Polarized communities search aims at locating query-dependent communities, in which mostly nodes within each community form intensive positive connections, while mostly nodes across two communities are connected by negative links. Current approaches towards polarized communities search typically model the network topology, while the key factor of node, i.e., the attributes, are largely ignored. Existing studies have shown that community formation is strongly influenced by node attributes and the formation of communities are determined by both network topology and node attributes simultaneously. However, it is nontrivial to incorporate node attributes for polarized communities search. Firstly, it is hard to handle the heterogeneous information from node attributes. Secondly, it is difficult to model the complex relations between network topology and node attributes in identifying polarized communities. To address the above challenges, we propose a novel method Co-guided Random Walk in Attributed signed networks (CoRWA) for polarized communities search by equipping with reasonable attribute setting. For the first challenge, we devise an attribute-based signed network to model the auxiliary relation between nodes and a weight assignment mechanism is designed to measure the reliability of the edges in the signed network. As to the second challenge, a co-guided random walk scheme in two signed networks is designed to explicitly model the relations between topology-based signed network and attribute-based signed network so as to enhance the search result of each other. Finally, we can identify polarized communities by a well-designed Rayleigh quotient in the signed network. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed CoRWA. Further analysis reveals the significance of node attributes for polarized communities search.
{"title":"Polarized Communities Search via Co-guided Random Walk in Attributed Signed Networks","authors":"Fanyi Yang, Huifang Ma, Cairui Yan, Zhixin Li, Liang Chang","doi":"10.1145/3613449","DOIUrl":"https://doi.org/10.1145/3613449","url":null,"abstract":"Polarized communities search aims at locating query-dependent communities, in which mostly nodes within each community form intensive positive connections, while mostly nodes across two communities are connected by negative links. Current approaches towards polarized communities search typically model the network topology, while the key factor of node, i.e., the attributes, are largely ignored. Existing studies have shown that community formation is strongly influenced by node attributes and the formation of communities are determined by both network topology and node attributes simultaneously. However, it is nontrivial to incorporate node attributes for polarized communities search. Firstly, it is hard to handle the heterogeneous information from node attributes. Secondly, it is difficult to model the complex relations between network topology and node attributes in identifying polarized communities. To address the above challenges, we propose a novel method Co-guided Random Walk in Attributed signed networks (CoRWA) for polarized communities search by equipping with reasonable attribute setting. For the first challenge, we devise an attribute-based signed network to model the auxiliary relation between nodes and a weight assignment mechanism is designed to measure the reliability of the edges in the signed network. As to the second challenge, a co-guided random walk scheme in two signed networks is designed to explicitly model the relations between topology-based signed network and attribute-based signed network so as to enhance the search result of each other. Finally, we can identify polarized communities by a well-designed Rayleigh quotient in the signed network. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed CoRWA. Further analysis reveals the significance of node attributes for polarized communities search.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135252728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nowadays, people of all ages are increasingly using Web platforms for social interaction. Consequently, many tasks are being transferred over social networks, like advertisements, political communications, and so on, yielding vast volumes of data disseminated over the network. However, this raises several concerns regarding the truthfulness of such data and the accounts generating them. Malicious users often manipulate data in order to gain profit. For example, malicious users often create fake accounts and fake followers to increase their popularity and attract more sponsors, followers, and so on, potentially producing several negative implications that impact the whole society. To deal with these issues it is necessary to increase the capability to properly identify fake accounts and followers. By exploiting automatically extracted data correlations characterizing meaningful patterns of malicious accounts, in this paper, we propose a new feature engineering strategy to augment the social network account dataset with additional features, aiming to enhance the capability of existing machine learning strategies to discriminate fake accounts. Experimental results produced through several machine learning models on account datasets of both the Twitter and the Instagram platforms highlight the effectiveness of the proposed approach towards the automatic discrimination of fake accounts. The choice of Twitter is mainly due to its strict privacy laws, and because its the only social network platform making data of their accounts publicly available.
