2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)最新文献

Study on Performance Bottleneck of Flow-Level Information-Centric Network Simulator 流量级信息中心网络模拟器的性能瓶颈研究

2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2023-06-26 DOI: 10.1109/COMPSAC57700.2023.10368592

Shota Inoue, Han Nay Aung, Keita Goto, Soma Yamamoto, Hiroyuki Ohsaki

引用次数: 0

RL-KDA: A K-degree Anonymity Algorithm Based on Reinforcement Learning 基于强化学习的k度匿名算法RL-KDA

2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2023-06-01 DOI: 10.1109/COMPSAC57700.2023.00100

Xuebin Ma, Nan Xiang, Yulan Gao

{"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}

引用次数: 0

Dealing with Explainability Requirements for Machine Learning Systems 处理机器学习系统的可解释性要求

2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2023-06-01 DOI: 10.1109/COMPSAC57700.2023.00182

Tong Li, Lu Han

引用次数: 0

Public Bicycle Flow Forecasting using Spatial and Temporal Graph Neural Network 基于时空图神经网络的公共自行车流量预测

2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2023-06-01 DOI: 10.1109/COMPSAC57700.2023.00071

Xiang-Li Lu, Hwai-Jung Hsu, William Cheng-Chung Chu

引用次数: 0

Defeasible-PROV: Conflict Resolution in Smart Building Devices 可失败的证明:智能建筑设备中的冲突解决

2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2023-06-01 DOI: 10.1109/COMPSAC57700.2023.00152

A. Farooq, Zac Taylor, Kyle Ruona, Thomas Moyer

引用次数: 0

Adversarial Human Context Recognition: Evasion Attacks and Defenses 对抗性人类语境识别:逃避攻击和防御

2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2023-06-01 DOI: 10.1109/COMPSAC57700.2023.00036

Abdulaziz Alajaji, Walter Gerych, kar 2402565399 ku, Luke Buquicchio, E. Agu, E. Rundensteiner

{"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}

引用次数: 0

An AI Framework for Modelling and Evaluating Attribution Methods in Enhanced Machine Learning Interpretability 在增强机器学习可解释性中建模和评估归因方法的AI框架

2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2023-06-01 DOI: 10.1109/COMPSAC57700.2023.00158

A. Cuzzocrea, Q. E. A. Ratul, Islam Belmerabet, Edoardo Serra

引用次数: 0

Partial Outsourcing of Malware Dynamic Analysis Without Disclosing File Contents 不公开文件内容的恶意软件动态分析部分外包

2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2023-06-01 DOI: 10.1109/COMPSAC57700.2023.00098

Keisuke Hamajima, Daisuke Kotani, Y. Okabe

引用次数: 0

The EcoIndex metric, reviewed from the perspective of Data Science techniques EcoIndex指标，从数据科学技术的角度进行回顾

2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2023-06-01 DOI: 10.1109/COMPSAC57700.2023.00172

C. Cérin, D. Trystram, Tarek Menouer

{"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}

引用次数: 0

An Efficient and Verifiable Polynomial Cross-chain Outsourcing Calculation Scheme for IoT 一种高效且可验证的多项式物联网跨链外包计算方案

2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2023-06-01 DOI: 10.1109/COMPSAC57700.2023.00056

Cui Zhang, Hui Yang, Jun Li, Yunhua He, J. Zhang, Q. Yao, Chao Li

{"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}

引用次数: 0