Growing technologies like virtualization and artificial intelligence have become more popular on mobile devices. But lack of resources faced for processing these applications is still major hurdle. Collaborative edge and cloud computing are one of the solutions to this problem. We have proposed a multi-period deep deterministic policy gradient (MP-DDPG) algorithm to find an optimal offloading policy by partitioning the task and offloading it to the collaborative cloud and edge network to reduce energy consumption. Our results show that MP-DDPG achieves the minimum latency and energy consumption in the collaborative cloud network.
{"title":"MP-DDPG: Optimal Latency-Energy Dynamic Offloading Scheme in Collaborative Cloud Networks","authors":"Jui Mhatre, Ahyoung Lee","doi":"10.1145/3555776.3577767","DOIUrl":"https://doi.org/10.1145/3555776.3577767","url":null,"abstract":"Growing technologies like virtualization and artificial intelligence have become more popular on mobile devices. But lack of resources faced for processing these applications is still major hurdle. Collaborative edge and cloud computing are one of the solutions to this problem. We have proposed a multi-period deep deterministic policy gradient (MP-DDPG) algorithm to find an optimal offloading policy by partitioning the task and offloading it to the collaborative cloud and edge network to reduce energy consumption. Our results show that MP-DDPG achieves the minimum latency and energy consumption in the collaborative cloud network.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"59 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80246251","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}
Geunsu Kim, Gyudo Park, Soohyeok Kang, Simon S. Woo
Most of the existing face recognition applications using deep learning models have leveraged CNN-based architectures as the feature extractor. However, recent studies have shown that in computer vision tasks, vision transformer-based models often outperform CNN-based models. Therefore, in this work, we propose a Sparse Vision Transformer (S-ViT) based on the Vision Transformer (ViT) architecture to improve the face recognition tasks. After the model is trained, S-ViT tends to have a sparse distribution of weights compared to ViT, so we named it according to these characteristics. Unlike the conventional ViT, our proposed S-ViT adopts image Relative Positional Encoding (iRPE) method for positional encoding. Also, S-ViT has been modified so that all token embeddings, not just class token, participate in the decoding process. Through extensive experiment, we showed that S-ViT achieves better performance in closed-set than the other baseline models, and showed better performance than the baseline ViT-based models. For example, when using ArcFace as the loss function in the identification protocol, S-ViT achieved up to 3.27% higher accuracy than ResNet50. We also show that the use of ArcFace loss functions yields greater performance gains in S-ViT than in baseline models. In addition, S-ViT has an advantage in cost-performance trade-off because it tends to be more robust to the pruning technique than the underlying model, ViT. Therefore, S-ViT offers the additional advantage, which can be applied more flexibly in the target devices with limited resources.
{"title":"S-ViT: Sparse Vision Transformer for Accurate Face Recognition","authors":"Geunsu Kim, Gyudo Park, Soohyeok Kang, Simon S. Woo","doi":"10.1145/3555776.3577640","DOIUrl":"https://doi.org/10.1145/3555776.3577640","url":null,"abstract":"Most of the existing face recognition applications using deep learning models have leveraged CNN-based architectures as the feature extractor. However, recent studies have shown that in computer vision tasks, vision transformer-based models often outperform CNN-based models. Therefore, in this work, we propose a Sparse Vision Transformer (S-ViT) based on the Vision Transformer (ViT) architecture to improve the face recognition tasks. After the model is trained, S-ViT tends to have a sparse distribution of weights compared to ViT, so we named it according to these characteristics. Unlike the conventional ViT, our proposed S-ViT adopts image Relative Positional Encoding (iRPE) method for positional encoding. Also, S-ViT has been modified so that all token embeddings, not just class token, participate in the decoding process. Through extensive experiment, we showed that S-ViT achieves better performance in closed-set than the other baseline models, and showed better performance than the baseline ViT-based models. For example, when using ArcFace as the loss function in the identification protocol, S-ViT achieved up to 3.27% higher accuracy than ResNet50. We also show that the use of ArcFace loss functions yields greater performance gains in S-ViT than in baseline models. In addition, S-ViT has an advantage in cost-performance trade-off because it tends to be more robust to the pruning technique than the underlying model, ViT. Therefore, S-ViT offers the additional advantage, which can be applied more flexibly in the target devices with limited resources.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"18 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81609493","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}
Gil-beom Lee, Jinbeom Kim, Taejune Kim, Simon S. Woo
Oriented object detection in aerial images is a challenging task due to the highly complex backgrounds and objects with arbitrary oriented and usually densely arranged. Existing oriented object detection methods adopt CNN-based methods, and they can be divided into three types: two-stage, one-stage, and anchor-free methods. All of them require non-maximum suppression (NMS) to eliminate the duplicated predictions. Recently, object detectors based on the transformer remove hand-designed components by directly solving set prediction problems via performing bipartite matching, and achieve state-of-the-art performances in general object detection. Motivated by this research, we propose a transformer-based oriented object detector named Rotated DETR with oriented bounding boxes (OBBs) labeling. We embed the scoring network to reduce the tokens corresponding to the background. In addition, we apply a proposal generator and iterative proposal refinement module in order to provide proposals with angle information to the transformer decoder. Rotated DETR achieves state-of-the-art performance on the single-stage and anchor-free oriented object detectors on DOTA, UCAS-AOD, and DIOR-R datasets with only 10% feature tokens. In the experiment, we show the effectiveness of the scoring network and iterative proposal refinement module.