{"title":"Malicious Account Identification in Social Network Platforms","authors":"Loredana Caruccio, Gaetano Cimino, Stefano Cirillo, Domenico Desiato, Giuseppe Polese, Genoveffa Tortora","doi":"10.1145/3625097","DOIUrl":"https://doi.org/10.1145/3625097","url":null,"abstract":"Nowadays, people of all ages are increasingly using Web platforms for social interaction. Consequently, many tasks are being transferred over social networks, like advertisements, political communications, and so on, yielding vast volumes of data disseminated over the network. However, this raises several concerns regarding the truthfulness of such data and the accounts generating them. Malicious users often manipulate data in order to gain profit. For example, malicious users often create fake accounts and fake followers to increase their popularity and attract more sponsors, followers, and so on, potentially producing several negative implications that impact the whole society. To deal with these issues it is necessary to increase the capability to properly identify fake accounts and followers. By exploiting automatically extracted data correlations characterizing meaningful patterns of malicious accounts, in this paper, we propose a new feature engineering strategy to augment the social network account dataset with additional features, aiming to enhance the capability of existing machine learning strategies to discriminate fake accounts. Experimental results produced through several machine learning models on account datasets of both the Twitter and the Instagram platforms highlight the effectiveness of the proposed approach towards the automatic discrimination of fake accounts. The choice of Twitter is mainly due to its strict privacy laws, and because its the only social network platform making data of their accounts publicly available.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136313989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wenhua Xiao, Xudong Fang, Bixin Liu, Ji Wang, Xiaomin Zhu
Opportunistic Mobile Edge Cloud in which opportunistically connected mobile devices run in a cooperative way to augment the capability of single device has become a timely and essential topic due to its widespread prospect under resource-constrained scenarios (e.g., disaster rescue). Because of the mobility of devices and the uncertainty of environments, it is inevitable that failures occur among the mobile nodes. Being different from existing studies that mainly focus on either data offloading or computing offloading among mobile devices in an ideal environment, we concentrate on how to guarantee the reliability of the task execution with the consideration of both data offloading and computing offloading under opportunistically connected mobile edge cloud. To this end, an optimization of mobile task offloading when considering reliability is formulated. Then, we propose a probabilistic model for task offloading and a reliability model for task execution, which estimates the probability of successful execution for a specific opportunistic path and describes the dynamic reliability of the task execution. Based on these models, a heuristic algorithm UNION (Fa u lt-Tolera n t Cooperat i ve C o mputi n g) is proposed to solve this NP-hard problem. Theoretical analysis shows that the complexity of UNION is (mathcal {O}(|mathcal {I}|^2+|mathcal {N}|) ) with guaranteeing the reliability of 0.99. Also, extensive experiments on real-world traces validate the superiority of the proposed algorithm UNION over existing typical strategies.
{"title":"UNION: Fault-Tolerant Cooperative Computing in Opportunistic Mobile Edge Cloud","authors":"Wenhua Xiao, Xudong Fang, Bixin Liu, Ji Wang, Xiaomin Zhu","doi":"10.1145/3617994","DOIUrl":"https://doi.org/10.1145/3617994","url":null,"abstract":"Opportunistic Mobile Edge Cloud in which opportunistically connected mobile devices run in a cooperative way to augment the capability of single device has become a timely and essential topic due to its widespread prospect under resource-constrained scenarios (e.g., disaster rescue). Because of the mobility of devices and the uncertainty of environments, it is inevitable that failures occur among the mobile nodes. Being different from existing studies that mainly focus on either data offloading or computing offloading among mobile devices in an ideal environment, we concentrate on how to guarantee the reliability of the task execution with the consideration of both data offloading and computing offloading under opportunistically connected mobile edge cloud. To this end, an optimization of mobile task offloading when considering reliability is formulated. Then, we propose a probabilistic model for task offloading and a reliability model for task execution, which estimates the probability of successful execution for a specific opportunistic path and describes the dynamic reliability of the task execution. Based on these models, a heuristic algorithm UNION (Fa u lt-Tolera n t Cooperat i ve C o mputi n g) is proposed to solve this NP-hard problem. Theoretical analysis shows that the complexity of UNION is (mathcal {O}(|mathcal {I}|^2+|mathcal {N}|) ) with guaranteeing the reliability of 0.99. Also, extensive experiments on real-world traces validate the superiority of the proposed algorithm UNION over existing typical strategies.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136308036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Containers have emerged as a popular technology for edge computing platforms. Although there are varieties of container orchestration frameworks, e.g., Kubernetes to provide high-reliable services for cloud infrastructure, providing real-time support at the containerized edge systems (CESs) remains a challenge. In this paper, we propose EdgeMan, a holistic edge service management framework for CESs, which consists of (1) a model-assisted event-driven lightweight online scheduling algorithm to provide request-level execution plans; (2) a bottleneck-metric-aware progressive resource allocation mechanism to improve resource efficiency. We then build a testbed that installed three containerized services with different latency sensitivities for concrete evaluation. Besides, we adopt real-world data traces from Alibaba and Twitter for large-scale emulations. Extensive experiments demonstrate that the deadline miss ratio of time-sensitive services run with EdgeMan is reduced by 85.9% on average compared with that of existing methods in both industry and academia.
