems. Simulations conducted on two real-world datasets demonstrate that our proposed method achieves higher accuracy, and the task allocation algorithm performs better than the compared algorithms in maximizing task completion rate and minimizing cost.

{"title":"P2-TaskMP: Privacy-Preserving Task Allocation Optimization Based on Mobility Prediction","authors":"Zhidong Xie , Tao Peng , Wei You , Guojun Wang , Qin Liu , Entao Luo","doi":"10.1016/j.future.2025.107720","DOIUrl":"10.1016/j.future.2025.107720","url":null,"abstract":"<div><div>The emergence of Mobile Crowd Sensing (MCS) has provided a new paradigm for data sensing. An effective task allocation can ensure the stability and efficiency of the system in MCS. In this paper, we propose a privacy-preserving multi-objective, multi-task allocation optimization scheme, P2-TaskMP (Privacy-Preserving Task Allocation Optimization based on Mobility Prediction), to solve the multi-objective optimization task assignment problem while preserving users’ privacy. The scheme evaluates participants’ task completion capabilities by introducing mobility prediction based on fuzzy logic, which then informs task pre-allocation to form the initial population, unlike traditional methods that initialize populations randomly. To address potential privacy leaks of participants’ spatiotemporal information during mobility prediction, we employ differential privacy techniques to add Laplace noise to participants’ historical trajectory records, achieving adequate privacy protection. P2-TaskMP achieves Pareto-optimal solutions using the NSGA-II-DE (Non-dominated Sorting Genetic Algorithm II with Differential Evolution) algorithm and realizes satisfactory results with fast solution speed for large-scale task allocation problems. Simulations conducted on two real-world datasets demonstrate that our proposed method achieves higher accuracy, and the task allocation algorithm performs better than the compared algorithms in maximizing task completion rate and minimizing cost.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107720"},"PeriodicalIF":6.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Achieving efficient and accurate privacy-preserving localization for internet of things: A quantization-based approach

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2025-02-01 DOI: 10.1016/j.future.2025.107740

Guanghui Wang , Xueyuan Zhang , Lingfeng Shen , Shengbo Chen , Fei Tong , Xin He , Wenyao Li

Privacy-preserving localization is an important enabling technology for location-based applications on the Internet of Things (IoT). Existing work utilizes encryption or noise-adding mechanism to develop privacy-preserving methods during the localization process. However, these methods still face the challenge of simultaneously achieve localization accuracy, privacy preservation and communication efficiency. To address the challenge, in this paper, a novel quantization-based privacy-preserving localization (QPPL) algorithm is proposed to estimate the target’s location with accuracy, privacy preservation and communication efficiency at the same time. Firstly, the location information is quantized, i.e., deviate the location data, to preserve the private location information during the localization process. With the quantization on the location information, the data scale is compressed to reduce communication cost and improve localization efficiency. Then, to improve the localization accuracy, an optimal weight allocation scheme is designed to aggregate the location estimates from the heterogeneous anchor devices. By minimizing the weighted sum of squared quantization errors of all anchor devices, a closed form optimal weight allocation scheme is derived by using convex optimization theory. Finally, through theoretical analysis, we prove the accuracy, privacy preservation and efficiency of the QPPL algorithm. Experimental evaluation demonstrates that QPPL has superior performance compared with existing methods.

{"title":"Achieving efficient and accurate privacy-preserving localization for internet of things: A quantization-based approach","authors":"Guanghui Wang , Xueyuan Zhang , Lingfeng Shen , Shengbo Chen , Fei Tong , Xin He , Wenyao Li","doi":"10.1016/j.future.2025.107740","DOIUrl":"10.1016/j.future.2025.107740","url":null,"abstract":"<div><div>Privacy-preserving localization is an important enabling technology for location-based applications on the Internet of Things (IoT). Existing work utilizes encryption or noise-adding mechanism to develop privacy-preserving methods during the localization process. However, these methods still face the challenge of simultaneously achieve localization accuracy, privacy preservation and communication efficiency. To address the challenge, in this paper, a novel quantization-based privacy-preserving localization (QPPL) algorithm is proposed to estimate the target’s location with accuracy, privacy preservation and communication efficiency at the same time. Firstly, the location information is quantized, i.e., deviate the location data, to preserve the private location information during the localization process. With the quantization on the location information, the data scale is compressed to reduce communication cost and improve localization efficiency. Then, to improve the localization accuracy, an optimal weight allocation scheme is designed to aggregate the location estimates from the heterogeneous anchor devices. By minimizing the weighted sum of squared quantization errors of all anchor devices, a closed form optimal weight allocation scheme is derived by using convex optimization theory. Finally, through theoretical analysis, we prove the accuracy, privacy preservation and efficiency of the QPPL algorithm. Experimental evaluation demonstrates that QPPL has superior performance compared with existing methods.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107740"},"PeriodicalIF":6.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prompting Robotic Modalities (PRM): A structured architecture for centralizing language models in complex systems

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2025-01-31 DOI: 10.1016/j.future.2025.107723

