Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912767
Michaël Mahamat, Ghada Jaber, A. Bouabdallah
The Internet of Things (IoT) has gained in popularity over the years and is used in numerous applications. IoT networks employ many constrained devices, thus, finding energy is mandatory to maximize device and network lifetime. In this paper, we investigate a scheme based on wireless Mobile Chargers (MCs) to maximize device lifetime. Instead of transmitting energy to devices to only charge them back, we design a charging scheme considering the near future needs of the devices. We provide our ongoing research on a context-aware wireless energy transfer scheme to charge the devices according to the current and probable upcoming events. Our scheme is based on two modules: a context reasoning module predicting the possible future events in the IoT network and an intelligent Wireless Mobile Charger using Deep Reinforcement Learning (DRL). Our solution aims to establish a preventive charging scheme, considering the energy status and probable future events.
{"title":"A Deep Reinforcement Learning-Based Context-Aware Wireless Mobile Charging Scheme for the Internet of Things","authors":"Michaël Mahamat, Ghada Jaber, A. Bouabdallah","doi":"10.1109/ISCC55528.2022.9912767","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912767","url":null,"abstract":"The Internet of Things (IoT) has gained in popularity over the years and is used in numerous applications. IoT networks employ many constrained devices, thus, finding energy is mandatory to maximize device and network lifetime. In this paper, we investigate a scheme based on wireless Mobile Chargers (MCs) to maximize device lifetime. Instead of transmitting energy to devices to only charge them back, we design a charging scheme considering the near future needs of the devices. We provide our ongoing research on a context-aware wireless energy transfer scheme to charge the devices according to the current and probable upcoming events. Our scheme is based on two modules: a context reasoning module predicting the possible future events in the IoT network and an intelligent Wireless Mobile Charger using Deep Reinforcement Learning (DRL). Our solution aims to establish a preventive charging scheme, considering the energy status and probable future events.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132905682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912777
Zhao Li, Shijun Zhang, Jiang Yin, Meijie Du, Zhongyi Zhang, Qingyun Liu
Along with the development of video streaming, the increasing number of pirated video websites has caused unprecedented damage to copyright holders and potential security risks to their users. Though many efforts have been made to take down pirated video websites, they are still emerging by utilizing evading approaches like Fast-Flux domains and Cybercrime-as-a-Service(CaaS) tools. In this paper, to detect pirated video websites, we propose a Third-party Enhanced Pirated Video Website Classification Network (TEP-Net), which integrates both semantic features and relationship information between websites and their third-party services. More specifically, we apply CNN-BiLSTM-Attention to explore both character-level and domain-level textual embedding and utilize relationship information by constructing statistical features in classification. The experiment shows that TEP-Net achieves a significant performance compared with existing methods. Furthermore, we perform an in-depth analysis of the CaaS behind pirated video websites. Our research can help the security community fight against video piracy more precisely and effectively.
{"title":"Fighting Against Piracy:An Approach to Detect Pirated Video Websites Enhanced by Third-party Services","authors":"Zhao Li, Shijun Zhang, Jiang Yin, Meijie Du, Zhongyi Zhang, Qingyun Liu","doi":"10.1109/ISCC55528.2022.9912777","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912777","url":null,"abstract":"Along with the development of video streaming, the increasing number of pirated video websites has caused unprecedented damage to copyright holders and potential security risks to their users. Though many efforts have been made to take down pirated video websites, they are still emerging by utilizing evading approaches like Fast-Flux domains and Cybercrime-as-a-Service(CaaS) tools. In this paper, to detect pirated video websites, we propose a Third-party Enhanced Pirated Video Website Classification Network (TEP-Net), which integrates both semantic features and relationship information between websites and their third-party services. More specifically, we apply CNN-BiLSTM-Attention to explore both character-level and domain-level textual embedding and utilize relationship information by constructing statistical features in classification. The experiment shows that TEP-Net achieves a significant performance compared with existing methods. Furthermore, we perform an in-depth analysis of the CaaS behind pirated video websites. Our research can help the security community fight against video piracy more precisely and effectively.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132942670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912963
Md Tahmid Hossain, R. E. Grande
Vehicular Cloud Computing (VCC) exhibits many drawbacks with the demands of vehicular applications and intermittent network conditions. Vehicular Fog computing is a novel method for supporting and promoting the effective sharing of services and resources in urban areas. Diverse works on vehicular resource management have sought to handle the very dynamic vehicular environment using various methods, such as policy-based greedy and stochastic techniques. Nevertheless, high vehicular mobility poses many issues that compromise service consistency, efficiency, and quality. Adaptive vehicular Fogs incorporating Reinforcement Learning can deal with mobility and correctly distribute services and resources across all Fogs. Thus, we introduce an adaptive resource management model using cloudlet dwell time for resource estimation, mathematical formula for Fog selection, and reinforcement learning for iterative review and feedback mechanism for generating optimal resource allocation policy.
