Information-Centric Networking (ICN) has gained attention as one of the next-generation internet architectures that focuses on the data being transmitted rather than the hosts transmitting it. Due to the differences between ICN and TCP/IP networks, it is not possible to evaluate the performance of ICN using network simulators designed for TCP/IP. A number of studies have been conducted to develop ICN network simulators. However, further acceleration of ICN network simulators is expected to enable large-scale ICN network performance evaluation. In this paper, we analyze the performance bottleneck of the flow-level ICN simulator called FICNSIM (Fluid-based ICNSIMulator) by profiling its performance using the Julia language source code. Specifically, we identify the processing that is causing the performance bottleneck of FICNSIM and investigate the scalability of FICNSIM with respect to network scale.
{"title":"Study on Performance Bottleneck of Flow-Level Information-Centric Network Simulator","authors":"Shota Inoue, Han Nay Aung, Keita Goto, Soma Yamamoto, Hiroyuki Ohsaki","doi":"10.1109/COMPSAC57700.2023.10368592","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.10368592","url":null,"abstract":"Information-Centric Networking (ICN) has gained attention as one of the next-generation internet architectures that focuses on the data being transmitted rather than the hosts transmitting it. Due to the differences between ICN and TCP/IP networks, it is not possible to evaluate the performance of ICN using network simulators designed for TCP/IP. A number of studies have been conducted to develop ICN network simulators. However, further acceleration of ICN network simulators is expected to enable large-scale ICN network performance evaluation. In this paper, we analyze the performance bottleneck of the flow-level ICN simulator called FICNSIM (Fluid-based ICNSIMulator) by profiling its performance using the Julia language source code. Specifically, we identify the processing that is causing the performance bottleneck of FICNSIM and investigate the scalability of FICNSIM with respect to network scale.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"241 1","pages":"1908-1911"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139368437","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 : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00100
Xuebin Ma, Nan Xiang, Yulan Gao
K-degree anonymity is one of the main techniques for data privacy and has gained attention in academia, industry, and government. Many social network data publishing algorithms based on K-anonymity techniques have been proposed, but most studies focus on static social networks. Compared to static social networks, dynamic social networks suffer from problems such as higher information loss and lower data utility. To address the existing problem of dynamic social networks, we propose a K-degree anonymity dynamic data publishing algorithm based on reinforcement learning. The algorithm ends with two phases: anonymization sequence and graph modification. In the anonymous sequence phase, this paper combines the idea of reinforcement learning and the characteristics of dynamic data change to build a reinforcement learning model for anonymous sequences. In this way, an ideal anonymous sequence can be created. We also propose a new strategy for graph modification, which selects edges according to degree centrality to generate anonymous graphs. Finally, experiments on real datasets show the effectiveness of our algorithm.
{"title":"RL-KDA: A K-degree Anonymity Algorithm Based on Reinforcement Learning","authors":"Xuebin Ma, Nan Xiang, Yulan Gao","doi":"10.1109/COMPSAC57700.2023.00100","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00100","url":null,"abstract":"K-degree anonymity is one of the main techniques for data privacy and has gained attention in academia, industry, and government. Many social network data publishing algorithms based on K-anonymity techniques have been proposed, but most studies focus on static social networks. Compared to static social networks, dynamic social networks suffer from problems such as higher information loss and lower data utility. To address the existing problem of dynamic social networks, we propose a K-degree anonymity dynamic data publishing algorithm based on reinforcement learning. The algorithm ends with two phases: anonymization sequence and graph modification. In the anonymous sequence phase, this paper combines the idea of reinforcement learning and the characteristics of dynamic data change to build a reinforcement learning model for anonymous sequences. In this way, an ideal anonymous sequence can be created. We also propose a new strategy for graph modification, which selects edges according to degree centrality to generate anonymous graphs. Finally, experiments on real datasets show the effectiveness of our algorithm.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114966031","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 : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00182
Tong Li, Lu Han
Explainability has recently been recognized as an increasingly important quality requirement for machine learning systems. Various methods have been proposed by machine learning researchers to explain the results of machine learning techniques. However, analyzing and operationalizing such explainability requirements is knowledge-intensive and time-consuming. This paper proposes an explainability requirements analysis framework using contextual goal models, aiming at systematically and automatically deriving appropriate explainability methods. Specifically, we comprehensively survey and analyze existing explainability methods, associating them with explainability requirements and emphasizing the context for applying them. In such a way, we can automatically operationalize explainability requirements into concrete explainability methods. We conducted a case study with ten participants to evaluate our proposal. The results illustrate the framework’s usability for satisfying the explainability requirements of machine learning systems.