Bilel Benjdira, Anis Koubaa, Anas M. Ali

Despite significant advancements in robotics and AI, existing systems often struggle to integrate diverse modalities (e.g., image, sound, actuator data) into a unified framework, resulting in fragmented architectures that limit adaptability, scalability, and explainability. To address these gaps, this paper introduces Prompting Robotic Modalities (PRM), a novel architecture that centralizes language models for controlling and managing complex systems through natural language. In PRM, each system modality (e.g., image, sound, actuator) is handled independently by a Modality Language Model (MLM), while a central Task Modality, powered by a Large Language Model (LLM), orchestrates complex tasks using information from the MLMs. Each MLM is trained on datasets that pair modality-specific data with rich textual descriptions, enabling intuitive, language-based interaction. We validate PRM with two main contributions: (1) ROSGPT_Vision, a new open-source ROS 2 package (available at https://github.com/bilel-bj/ROSGPT_Vision) for visual modality tasks, achieving up to 66% classification accuracy in driver-focus monitoring—surpassing other tested models in its category; and (2) CarMate, a driver-distraction detection application that significantly reduces development time and cost by allowing rapid adaptation to new monitoring tasks via simple prompt adjustments. In addition, we develop a Navigation Language Model (NLM) that converts free-form human language orders into detailed ROS commands, underscoring PRM’s modality-agnostic adaptability. Experimental results demonstrate that PRM simplifies system development, outperforms baseline vision-language approaches in specialized tasks (e.g., driver monitoring), reduces complexity through prompt engineering rather than extensive coding, and enhances explainability via natural-language-based diagnostics. Hence, PRM lays a promising foundation for next-generation complex and robotic systems by integrating advanced language model capabilities at their core, making them more adaptable to new environments, cost-effective, and user-friendly.

{"title":"Prompting Robotic Modalities (PRM): A structured architecture for centralizing language models in complex systems","authors":"Bilel Benjdira, Anis Koubaa, Anas M. Ali","doi":"10.1016/j.future.2025.107723","DOIUrl":"10.1016/j.future.2025.107723","url":null,"abstract":"<div><div>Despite significant advancements in robotics and AI, existing systems often struggle to integrate diverse modalities (e.g., image, sound, actuator data) into a unified framework, resulting in fragmented architectures that limit adaptability, scalability, and explainability. To address these gaps, this paper introduces Prompting Robotic Modalities (PRM), a novel architecture that centralizes language models for controlling and managing complex systems through natural language. In PRM, each system modality (e.g., image, sound, actuator) is handled independently by a Modality Language Model (MLM), while a central Task Modality, powered by a Large Language Model (LLM), orchestrates complex tasks using information from the MLMs. Each MLM is trained on datasets that pair modality-specific data with rich textual descriptions, enabling intuitive, language-based interaction. We validate PRM with two main contributions: (1) ROSGPT_Vision, a new open-source ROS 2 package (available at <span><span>https://github.com/bilel-bj/ROSGPT_Vision</span><svg><path></path></svg></span>) for visual modality tasks, achieving up to 66% classification accuracy in driver-focus monitoring—surpassing other tested models in its category; and (2) CarMate, a driver-distraction detection application that significantly reduces development time and cost by allowing rapid adaptation to new monitoring tasks via simple prompt adjustments. In addition, we develop a Navigation Language Model (NLM) that converts free-form human language orders into detailed ROS commands, underscoring PRM’s modality-agnostic adaptability. Experimental results demonstrate that PRM simplifies system development, outperforms baseline vision-language approaches in specialized tasks (e.g., driver monitoring), reduces complexity through prompt engineering rather than extensive coding, and enhances explainability via natural-language-based diagnostics. Hence, PRM lays a promising foundation for next-generation complex and robotic systems by integrating advanced language model capabilities at their core, making them more adaptable to new environments, cost-effective, and user-friendly.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107723"},"PeriodicalIF":6.2,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EPPDL: An efficient privacy-preserving distributed ledger for digital asset transfer in Web3.0

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience

Pub Date : 2025-01-31 DOI: 10.1016/j.future.2025.107735

Lichuan Ma , Lu Zhou , Hang Huang , Youyang Qu , Xuefeng Liu

Nowadays, a new generation of decentralized internet framework, coined as Web3.0, is emerging. However, due to the insufficient computing power on the user side and the know-your-customer regulatory requirements, it is unrealistic to fully achieve decentralization in Web3.0 currently. The service provider-intermediated architecture seems more practical by including federated service providers. At the same time, in order to fully stimulate users to create and share contents in the era of Web3.0, the importance of digital assets, e.g., digit tokens and cryptocurrencies, is increasing. As a result, whether digital asset transfer can be securely and efficiently accommodated determines the further development of Web3.0. Blockchain is believed to be one effective solution to guarantee the security of digital asset transfer. However, existing works either target fully decentralized scenarios or fail to settle digital asset transfers with high efficiency. Thus in this paper, a formal yet novel service provider-intermediated architecture is firstly proposed to closely align with the practical requirements of Web3.0. Then, an efficient privacy-preserving distributed ledger construction protocol, coined as EPPDL, is proposed to safeguard digital asset transfer among users registered at different service providers. Concrete security analysis proves that the proposed EPPDL is secure against different types of adversaries, while comprehensive experiments verify its efficiency and effectiveness.

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