{"title":"Adaptive Q-leaming-supported Resource Allocation Model in Vehicular Fogs","authors":"Md Tahmid Hossain, R. E. Grande","doi":"10.1109/ISCC55528.2022.9912963","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912963","url":null,"abstract":"Vehicular Cloud Computing (VCC) exhibits many drawbacks with the demands of vehicular applications and intermittent network conditions. Vehicular Fog computing is a novel method for supporting and promoting the effective sharing of services and resources in urban areas. Diverse works on vehicular resource management have sought to handle the very dynamic vehicular environment using various methods, such as policy-based greedy and stochastic techniques. Nevertheless, high vehicular mobility poses many issues that compromise service consistency, efficiency, and quality. Adaptive vehicular Fogs incorporating Reinforcement Learning can deal with mobility and correctly distribute services and resources across all Fogs. Thus, we introduce an adaptive resource management model using cloudlet dwell time for resource estimation, mathematical formula for Fog selection, and reinforcement learning for iterative review and feedback mechanism for generating optimal resource allocation policy.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126999394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912766
Esmaeil Amiri, Ning Wang, S. Vural, R. Tafazolli
Distributed mobility management (DMM) solution is proposed to address the downsides of centralized mobility management protocols. The standard DMM is proposed for flat architectures and always selects the anchor point from access layer. Numerical analysis is used in this paper to show that dynamic anchor point selection can improve the performance of standard DMM in terms of packet signalling and delivery cost. In next step, an SDN-based DMM solution that we refer to as SD-DMM is presented to provide dynamic anchor point selection for hierarchical mobile network architecture. In SD-DMM, the anchor point is dynamically selected for each mobile node by a virtual function implemented as an application on top of the SDN controller which has a global view of the network. The main advantages of SD-DMM is to decrease packet delivery cost.
{"title":"Dynamic Anchor Point Selection in Software Defined Distributed Mobility Management","authors":"Esmaeil Amiri, Ning Wang, S. Vural, R. Tafazolli","doi":"10.1109/ISCC55528.2022.9912766","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912766","url":null,"abstract":"Distributed mobility management (DMM) solution is proposed to address the downsides of centralized mobility management protocols. The standard DMM is proposed for flat architectures and always selects the anchor point from access layer. Numerical analysis is used in this paper to show that dynamic anchor point selection can improve the performance of standard DMM in terms of packet signalling and delivery cost. In next step, an SDN-based DMM solution that we refer to as SD-DMM is presented to provide dynamic anchor point selection for hierarchical mobile network architecture. In SD-DMM, the anchor point is dynamically selected for each mobile node by a virtual function implemented as an application on top of the SDN controller which has a global view of the network. The main advantages of SD-DMM is to decrease packet delivery cost.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124774538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912984
Binal Tejani, R. E. Grande
The allocation and management of vehicular resources are essential in enabling services in Vehicular Cloud networks. Combined Resource Units (CRUs) allow for relaxed resource management by utilizing vehicular resources clustered in virtualized units and easing the fulfillment of service requests. Previous works have used mobility-based models such as SMDP and MDP for resource allocation. However, these models have presented significant system overhead, which has impacted the network's performance. Therefore, this work proposes a game theory model for assigning CRUs to satisfy service requests. The utility function of CRUs is maximized by playing a non-cooperative game between service requests. Two different game models are implemented based on exhaustive search and pruning methods. These models use distinct utility functions, which differ in terms of distance and signal strength of the CRUs. Comparing the performance of the two models, the pruning model offers a 90% success rate towards satisfying service requests.
{"title":"Fair Connectivity-Oriented Allocation for Combined Resources in VCC Networks","authors":"Binal Tejani, R. E. Grande","doi":"10.1109/ISCC55528.2022.9912984","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912984","url":null,"abstract":"The allocation and management of vehicular resources are essential in enabling services in Vehicular Cloud networks. Combined Resource Units (CRUs) allow for relaxed resource management by utilizing vehicular resources clustered in virtualized units and easing the fulfillment of service requests. Previous works have used mobility-based models such as SMDP and MDP for resource allocation. However, these models have presented significant system overhead, which has impacted the network's performance. Therefore, this work proposes a game theory model for assigning CRUs to satisfy service requests. The utility function of CRUs is maximized by playing a non-cooperative game between service requests. Two different game models are implemented based on exhaustive search and pruning methods. These models use distinct utility functions, which differ in terms of distance and signal strength of the CRUs. Comparing the performance of the two models, the pruning model offers a 90% success rate towards satisfying service requests.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125067182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912926
M. Mahzoun, L. Kraleva, R. Posteuca, T. Ashur
K-Cipher is an ultra low latency block cipher with variable-length parameters designed by Intel Labs. In this work, we analyze the security of K-Cipher and propose a differential cryptanalysis attack with the complexity of $2^{29.7}$ for a variant of K-Cipher with state size $n=24$ bits state and block size $m=8$ bits. Our attack recovers the secret key and secret randomizer values with a total length of 240 bits in $sim 30$ minutes on a standard desktop machine. We show that it is possible to extend the same attack for an arbitrary set of parameters.