{"title":"Dealing with Explainability Requirements for Machine Learning Systems","authors":"Tong Li, Lu Han","doi":"10.1109/COMPSAC57700.2023.00182","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00182","url":null,"abstract":"Explainability has recently been recognized as an increasingly important quality requirement for machine learning systems. Various methods have been proposed by machine learning researchers to explain the results of machine learning techniques. However, analyzing and operationalizing such explainability requirements is knowledge-intensive and time-consuming. This paper proposes an explainability requirements analysis framework using contextual goal models, aiming at systematically and automatically deriving appropriate explainability methods. Specifically, we comprehensively survey and analyze existing explainability methods, associating them with explainability requirements and emphasizing the context for applying them. In such a way, we can automatically operationalize explainability requirements into concrete explainability methods. We conducted a case study with ten participants to evaluate our proposal. The results illustrate the framework’s usability for satisfying the explainability requirements of machine learning systems.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115291860","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 : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00071
Xiang-Li Lu, Hwai-Jung Hsu, William Cheng-Chung Chu
Public bicycle systems (PBSs) that connect end users’ houses to public mass transportation are typically viewed as the "last-mile" of public transportation. Because of the limited capacity of stations in a PBS, the PBS operator must dispatch bicycles between stations to ensure that there are always bicycles/spaces available for bicycle borrowing/returning. However, because the variances in flows among stations are large, bicycle dispatch is difficult without a precise flow forecasting approach. In this paper, we propose an innovative approach to forecast bicycle flow on the basis of a graph neural network (GNN). Instead of processing the temporal information using RNN, or 1D-CNN, our approach integrates both spatial and temporal information into graphs, and analyzes them using graph convolution. Our approach works well on NYCitibike open dataset in terms of prediction accuracy. From the experiment, our approach shows it capability in accurate forecasting of peak flows and self-adjustment while perceiving abnormal flows caused by sporadic situations.
{"title":"Public Bicycle Flow Forecasting using Spatial and Temporal Graph Neural Network","authors":"Xiang-Li Lu, Hwai-Jung Hsu, William Cheng-Chung Chu","doi":"10.1109/COMPSAC57700.2023.00071","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00071","url":null,"abstract":"Public bicycle systems (PBSs) that connect end users’ houses to public mass transportation are typically viewed as the \"last-mile\" of public transportation. Because of the limited capacity of stations in a PBS, the PBS operator must dispatch bicycles between stations to ensure that there are always bicycles/spaces available for bicycle borrowing/returning. However, because the variances in flows among stations are large, bicycle dispatch is difficult without a precise flow forecasting approach. In this paper, we propose an innovative approach to forecast bicycle flow on the basis of a graph neural network (GNN). Instead of processing the temporal information using RNN, or 1D-CNN, our approach integrates both spatial and temporal information into graphs, and analyzes them using graph convolution. Our approach works well on NYCitibike open dataset in terms of prediction accuracy. From the experiment, our approach shows it capability in accurate forecasting of peak flows and self-adjustment while perceiving abnormal flows caused by sporadic situations.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123099540","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 : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00152
A. Farooq, Zac Taylor, Kyle Ruona, Thomas Moyer
Programmable Logic Controllers (PLCs) are an integral component for managing automation processes of smart buildings. PLCs use protocols which make these control systems vulnerable to many common attacks due to which it is possible to create conflicts on certain devices of smart buildings thereby disrupting functionality. In this paper, we propose DEFEASIBLE-PROV, a system for resolving conflicts in the system by detecting the conflict creating sensors and conflict impacted actuators. Our tool is capable of blocking conflict creating rules in the system. Our evaluation results show that our proposed methodology contributes significantly to conflict resolution in the system.
