Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912885
Gengbiao Yue, Yumei Wang, Yu Liu
In software-defined networks, due to the limited flow table capacity, unreasonable rule placement will cause the flow table overflow problem. These flows without flow rules installed need to be processed by controller, which increases and even unbalances controller load. Based on the average packet end-to-end delay, we propose a rule placement and switch migration-based scheme for controller load balancing. In the routing and rule placement phase, the Cost-Aware Routing (CAR) algorithm takes into account the flow table occupancy while utilizing the installed rules to alleviate flow table overflow and preliminarily balance the controller load. In the switch migration phase, the Benefit-Cost Switch Migration (BCSM) algorithm obtains the migration option with the maximum total benefit. Numerical results show that the CAR algorithm reduces and balances controller load to achieve lower delay than Random and FlowStat. And the BCSM algorithm balances the controller load and reduces packet delay than SMCS and ESMLB.
{"title":"Rule Placement and Switch Migration-based Scheme for Controller Load Balancing in SDN","authors":"Gengbiao Yue, Yumei Wang, Yu Liu","doi":"10.1109/ISCC55528.2022.9912885","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912885","url":null,"abstract":"In software-defined networks, due to the limited flow table capacity, unreasonable rule placement will cause the flow table overflow problem. These flows without flow rules installed need to be processed by controller, which increases and even unbalances controller load. Based on the average packet end-to-end delay, we propose a rule placement and switch migration-based scheme for controller load balancing. In the routing and rule placement phase, the Cost-Aware Routing (CAR) algorithm takes into account the flow table occupancy while utilizing the installed rules to alleviate flow table overflow and preliminarily balance the controller load. In the switch migration phase, the Benefit-Cost Switch Migration (BCSM) algorithm obtains the migration option with the maximum total benefit. Numerical results show that the CAR algorithm reduces and balances controller load to achieve lower delay than Random and FlowStat. And the BCSM algorithm balances the controller load and reduces packet delay than SMCS and ESMLB.","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":"117335610","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/ISCC55528.2022.9912940
A. Celesti, M. Fazio, Lorenzo Carnevale, M. Villari
Current speech recognition services are not suitable for people with speech disorders, which present difficulties in coordinating muscles and articulating words and sentences. In this case, a speaker-dependent approach is strongly required in order to address the specific vocal disarticulation. Several Deep learning approaches have been proposed in the literature to address this problem. However, they require many voice samples of people to properly work, and this is not practical. In this paper, we present an innovative Automatic Speech Recognition (ASR) system which is able to correct failures of deep learning based solution adopting Natural Language Processing (NLP) techniques. The proposed solution can perform both single word and whole sentence corrections by analyzing the speech context. We evaluated the solution in a home automation case study and proved the good accuracy of our model.
{"title":"A NLP-based Approach to Improve Speech Recognition Services for People with Speech Disorders","authors":"A. Celesti, M. Fazio, Lorenzo Carnevale, M. Villari","doi":"10.1109/ISCC55528.2022.9912940","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912940","url":null,"abstract":"Current speech recognition services are not suitable for people with speech disorders, which present difficulties in coordinating muscles and articulating words and sentences. In this case, a speaker-dependent approach is strongly required in order to address the specific vocal disarticulation. Several Deep learning approaches have been proposed in the literature to address this problem. However, they require many voice samples of people to properly work, and this is not practical. In this paper, we present an innovative Automatic Speech Recognition (ASR) system which is able to correct failures of deep learning based solution adopting Natural Language Processing (NLP) techniques. The proposed solution can perform both single word and whole sentence corrections by analyzing the speech context. We evaluated the solution in a home automation case study and proved the good accuracy of our model.","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":"123114246","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/ISCC55528.2022.9912469
Leah Kazenmayer, Gabriela Ford, Jiechao Zhang, Rezaur Rahman, Furkan Cimen, D. Turgut, Samiul Hasan
Predicting short-term traffic volume is essential to improve transportation systems management and operations (TSM0) and the overall efficiency of traffic networks. The real-time data, collected from Internet of Things (loT) devices, can be used to predict traffic volume. More specifically, the Automated Traffic Signal Performance Measures (ATSPM) data contain high-fidelity traffic data at multiple intersections and can reveal the spatio-temporal patterns of traffic volume for each signal. In this study, we have developed a machine learning-based approach using the data collected from ATSPM sensors of a corridor in Orlando, FL to predict future hourly traffic. The hourly predictions are calculated based on the previous six hours volume seen at the selected intersections. Additional factors that play an important role in traffic fluctuations include peak hours, day of the week, holidays, among others. Multiple machine learning models are applied to the dataset to determine the model with the best performance. Random Forest, XGBoost, and LSTM models show the best performance in predicting hourly traffic volumes.