{"title":"Providing Realtime Support for Containerized Edge Services","authors":"Wenzhao Zhang, Yi Gao, Wei Dong","doi":"10.1145/3617123","DOIUrl":"https://doi.org/10.1145/3617123","url":null,"abstract":"Containers have emerged as a popular technology for edge computing platforms. Although there are varieties of container orchestration frameworks, e.g., Kubernetes to provide high-reliable services for cloud infrastructure, providing real-time support at the containerized edge systems (CESs) remains a challenge. In this paper, we propose EdgeMan, a holistic edge service management framework for CESs, which consists of (1) a model-assisted event-driven lightweight online scheduling algorithm to provide request-level execution plans; (2) a bottleneck-metric-aware progressive resource allocation mechanism to improve resource efficiency. We then build a testbed that installed three containerized services with different latency sensitivities for concrete evaluation. Besides, we adopt real-world data traces from Alibaba and Twitter for large-scale emulations. Extensive experiments demonstrate that the deadline miss ratio of time-sensitive services run with EdgeMan is reduced by 85.9% on average compared with that of existing methods in both industry and academia.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42734023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The construction and governance of smart cities require the collaboration of different systems and different regions. How to realize the monitoring of abnormal hot spots through the collaboration of subsystems with limited resources is related to the stability and efficiency of the city. This work constructs a hot data processing framework for drones and 5G edge computing infrastructure, as well as an Ensemble Multi-Objective Cooperative Learning (EMOCL) method to process three different types of hot data. The data collection phase combines set operations with the 0-1 multi-knapsack model, and the cooperative learning phase realizes the degree of cooperation control while retaining the ability of independent optimization of the subsystem. Finally, the advantages of the framework are verified by hot data coverage and collaborative processing efficiency, resource use cost and balance.
{"title":"Collaborative Hotspot Data Collection with Drones and 5G Edge Computing in Smart City","authors":"Pei-Cheng Song, Jeng-Shyang Pan, H. Chao, S. Chu","doi":"10.1145/3617373","DOIUrl":"https://doi.org/10.1145/3617373","url":null,"abstract":"The construction and governance of smart cities require the collaboration of different systems and different regions. How to realize the monitoring of abnormal hot spots through the collaboration of subsystems with limited resources is related to the stability and efficiency of the city. This work constructs a hot data processing framework for drones and 5G edge computing infrastructure, as well as an Ensemble Multi-Objective Cooperative Learning (EMOCL) method to process three different types of hot data. The data collection phase combines set operations with the 0-1 multi-knapsack model, and the cooperative learning phase realizes the degree of cooperation control while retaining the ability of independent optimization of the subsystem. Finally, the advantages of the framework are verified by hot data coverage and collaborative processing efficiency, resource use cost and balance.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48410095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Internet of Things (IoT) connects a massive number of smart devices to the Internet, in which all data, applications, devices, and users require connectivity, security, and trust. Traditional security approaches assume that all participants within the network perimeter are trustworthy. However, in IoT environment data, applications, devices, and users are gradually moving outside the traditional trusted defence perimeter and have become a source of security risks. Unlike traditional security approaches, which are initially designed for the optimum protection and only act if a process is malicious, the zero-trust security framework upholds the “verify and never trust” principle. Zero trust-based approaches assume that everything within the system is untrustworthy and needs to be verified to prevent threats. Meanwhile, the blockchain technology shows promises on cyber security and several blockchain security mechanisms have been developed, including access management, user authentication, and transaction security. Due to its prowess in enhancing cyber security, blockchain can provide zero trust security framework with highly accessible and transparent security mechanisms via a visible blockchain, in which all transactions are visible to restricted operators. Zero-trust models can be secured further by a blockchain due to its sheer immutable nature and blockchain technology is expected to recognise them, authenticate their trust, and allow them access. Blockchain-enabled zero trust security can detect suspicious online transaction, isolate connection, and restrict access to the user. This special issue received in total 37 high-quality submissions. Per journal policy, it was ensured that handling editors did not have any potential conflict of interest with authors of submitted papers. All submitted papers were reviewed by at least three independent potential referees. The papers were evaluated for their rigor and quality, and also for their relevance to the theme of our special issue. After evaluating the overall scores, seven papers were selected by the guest editors and approved by the Editor-in-Chief for inclusion in this special issue. We will now briefly introduce the accepted papers.