{"title":"Defeasible-PROV: Conflict Resolution in Smart Building Devices","authors":"A. Farooq, Zac Taylor, Kyle Ruona, Thomas Moyer","doi":"10.1109/COMPSAC57700.2023.00152","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00152","url":null,"abstract":"Programmable Logic Controllers (PLCs) are an integral component for managing automation processes of smart buildings. PLCs use protocols which make these control systems vulnerable to many common attacks due to which it is possible to create conflicts on certain devices of smart buildings thereby disrupting functionality. In this paper, we propose DEFEASIBLE-PROV, a system for resolving conflicts in the system by detecting the conflict creating sensors and conflict impacted actuators. Our tool is capable of blocking conflict creating rules in the system. Our evaluation results show that our proposed methodology contributes significantly to conflict resolution in the system.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"17 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120844373","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 : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00036
Abdulaziz Alajaji, Walter Gerych, kar 2402565399 ku, Luke Buquicchio, E. Agu, E. Rundensteiner
Human Context Recognition (HCR) from smartphone sensor data is a crucial task for Context-Aware (CA) systems, such as those targeting the healthcare and security domains. HCR models deployed in the wild are susceptible to adversarial attacks, wherein an adversary perturbs input sensor values to cause malicious mis-classifications. In this study, we demonstrate evasion attacks that can be perpetuated during model inference, particularly input perturbations that are adversarially calibrated to fool classifiers. In contrast to white-box methods that require impractical levels of system access, black-box evasion attacks merely require the ability to query the model with arbitrary inputs. Specifically, we generate adversarial perturbations using only class confidence scores, as in the Zoo attack, or only class decisions, as in the HopSkipJump (HSJ) attack that correspond with plausible scenarios of possible adversarial attacks. We empirically demonstrate that sophisticated adversarial evasion attacks can significantly impair the accuracy of HCR models, resulting in a performance drop of up to 60% in f1-score. We also propose RobustHCR, an innovative framework for demonstrating and defending against black box evasion threats using a provable defense based on a duality-based network. RobustHCR is able to make reliable predictions regardless of whether its input is under attack or not, effectively mitigating the potential negative impacts caused by adversarial attacks. Rigorous evaluation on both scripted and in-the-wild smartphone HCR datasets demonstrates that RobustHCR can significantly improve the HCR model’s robustness and protect it from possible evasion attacks while maintaining acceptable performance on "clean" inputs. In particular, an HCR model with integrated RobustHCR defenses experienced an f1-score reduction of about 3% as opposed to a reduction of over 50% for an HCR model without a defense.
{"title":"Adversarial Human Context Recognition: Evasion Attacks and Defenses","authors":"Abdulaziz Alajaji, Walter Gerych, kar 2402565399 ku, Luke Buquicchio, E. Agu, E. Rundensteiner","doi":"10.1109/COMPSAC57700.2023.00036","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00036","url":null,"abstract":"Human Context Recognition (HCR) from smartphone sensor data is a crucial task for Context-Aware (CA) systems, such as those targeting the healthcare and security domains. HCR models deployed in the wild are susceptible to adversarial attacks, wherein an adversary perturbs input sensor values to cause malicious mis-classifications. In this study, we demonstrate evasion attacks that can be perpetuated during model inference, particularly input perturbations that are adversarially calibrated to fool classifiers. In contrast to white-box methods that require impractical levels of system access, black-box evasion attacks merely require the ability to query the model with arbitrary inputs. Specifically, we generate adversarial perturbations using only class confidence scores, as in the Zoo attack, or only class decisions, as in the HopSkipJump (HSJ) attack that correspond with plausible scenarios of possible adversarial attacks. We empirically demonstrate that sophisticated adversarial evasion attacks can significantly impair the accuracy of HCR models, resulting in a performance drop of up to 60% in f1-score. We also propose RobustHCR, an innovative framework for demonstrating and defending against black box evasion threats using a provable defense based on a duality-based network. RobustHCR is able to make reliable predictions regardless of whether its input is under attack or not, effectively mitigating the potential negative impacts caused by adversarial attacks. Rigorous evaluation on both scripted and in-the-wild smartphone HCR datasets demonstrates that RobustHCR can significantly improve the HCR model’s robustness and protect it from possible evasion attacks while maintaining acceptable performance on \"clean\" inputs. In particular, an HCR model with integrated RobustHCR defenses experienced an f1-score reduction of about 3% as opposed to a reduction of over 50% for an HCR model without a defense.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127231643","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 : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00158
A. Cuzzocrea, Q. E. A. Ratul, Islam Belmerabet, Edoardo Serra
In this paper, we propose a general methodology for estimating the degree of the attribution methods precision and generality in machine learning interpretability. Additionally, we propose a technique to measure the attribution consistency between two attribution methods. In our experiments, we focus on the two well-known model agnostic attribution methods, SHAP and LIME, then we evaluate them on two real applications in the attack detection field. Our proposed methodology highlights the fact that both LIME and SHAP are lacking precision, generality, and consistency. Therefore, more inspection is needed in the attribution research field.