{"title":"Traffic Volume Prediction with Automated Signal Performance Measures (ATSPM) Data","authors":"Leah Kazenmayer, Gabriela Ford, Jiechao Zhang, Rezaur Rahman, Furkan Cimen, D. Turgut, Samiul Hasan","doi":"10.1109/ISCC55528.2022.9912469","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912469","url":null,"abstract":"Predicting short-term traffic volume is essential to improve transportation systems management and operations (TSM0) and the overall efficiency of traffic networks. The real-time data, collected from Internet of Things (loT) devices, can be used to predict traffic volume. More specifically, the Automated Traffic Signal Performance Measures (ATSPM) data contain high-fidelity traffic data at multiple intersections and can reveal the spatio-temporal patterns of traffic volume for each signal. In this study, we have developed a machine learning-based approach using the data collected from ATSPM sensors of a corridor in Orlando, FL to predict future hourly traffic. The hourly predictions are calculated based on the previous six hours volume seen at the selected intersections. Additional factors that play an important role in traffic fluctuations include peak hours, day of the week, holidays, among others. Multiple machine learning models are applied to the dataset to determine the model with the best performance. Random Forest, XGBoost, and LSTM models show the best performance in predicting hourly traffic volumes.","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":"123234542","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/ISCC55528.2022.9912991
Iakovos Pittaras, N. Fotiou, Christos Karapapas, V. Siris, George C. Polyzos
The proliferation of Internet of Things (IoT) devices and applications that need to cooperate unattended highlights the need for seamless interoperability and intrinsic security. We argue that Distributed Ledger Technologies (DLTs), due to their decentralized nature, transparent operations, immutability, and availability, can enhance the security, reliability, and interoperability of such IoT systems. In this paper, we advance the integration of W3C's Web of Things (WoT) standards with DLTs and smart contracts, introducing smart contracts as “Digital Twins” of (physical) devices, or whole Cyber-Physical subsystems. Namely, we introduce a DLT-based architecture for controlling devices across federated IoT systems, securely, reliably, and with full auditability. The proposed architecture provides mass actuation and service composition with notable security properties, such as full auditability, transparency, and high availability. Specifically, a single request, with multiple action parameters and conditions, can trigger the reliable and secure actuation of a large number of possibly physically dispersed actuators.
{"title":"Secure, Mass Web of Things Actuation Using Smart Contracts-Based Digital Twins","authors":"Iakovos Pittaras, N. Fotiou, Christos Karapapas, V. Siris, George C. Polyzos","doi":"10.1109/ISCC55528.2022.9912991","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912991","url":null,"abstract":"The proliferation of Internet of Things (IoT) devices and applications that need to cooperate unattended highlights the need for seamless interoperability and intrinsic security. We argue that Distributed Ledger Technologies (DLTs), due to their decentralized nature, transparent operations, immutability, and availability, can enhance the security, reliability, and interoperability of such IoT systems. In this paper, we advance the integration of W3C's Web of Things (WoT) standards with DLTs and smart contracts, introducing smart contracts as “Digital Twins” of (physical) devices, or whole Cyber-Physical subsystems. Namely, we introduce a DLT-based architecture for controlling devices across federated IoT systems, securely, reliably, and with full auditability. The proposed architecture provides mass actuation and service composition with notable security properties, such as full auditability, transparency, and high availability. Specifically, a single request, with multiple action parameters and conditions, can trigger the reliable and secure actuation of a large number of possibly physically dispersed actuators.","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":"115309370","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/ISCC55528.2022.9912959
P. Kokkinos
The emergence of Internet of Things (IoT) and the anticipated 5G/6G applications lead to several challenges regarding the rapid and the efficient processing of massive amounts of data, which are generated, transferred and processed within a city boundaries. Towards this end, the convergence of computing, storage and networking infrastructures operating in a megacity environment is pivotal. In this work, we present several related research innovations regarding the service of user and application demands, the orchestration of cloud and edge resources and the realization of edge infrastructures.
{"title":"Towards the Realization of Converged Cloud, Edge and Networking Infrastructures in Smart MegaCities","authors":"P. Kokkinos","doi":"10.1109/ISCC55528.2022.9912959","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912959","url":null,"abstract":"The emergence of Internet of Things (IoT) and the anticipated 5G/6G applications lead to several challenges regarding the rapid and the efficient processing of massive amounts of data, which are generated, transferred and processed within a city boundaries. Towards this end, the convergence of computing, storage and networking infrastructures operating in a megacity environment is pivotal. In this work, we present several related research innovations regarding the service of user and application demands, the orchestration of cloud and edge resources and the realization of edge infrastructures.","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":"123259494","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}
With the development of 360-degree video and HTTP adaptive streaming (HAS), tile-based adaptive 360-degree video streaming has become a promising paradigm for reducing the bandwidth consumption of delivering the panoramic video content. However, there are two main challenges for the adaptive 360-degree video streaming, accurate long-term prediction of the future field of view (Fo V) and optimal adaptive bitrate (ABR) transmission strategy. In this paper, we propose an attention-based multi-user Fo V joint prediction approach to improve the accuracy, establishing a probability model of watching video tiles for users and applying Long Short-Term Memory (LSTM) network and DBSCAN clustering method. Furthermore, we present an adaptive 360-degree video streaming approach based on deep reinforcement learning (DRL), using A3C algorithm to optimize the QoE. The real-world trace-driven experiments demonstrate that our approach achieves about 8 % gains on user Fo V prediction precision and an increase at least 20 % on user QoE compared with the benchmarks.