{"title":"Differential Cryptanalysis of K-Cipher","authors":"M. Mahzoun, L. Kraleva, R. Posteuca, T. Ashur","doi":"10.1109/ISCC55528.2022.9912926","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912926","url":null,"abstract":"K-Cipher is an ultra low latency block cipher with variable-length parameters designed by Intel Labs. In this work, we analyze the security of K-Cipher and propose a differential cryptanalysis attack with the complexity of $2^{29.7}$ for a variant of K-Cipher with state size $n=24$ bits state and block size $m=8$ bits. Our attack recovers the secret key and secret randomizer values with a total length of 240 bits in $sim 30$ minutes on a standard desktop machine. We show that it is possible to extend the same attack for an arbitrary set of parameters.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127265329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912916
Md. Akbar Hossain, S. Ray, Geri Harris, Shakil Ahmed
Dementia patients living alone in the communities without much support from near and dear ones find it chal-lenging to receive instant help when needed including during emergencies. This work focuses on designing an end - to-end response system primarily to support early-stage Alzheimer's dementia (AD) patients living alone in their homes, in case of emergencies, including medical emergencies. The system aided with pervasive technologies can integrate AD patients, informal caregivers, and formal caregivers to support AD patients in need. Informal caregivers act as first responders to attend to patients and selecting appropriate informal caregivers based on certain predefined parameters is an important component of this system. This work has used single and ensemble Machine Learning (ML) algorithms for binary (to check if informal caregiver is available) and multiclass (to select the most suitable informal caregiver) classification.
{"title":"An Emergency Response System to Support Early Stage Dementia Patients","authors":"Md. Akbar Hossain, S. Ray, Geri Harris, Shakil Ahmed","doi":"10.1109/ISCC55528.2022.9912916","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912916","url":null,"abstract":"Dementia patients living alone in the communities without much support from near and dear ones find it chal-lenging to receive instant help when needed including during emergencies. This work focuses on designing an end - to-end response system primarily to support early-stage Alzheimer's dementia (AD) patients living alone in their homes, in case of emergencies, including medical emergencies. The system aided with pervasive technologies can integrate AD patients, informal caregivers, and formal caregivers to support AD patients in need. Informal caregivers act as first responders to attend to patients and selecting appropriate informal caregivers based on certain predefined parameters is an important component of this system. This work has used single and ensemble Machine Learning (ML) algorithms for binary (to check if informal caregiver is available) and multiclass (to select the most suitable informal caregiver) classification.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128840683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912980
Bruno Chianca Ferreira, G. Dufour, Guthemberg Silvestre
Emerging edge applications introduced new computing time-variant topologies with mobile nodes connected via ad hoc networks. Such topologies are fundamentally different from cloud infrastructures due to the lack of hierarchy and clear network function separation. Sometimes, nodes that are sources are also destinations and routers, hence, creating dynamic flow patterns traversing the network. The latter, thus, can change the average performance of a distributed system, such as throughput and latency. This work introduces an analytical model based on fluid quantities to study data flows of distributed systems in mobile ad hoc networks. Using an approach based on a network of queues with evenly distributed bandwidth over concurrent flows, this lightweight model enables fast, coarse-grained analysis of different distributed systems configurations. They enable the analysis of different topologies, mobility and data flow models with a small footprint. The model was implemented, validated and evaluated with stress workloads to confirm its accuracy.