物联网(Internet of Things, IoT)将大量的智能设备连接到互联网上，所有的数据、应用、设备和用户都需要连接、安全、信任。传统的安全方法假设网络边界内的所有参与者都是值得信任的。然而，在物联网环境中，数据、应用程序、设备和用户逐渐超出了传统的可信防御范围，成为安全风险的来源。与传统的安全方法不同，传统的安全方法最初是为最佳保护而设计的，只有在进程是恶意的情况下才会采取行动，零信任安全框架坚持“验证且永不信任”原则。基于零信任的方法假设系统中的所有内容都是不可信的，需要进行验证以防止威胁。与此同时，区块链技术在网络安全方面表现出了良好的前景，包括访问管理、用户认证和交易安全等多种区块链安全机制已经开发出来。由于区块链在增强网络安全方面的卓越能力，它可以通过一个可见的区块链提供零信任安全框架，具有高度可访问和透明的安全机制，其中所有交易对受限制的运营商都是可见的。由于其绝对不可变的性质，零信任模型可以通过区块链进一步得到保护，区块链技术有望识别它们，验证它们的信任，并允许它们访问。区块链支持的零信任安全可以检测可疑的在线交易，隔离连接，并限制用户的访问。本期特刊共收到37份高质量的投稿。根据期刊政策，处理编辑不会与提交论文的作者有任何潜在的利益冲突。所有提交的论文都由至少三名独立的潜在审稿人审阅。这些论文因其严谨性和质量以及它们与我们特刊主题的相关性而受到评价。经过综合评分，由特邀编辑选出7篇论文，经总编辑批准，纳入本期特刊。现在我们将简要介绍被接受的论文。
{"title":"Blockchain-based Zero Trust Cybersecurity in the Internet of Things","authors":"Shancang Li, Surya Nepal, T. Tryfonas, Hongwei Li","doi":"10.1145/3594535","DOIUrl":"https://doi.org/10.1145/3594535","url":null,"abstract":"The Internet of Things (IoT) connects a massive number of smart devices to the Internet, in which all data, applications, devices, and users require connectivity, security, and trust. Traditional security approaches assume that all participants within the network perimeter are trustworthy. However, in IoT environment data, applications, devices, and users are gradually moving outside the traditional trusted defence perimeter and have become a source of security risks. Unlike traditional security approaches, which are initially designed for the optimum protection and only act if a process is malicious, the zero-trust security framework upholds the “verify and never trust” principle. Zero trust-based approaches assume that everything within the system is untrustworthy and needs to be verified to prevent threats. Meanwhile, the blockchain technology shows promises on cyber security and several blockchain security mechanisms have been developed, including access management, user authentication, and transaction security. Due to its prowess in enhancing cyber security, blockchain can provide zero trust security framework with highly accessible and transparent security mechanisms via a visible blockchain, in which all transactions are visible to restricted operators. Zero-trust models can be secured further by a blockchain due to its sheer immutable nature and blockchain technology is expected to recognise them, authenticate their trust, and allow them access. Blockchain-enabled zero trust security can detect suspicious online transaction, isolate connection, and restrict access to the user. This special issue received in total 37 high-quality submissions. Per journal policy, it was ensured that handling editors did not have any potential conflict of interest with authors of submitted papers. All submitted papers were reviewed by at least three independent potential referees. The papers were evaluated for their rigor and quality, and also for their relevance to the theme of our special issue. After evaluating the overall scores, seven papers were selected by the guest editors and approved by the Editor-in-Chief for inclusion in this special issue. We will now briefly introduce the accepted papers.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45223585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Large training data and expensive model tweaking are standard features of deep learning with images. As a result, data owners often utilize cloud resources to develop large-scale complex models, which also raises privacy concerns. Existing cryptographic solutions for training deep neural networks (DNNs) are too expensive, cannot effectively utilize cloud GPU resources, and also put a significant burden on client-side pre-processing. This article presents an image disguising approach: DisguisedNets, which allows users