{"title":"Deep Reinforcement Learning Based Adaptive 360-degree Video Streaming with Field of View Joint Prediction","authors":"Yuanhong Zhang, Zhiwen Wang, Junquan Liu, Haipeng Du, Qinghua Zheng, Weizhan Zhang","doi":"10.1109/ISCC55528.2022.9913007","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9913007","url":null,"abstract":"With the development of 360-degree video and HTTP adaptive streaming (HAS), tile-based adaptive 360-degree video streaming has become a promising paradigm for reducing the bandwidth consumption of delivering the panoramic video content. However, there are two main challenges for the adaptive 360-degree video streaming, accurate long-term prediction of the future field of view (Fo V) and optimal adaptive bitrate (ABR) transmission strategy. In this paper, we propose an attention-based multi-user Fo V joint prediction approach to improve the accuracy, establishing a probability model of watching video tiles for users and applying Long Short-Term Memory (LSTM) network and DBSCAN clustering method. Furthermore, we present an adaptive 360-degree video streaming approach based on deep reinforcement learning (DRL), using A3C algorithm to optimize the QoE. The real-world trace-driven experiments demonstrate that our approach achieves about 8 % gains on user Fo V prediction precision and an increase at least 20 % on user QoE compared with the benchmarks.","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":"124671229","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/ISCC55528.2022.9912930
Mingzhu Qiang, Changyan Yi, Juan Li, Kun Zhu, Jun Cai
In this paper, a multi-layer edge computing frame-work for the virtual machine (VM) placement and computation offloading in industrial Internet of Things (IIoT) is proposed. Unlike most existing works, we focus on addressing the temporal dependency among tasks in an IIoT task flow, and consider that there is a stringent requirement on its completion time (including the transmission time, computation time and waiting time). For striking a balance between the system completion time and the energy consumption while satisfying the storage capacity of edge servers (ESs), completion deadline of time-sequential task flows, and placement requirements of VMs, we design a many-to-one matching game (MGVDA) to jointly determine the optimal VM placement and task offloading decisions. Finally, we prove that the resulted matching game solution is effective and stable. Simulation results examine the efficiency of the proposed MGVDA and show its superiority over the counterparts.
{"title":"Joint Task Offloading and VM Placement for Edge Computing with Time-Sequential IIoT Applications","authors":"Mingzhu Qiang, Changyan Yi, Juan Li, Kun Zhu, Jun Cai","doi":"10.1109/ISCC55528.2022.9912930","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912930","url":null,"abstract":"In this paper, a multi-layer edge computing frame-work for the virtual machine (VM) placement and computation offloading in industrial Internet of Things (IIoT) is proposed. Unlike most existing works, we focus on addressing the temporal dependency among tasks in an IIoT task flow, and consider that there is a stringent requirement on its completion time (including the transmission time, computation time and waiting time). For striking a balance between the system completion time and the energy consumption while satisfying the storage capacity of edge servers (ESs), completion deadline of time-sequential task flows, and placement requirements of VMs, we design a many-to-one matching game (MGVDA) to jointly determine the optimal VM placement and task offloading decisions. Finally, we prove that the resulted matching game solution is effective and stable. Simulation results examine the efficiency of the proposed MGVDA and show its superiority over the counterparts.","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":"122750142","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/ISCC55528.2022.9912936
Ioanna Angeliki Kapetanidou, Stavros Malagaris, V. Tsaoussidis
Named Data Networking (NDN) has emerged as a promising Future Internet architecture. NDN provisions security by design and guarantees that data packets are immutable and authentic. Nevertheless, its inherent in-network caching feature has opened the door to new types of security attacks. One such critical security issue in NDN is content poisoning attacks. In content poisoning, the attacker aims at injecting poisonous (i.e., fake or invalid) content in the network caches. In this paper, we propose a reputation-based content poisoning mitigation model, which assists both the access and the core network nodes in identifying the sources from which poisonous content is originated, and subsequently, limiting the Interest flow towards those notorious sources as well as in avoiding caching poisonous content.