{"title":"An AI Framework for Modelling and Evaluating Attribution Methods in Enhanced Machine Learning Interpretability","authors":"A. Cuzzocrea, Q. E. A. Ratul, Islam Belmerabet, Edoardo Serra","doi":"10.1109/COMPSAC57700.2023.00158","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00158","url":null,"abstract":"In this paper, we propose a general methodology for estimating the degree of the attribution methods precision and generality in machine learning interpretability. Additionally, we propose a technique to measure the attribution consistency between two attribution methods. In our experiments, we focus on the two well-known model agnostic attribution methods, SHAP and LIME, then we evaluate them on two real applications in the attack detection field. Our proposed methodology highlights the fact that both LIME and SHAP are lacking precision, generality, and consistency. Therefore, more inspection is needed in the attribution research field.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"780 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123282669","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 : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00098
Keisuke Hamajima, Daisuke Kotani, Y. Okabe
Dynamic analysis is one of the methods to analyze malware. However, if the file to be analyzed contains confidential information, disclosing it to the analyst outside the organization is undesirable. Previous works proposed classifying malware while preserving privacy or outsourcing dynamic analysis, but it is challenging to outsource dynamic analysis without disclosing file contents. The proposed method builds the Local Environment for users and the Remote Environment for analysts outside the organization. We proposed partial outsourcing, which opens a file in the Local Environment, reproduces its behavior in the Remote Environment, and conducts dynamic analysis based on this information. The Local Environment hooks an API call and retrieves information on the function name and arguments. Then, the Local Environment sends the information to the Remote Environment to reproduce file behavior. Our method could reproduce most operations on files and registries but could not reproduce some operations on files.
{"title":"Partial Outsourcing of Malware Dynamic Analysis Without Disclosing File Contents","authors":"Keisuke Hamajima, Daisuke Kotani, Y. Okabe","doi":"10.1109/COMPSAC57700.2023.00098","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00098","url":null,"abstract":"Dynamic analysis is one of the methods to analyze malware. However, if the file to be analyzed contains confidential information, disclosing it to the analyst outside the organization is undesirable. Previous works proposed classifying malware while preserving privacy or outsourcing dynamic analysis, but it is challenging to outsource dynamic analysis without disclosing file contents. The proposed method builds the Local Environment for users and the Remote Environment for analysts outside the organization. We proposed partial outsourcing, which opens a file in the Local Environment, reproduces its behavior in the Remote Environment, and conducts dynamic analysis based on this information. The Local Environment hooks an API call and retrieves information on the function name and arguments. Then, the Local Environment sends the information to the Remote Environment to reproduce file behavior. Our method could reproduce most operations on files and registries but could not reproduce some operations on files.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116155439","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 : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00172
C. Cérin, D. Trystram, Tarek Menouer
EcoIndex has been proposed to evaluate the absolute environmental performance of a given URL using a score ranging from 0 to 100 (higher is better). In this article, we revisit the calculation method of the EcoIndex metric through low-cost Machine Learning (ML) approaches. Our research aims to extend the initial idea of analytical computation, i.e., a relation (equation) between three variables, in the direction of algorithmic Machine Learning (ML) computations, allowing to treat large numbers of data, which is not the case with the current computation. For a URL, our new calculation methods mimic the initial metric and return an environmental performance score but make fewer assumptions than the initial method. We develop several ML ways, either using learning techniques (Locality Sensitive Hashing, K Nearest Neighbor) or matrix computation constitutes the paper’s first contribution. We use standard methods to keep the solutions simple and understood by the public. The second contribution corresponds to a discussion on our implementations, available on a GitHub repository. As major findings or trends of our study, we also discuss the limits of the past and new approaches in a search for new metrics regarding the environmental performance of HTTP requests admissible by the most significant number of people. Our work refers to the uses of digital technology. Therefore, explaining the environmental footprint measures with few words seems important if we want to move towards greater digital sobriety. Otherwise, we run the risk of not being followed by civil society.