to securely outsource images to the cloud and enables confidential, efficient GPU-based model training. DisguisedNets uses a novel combination of image blocktization, block-level random permutation, and block-level secure transformations: random multidimensional projection (RMT) or AES pixel-level encryption (AES) to transform training data. Users can use existing DNN training methods and GPU resources without any modification to training models with disguised images. We have analyzed and evaluated the methods under a multi-level threat model and compared them with another similar method—InstaHide. We also show that the image disguising approach, including both DisguisedNets and InstaHide, can effectively protect models from model-targeted attacks.
{"title":"DisguisedNets: Secure Image Outsourcing for Confidential Model Training in Clouds","authors":"Keke Chen, Yuechun Gu, Sagar Sharma","doi":"10.1145/3609506","DOIUrl":"https://doi.org/10.1145/3609506","url":null,"abstract":"Large training data and expensive model tweaking are standard features of deep learning with images. As a result, data owners often utilize cloud resources to develop large-scale complex models, which also raises privacy concerns. Existing cryptographic solutions for training deep neural networks (DNNs) are too expensive, cannot effectively utilize cloud GPU resources, and also put a significant burden on client-side pre-processing. This article presents an image disguising approach: DisguisedNets, which allows users to securely outsource images to the cloud and enables confidential, efficient GPU-based model training. DisguisedNets uses a novel combination of image blocktization, block-level random permutation, and block-level secure transformations: random multidimensional projection (RMT) or AES pixel-level encryption (AES) to transform training data. Users can use existing DNN training methods and GPU resources without any modification to training models with disguised images. We have analyzed and evaluated the methods under a multi-level threat model and compared them with another similar method—InstaHide. We also show that the image disguising approach, including both DisguisedNets and InstaHide, can effectively protect models from model-targeted attacks.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43414514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-15DOI: https://dl.acm.org/doi/10.1145/3609506
Keke Chen, Yuechun Gu, Sagar Sharma
Large training data and expensive model tweaking are standard features of deep learning with images. As a result, data owners often utilize cloud resources to develop large-scale complex models, which also raises privacy concerns. Existing cryptographic solutions for training deep neural networks (DNNs) are too expensive, cannot effectively utilize cloud GPU resources, and also put a significant burden on client-side pre-processing. This paper presents an image disguising approach: DisguisedNets that allows users to securely outsource images to the cloud and enables confidential, efficient GPU-based model training. DisgisedNets use a novel combination of image blocktization, block-level random permutation, and block-level secure transformations: random multidimensional projection (RMT) or AES pixel-level encryption (AES) to transform training data. Users can use existing DNN training methods and GPU resources without any modification to training models with disguised images. We have analyzed and evaluated the methods under a multi-level threat model and compared them with another similar method – InstaHide. We also show that the image disguising approach, including both DisguisedNets and InstaHide, can effectively protect models from model-targeted attacks.