{"title":"Avoiding Notorious Content Sources: A Content-Poisoning Attack Mitigation Approach","authors":"Ioanna Angeliki Kapetanidou, Stavros Malagaris, V. Tsaoussidis","doi":"10.1109/ISCC55528.2022.9912936","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912936","url":null,"abstract":"Named Data Networking (NDN) has emerged as a promising Future Internet architecture. NDN provisions security by design and guarantees that data packets are immutable and authentic. Nevertheless, its inherent in-network caching feature has opened the door to new types of security attacks. One such critical security issue in NDN is content poisoning attacks. In content poisoning, the attacker aims at injecting poisonous (i.e., fake or invalid) content in the network caches. In this paper, we propose a reputation-based content poisoning mitigation model, which assists both the access and the core network nodes in identifying the sources from which poisonous content is originated, and subsequently, limiting the Interest flow towards those notorious sources as well as in avoiding caching poisonous content.","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":"116234767","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/ISCC55528.2022.9912864
Guozhi Lin, Jingguo Ge, Yulei Wu, Hui Li, Liangxiong Li
The Digital Twin Network (DTN) is a key enabling technology for efficient and intelligent network management in modern communication networks. Learning dynamic net-work behaviors at the flow granularity is a core element for realizing DTN with accurate network modelling. However, it is challenging due to the complexity of network architectures and the proliferation of emerging network applications. In this paper, we devise a Packet-Action Sequence Model to represent all possible packets behaviors in a unified way. Besides, we propose a novel and effective algorithm to assess whether the behavior pattern is time dependent or independent by using the temporal characteristics of packets in a network flow, so as to learn the key factors of packets that contribute to network behaviors. Based on two typical scenarios, i.e., packet caching and routing, the experimental results verify that the proposed algorithm can identify network behavior patterns and learn key factors affecting the behaviors with over 99 % accuracy.
{"title":"Digital Twin Networks: Learning Dynamic Network Behaviors from Network Flows","authors":"Guozhi Lin, Jingguo Ge, Yulei Wu, Hui Li, Liangxiong Li","doi":"10.1109/ISCC55528.2022.9912864","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912864","url":null,"abstract":"The Digital Twin Network (DTN) is a key enabling technology for efficient and intelligent network management in modern communication networks. Learning dynamic net-work behaviors at the flow granularity is a core element for realizing DTN with accurate network modelling. However, it is challenging due to the complexity of network architectures and the proliferation of emerging network applications. In this paper, we devise a Packet-Action Sequence Model to represent all possible packets behaviors in a unified way. Besides, we propose a novel and effective algorithm to assess whether the behavior pattern is time dependent or independent by using the temporal characteristics of packets in a network flow, so as to learn the key factors of packets that contribute to network behaviors. Based on two typical scenarios, i.e., packet caching and routing, the experimental results verify that the proposed algorithm can identify network behavior patterns and learn key factors affecting the behaviors with over 99 % 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":"123786921","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/ISCC55528.2022.9912964
Nan Xiang, Xuebin Ma
Data anonymization is one of the most important directions in privacy-preserving. However, research shows that simple anonymization of data does not protect privacy. To solve this problem, we present a novel and effective algorithm named tree-based K-degree anonymity (TKDA). We devise a new anonymity sequence generation method to reduce the information loss for social graphs. Then, the dynamic anonymization process is implemented by a depth-first search (DFS) traversal algorithm. Finally, the graph modification algorithm based on the anonymous sequence can keep the original graph structure stable. Average Path Length (APL), Average Clustering Coefficient (ACC), and Transitivity (T) are employed to evaluate the method. Experimental results on several datasets show that TKDA is closer to the values of the original graphs on the correlated three experimental metrics, which indicates that TKDA portrays the real data in more detail and improves the utility of the released data.
{"title":"TKDA: An Improved Method for K-degree Anonymity in Social Graphs","authors":"Nan Xiang, Xuebin Ma","doi":"10.1109/ISCC55528.2022.9912964","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912964","url":null,"abstract":"Data anonymization is one of the most important directions in privacy-preserving. However, research shows that simple anonymization of data does not protect privacy. To solve this problem, we present a novel and effective algorithm named tree-based K-degree anonymity (TKDA). We devise a new anonymity sequence generation method to reduce the information loss for social graphs. Then, the dynamic anonymization process is implemented by a depth-first search (DFS) traversal algorithm. Finally, the graph modification algorithm based on the anonymous sequence can keep the original graph structure stable. Average Path Length (APL), Average Clustering Coefficient (ACC), and Transitivity (T) are employed to evaluate the method. Experimental results on several datasets show that TKDA is closer to the values of the original graphs on the correlated three experimental metrics, which indicates that TKDA portrays the real data in more detail and improves the utility of the released data.","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":"128399767","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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