{"title":"The EcoIndex metric, reviewed from the perspective of Data Science techniques","authors":"C. Cérin, D. Trystram, Tarek Menouer","doi":"10.1109/COMPSAC57700.2023.00172","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00172","url":null,"abstract":"EcoIndex has been proposed to evaluate the absolute environmental performance of a given URL using a score ranging from 0 to 100 (higher is better). In this article, we revisit the calculation method of the EcoIndex metric through low-cost Machine Learning (ML) approaches. Our research aims to extend the initial idea of analytical computation, i.e., a relation (equation) between three variables, in the direction of algorithmic Machine Learning (ML) computations, allowing to treat large numbers of data, which is not the case with the current computation. For a URL, our new calculation methods mimic the initial metric and return an environmental performance score but make fewer assumptions than the initial method. We develop several ML ways, either using learning techniques (Locality Sensitive Hashing, K Nearest Neighbor) or matrix computation constitutes the paper’s first contribution. We use standard methods to keep the solutions simple and understood by the public. The second contribution corresponds to a discussion on our implementations, available on a GitHub repository. As major findings or trends of our study, we also discuss the limits of the past and new approaches in a search for new metrics regarding the environmental performance of HTTP requests admissible by the most significant number of people. Our work refers to the uses of digital technology. Therefore, explaining the environmental footprint measures with few words seems important if we want to move towards greater digital sobriety. Otherwise, we run the risk of not being followed by civil society.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128459135","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 : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00056
Cui Zhang, Hui Yang, Jun Li, Yunhua He, J. Zhang, Q. Yao, Chao Li
The increase in IoT(Internet of Things) computing demands has brought more and more attention to the polynomial outsourcing computing. As a distributed computing method, IoT polynomial outsourcing computing based on cross-chain can enable IoT data on other blockchains to participate in computing by outsourcing polynomials. However, the trust isolation between multiple blockchains will bring challenges to the efficient verification of polynomial cross-chain outsourced calculations. In this paper, we first design a multi-chain outsourcing computing model to improve the efficiency of IoT cross-chain computing by outsourcing polynomials to blockchains that store related data. Then, an efficient and verifiable polynomial cross-chain outsourcing calculation scheme is proposed. In this scheme, we design a polynomial commitment generation algorithm, a witness generation algorithm and a cross-chain verification algorithm by combining commitment and witness mechanisms. These algorithms work together to efficiently verify the correctness and integrity of the calculation results of the outsourced polynomials. Security analysis and experimental results show that the scheme is feasible in practice.
{"title":"An Efficient and Verifiable Polynomial Cross-chain Outsourcing Calculation Scheme for IoT","authors":"Cui Zhang, Hui Yang, Jun Li, Yunhua He, J. Zhang, Q. Yao, Chao Li","doi":"10.1109/COMPSAC57700.2023.00056","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00056","url":null,"abstract":"The increase in IoT(Internet of Things) computing demands has brought more and more attention to the polynomial outsourcing computing. As a distributed computing method, IoT polynomial outsourcing computing based on cross-chain can enable IoT data on other blockchains to participate in computing by outsourcing polynomials. However, the trust isolation between multiple blockchains will bring challenges to the efficient verification of polynomial cross-chain outsourced calculations. In this paper, we first design a multi-chain outsourcing computing model to improve the efficiency of IoT cross-chain computing by outsourcing polynomials to blockchains that store related data. Then, an efficient and verifiable polynomial cross-chain outsourcing calculation scheme is proposed. In this scheme, we design a polynomial commitment generation algorithm, a witness generation algorithm and a cross-chain verification algorithm by combining commitment and witness mechanisms. These algorithms work together to efficiently verify the correctness and integrity of the calculation results of the outsourced polynomials. Security analysis and experimental results show that the scheme is feasible in practice.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128355314","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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