{"title":"DisguisedNets: Secure Image Outsourcing for Confidential Model Training in Clouds","authors":"Keke Chen, Yuechun Gu, Sagar Sharma","doi":"https://dl.acm.org/doi/10.1145/3609506","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3609506","url":null,"abstract":"<p>Large training data and expensive model tweaking are standard features of deep learning with images. As a result, data owners often utilize cloud resources to develop large-scale complex models, which also raises privacy concerns. Existing cryptographic solutions for training deep neural networks (DNNs) are too expensive, cannot effectively utilize cloud GPU resources, and also put a significant burden on client-side pre-processing. This paper presents an image disguising approach: DisguisedNets that allows users to securely outsource images to the cloud and enables confidential, efficient GPU-based model training. DisgisedNets use a novel combination of image blocktization, block-level random permutation, and block-level secure transformations: random multidimensional projection (RMT) or AES pixel-level encryption (AES) to transform training data. Users can use existing DNN training methods and GPU resources without any modification to training models with disguised images. We have analyzed and evaluated the methods under a multi-level threat model and compared them with another similar method – InstaHide. We also show that the image disguising approach, including both DisguisedNets and InstaHide, can effectively protect models from model-targeted attacks.</p>","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138533446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose a novel end-to-end trust management framework for crowdsourced Internet of Things (IoT) services. The framework targets three main aspects: trust assessment, trust information credibility and accuracy, and trust information storage. We harness the usage patterns of IoT consumers to offer a trust assessment that adapts to IoT consumers’ uses. Additionally, our framework ascertains the credibility and accuracy of trust-related information before trust assessment. This is achieved by validating the data collected by IoT consumers and providers. In addition, our framework ensures the contextual fairness between IoT services and trust information. Moreover, we propose a blockchain-based trust information storage approach. Our proposed storage solution preserves the integrity and availability of trust information.
{"title":"An End-to-end Trust Management Framework for Crowdsourced IoT Services","authors":"Mohammed Bahutair, A. Bouguettaya","doi":"10.1145/3600232","DOIUrl":"https://doi.org/10.1145/3600232","url":null,"abstract":"We propose a novel end-to-end trust management framework for crowdsourced Internet of Things (IoT) services. The framework targets three main aspects: trust assessment, trust information credibility and accuracy, and trust information storage. We harness the usage patterns of IoT consumers to offer a trust assessment that adapts to IoT consumers’ uses. Additionally, our framework ascertains the credibility and accuracy of trust-related information before trust assessment. This is achieved by validating the data collected by IoT consumers and providers. In addition, our framework ensures the contextual fairness between IoT services and trust information. Moreover, we propose a blockchain-based trust information storage approach. Our proposed storage solution preserves the integrity and availability of trust information.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43502339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: https://dl.acm.org/doi/10.1145/3600232
Mohammed Bahutair, Athman Bouguettaya
We propose a novel end-to-end trust management framework for crowdsourced IoT services. The framework targets three main aspects: trust assessment, trust information credibility and accuracy, and trust information storage. We harness the usage patterns of IoT consumers to offer a trust assessment that adapts to IoT consumers’ uses. Additionally, our framework ascertains the credibility and accuracy of trust-related information before trust assessment. This is achieved by validating the data collected by IoT consumers and providers. In addition, our framework ensures the contextual fairness between IoT services and trust information. Moreover, we propose a blockchain-based trust information storage approach. Our proposed storage solution preserves the integrity and availability of trust information.
{"title":"An End-to-End Trust Management Framework for Crowdsourced IoT Services","authors":"Mohammed Bahutair, Athman Bouguettaya","doi":"https://dl.acm.org/doi/10.1145/3600232","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3600232","url":null,"abstract":"<p>We propose a novel end-to-end trust management framework for crowdsourced IoT services. The framework targets three main aspects: <i>trust assessment</i>, <i>trust information credibility and accuracy</i>, and <i>trust information storage</i>. We harness the <i>usage patterns</i> of IoT consumers to offer a trust assessment that <i>adapts</i> to IoT consumers’ uses. Additionally, our framework ascertains the <i>credibility</i> and <i>accuracy</i> of trust-related information before trust assessment. This is achieved by validating the data collected by IoT consumers and providers. In addition, our framework ensures the <i>contextual fairness</i> between IoT services and trust information. Moreover, we propose a blockchain-based trust information storage approach. Our proposed storage solution preserves the <i>integrity</i> and <i>availability</i> of trust information.</p>","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138533444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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