{"title":"A Lightweight Fluid Model for Mobile Ad hoc Distributed Systems","authors":"Bruno Chianca Ferreira, G. Dufour, Guthemberg Silvestre","doi":"10.1109/ISCC55528.2022.9912980","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912980","url":null,"abstract":"Emerging edge applications introduced new computing time-variant topologies with mobile nodes connected via ad hoc networks. Such topologies are fundamentally different from cloud infrastructures due to the lack of hierarchy and clear network function separation. Sometimes, nodes that are sources are also destinations and routers, hence, creating dynamic flow patterns traversing the network. The latter, thus, can change the average performance of a distributed system, such as throughput and latency. This work introduces an analytical model based on fluid quantities to study data flows of distributed systems in mobile ad hoc networks. Using an approach based on a network of queues with evenly distributed bandwidth over concurrent flows, this lightweight model enables fast, coarse-grained analysis of different distributed systems configurations. They enable the analysis of different topologies, mobility and data flow models with a small footprint. The model was implemented, validated and evaluated with stress workloads to confirm its accuracy.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121026182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912971
J. Baranda, L. Vettori, J. Mangues‐Bafalluy, R. Martínez, E. Zeydan
Heterogeneity is one relevant characteristic of next generation mobile networks. This term embraces not only different kind of infrastructure resources or transmission technologies (e.g., networking vs computing, wireless vs optical), but it also applies to different ways of implementing the network functions (NFs) composing the network services (NSs). This allows the definition of hybrid NSs combining different kind of components, such as physical, virtual, and cloud-native NFs. This demonstration shows the enhancements introduced in the 5Growth platform to manage physical NFs, hence increasing the capabilities of this platform to cope with more heterogeneous hybrid NSs in multi-site scenarios. In particular, we show the deployment of an NS constituted by physical, virtual, and cloud-native NFs implementing an end-to-end service covering access, mobile core and application functionalities.
{"title":"Demo: Automated Multi-Site E2E Orchestration of Hybrid Network Services Mixing PNF, VNF and CNFs","authors":"J. Baranda, L. Vettori, J. Mangues‐Bafalluy, R. Martínez, E. Zeydan","doi":"10.1109/ISCC55528.2022.9912971","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912971","url":null,"abstract":"Heterogeneity is one relevant characteristic of next generation mobile networks. This term embraces not only different kind of infrastructure resources or transmission technologies (e.g., networking vs computing, wireless vs optical), but it also applies to different ways of implementing the network functions (NFs) composing the network services (NSs). This allows the definition of hybrid NSs combining different kind of components, such as physical, virtual, and cloud-native NFs. This demonstration shows the enhancements introduced in the 5Growth platform to manage physical NFs, hence increasing the capabilities of this platform to cope with more heterogeneous hybrid NSs in multi-site scenarios. In particular, we show the deployment of an NS constituted by physical, virtual, and cloud-native NFs implementing an end-to-end service covering access, mobile core and application functionalities.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121158503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912942
Donghui Ding, Bo Long, Feng Zhuo, Zhongcheng Li, Hanwen Zhang, Chen Tian, Yi Sun
Hashed Timelock Contract (HTLC) is a widely-used protocol for cross-chain asset swaps. However, it relies on serial asset-locking to guarantee atomicity, which causes high latency and poor fairness. Aiming at the drawbacks of HTLC, we propose Lilac, a cross-chain asset swap protocol that supports parallel asset-locking. Lilac replaces the unique asset-unlocking credential in HTLC with multiple sub-credentials generated by all participating users, and the sequence of sub-credentials is used as the complete asset-unlocking credential. Users obtain the complete credential only when all assets have been locked, and the credential construction process is independent of the order in which assets are locked, so atomicity can be guaranteed when users lock their assets in parallel. Experiments show when a swap involves 2 to 4 blockchains, Lilac reduces the swap latency by 36.75% to 62.20%. Moreover, Lilac reduces the waiting time gap between different users so the fairness of a swap is improved.
{"title":"Lilac: Parallelizing Atomic Cross-Chain Swaps","authors":"Donghui Ding, Bo Long, Feng Zhuo, Zhongcheng Li, Hanwen Zhang, Chen Tian, Yi Sun","doi":"10.1109/ISCC55528.2022.9912942","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912942","url":null,"abstract":"Hashed Timelock Contract (HTLC) is a widely-used protocol for cross-chain asset swaps. However, it relies on serial asset-locking to guarantee atomicity, which causes high latency and poor fairness. Aiming at the drawbacks of HTLC, we propose Lilac, a cross-chain asset swap protocol that supports parallel asset-locking. Lilac replaces the unique asset-unlocking credential in HTLC with multiple sub-credentials generated by all participating users, and the sequence of sub-credentials is used as the complete asset-unlocking credential. Users obtain the complete credential only when all assets have been locked, and the credential construction process is independent of the order in which assets are locked, so atomicity can be guaranteed when users lock their assets in parallel. Experiments show when a swap involves 2 to 4 blockchains, Lilac reduces the swap latency by 36.75% to 62.20%. Moreover, Lilac reduces the waiting time gap between different users so the fairness of a swap is improved.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131490660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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