{"title":"Rotated-DETR: an End-to-End Transformer-based Oriented Object Detector for Aerial Images","authors":"Gil-beom Lee, Jinbeom Kim, Taejune Kim, Simon S. Woo","doi":"10.1145/3555776.3577745","DOIUrl":"https://doi.org/10.1145/3555776.3577745","url":null,"abstract":"Oriented object detection in aerial images is a challenging task due to the highly complex backgrounds and objects with arbitrary oriented and usually densely arranged. Existing oriented object detection methods adopt CNN-based methods, and they can be divided into three types: two-stage, one-stage, and anchor-free methods. All of them require non-maximum suppression (NMS) to eliminate the duplicated predictions. Recently, object detectors based on the transformer remove hand-designed components by directly solving set prediction problems via performing bipartite matching, and achieve state-of-the-art performances in general object detection. Motivated by this research, we propose a transformer-based oriented object detector named Rotated DETR with oriented bounding boxes (OBBs) labeling. We embed the scoring network to reduce the tokens corresponding to the background. In addition, we apply a proposal generator and iterative proposal refinement module in order to provide proposals with angle information to the transformer decoder. Rotated DETR achieves state-of-the-art performance on the single-stage and anchor-free oriented object detectors on DOTA, UCAS-AOD, and DIOR-R datasets with only 10% feature tokens. In the experiment, we show the effectiveness of the scoring network and iterative proposal refinement module.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"10 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89429724","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}
Thomas Haines, Johannes Müller, Iñigo Querejeta-Azurmendi
Electronic voting (e-voting) is regularly used in many countries and organizations for legally binding elections. In order to conduct such elections securely, numerous e-voting systems have been proposed over the last few decades. Notably, some of these systems were designed to provide coercion-resistance. This property protects against potential adversaries trying to swing an election by coercing voters. Despite the multitude of existing coercion-resistant e-voting systems, to date, only few of them can handle large-scale Internet elections efficiently. One of these systems, VoteAgain (USENIX Security 2020), was originally claimed secure under similar trust assumptions to state-of-the-art e-voting systems without coercion-resistance. In this work, we review VoteAgain's security properties. We discover that, unlike originally claimed, VoteAgain is no more secure than a trivial voting system with a completely trusted election authority. In order to mitigate this issue, we propose a variant of VoteAgain which effectively mitigates trust on the election authorities and, at the same time, preserves VoteAgain's usability and efficiency. Altogether, our findings bring the state of science one step closer to the goal of scalable coercion-resistant e-voting being secure under reasonable trust assumptions.
{"title":"Scalable Coercion-Resistant E-Voting under Weaker Trust Assumptions","authors":"Thomas Haines, Johannes Müller, Iñigo Querejeta-Azurmendi","doi":"10.1145/3555776.3578730","DOIUrl":"https://doi.org/10.1145/3555776.3578730","url":null,"abstract":"Electronic voting (e-voting) is regularly used in many countries and organizations for legally binding elections. In order to conduct such elections securely, numerous e-voting systems have been proposed over the last few decades. Notably, some of these systems were designed to provide coercion-resistance. This property protects against potential adversaries trying to swing an election by coercing voters. Despite the multitude of existing coercion-resistant e-voting systems, to date, only few of them can handle large-scale Internet elections efficiently. One of these systems, VoteAgain (USENIX Security 2020), was originally claimed secure under similar trust assumptions to state-of-the-art e-voting systems without coercion-resistance. In this work, we review VoteAgain's security properties. We discover that, unlike originally claimed, VoteAgain is no more secure than a trivial voting system with a completely trusted election authority. In order to mitigate this issue, we propose a variant of VoteAgain which effectively mitigates trust on the election authorities and, at the same time, preserves VoteAgain's usability and efficiency. Altogether, our findings bring the state of science one step closer to the goal of scalable coercion-resistant e-voting being secure under reasonable trust assumptions.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"22 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89267605","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}
Mahyar Tourchi Moghaddam, Andreas Edal Pedersen, William Walter Lillebroe Bolding, T. Worm
The Single Sign-On (SSO) method eases the authentication and authorization process. The solution substantially impacts the users' experience since they only need to authenticate once to access multiple services without re-authenticating. This paper adopts an incremental prototyping approach to develop an SSO system. The research reveals that while SSO improves users' quality of experience, it could imply performance and security issues if traditional architectures are adopted. Thus, a Microservices-based approach with containerization is subsequently proposed to overcome SSO's quality issues in practice. The SSO system is containerized using Docker and managed using Docker Compose. The results show a significant performance and security improvement.
{"title":"A Performant and Secure Single Sign-On System Using Microservices","authors":"Mahyar Tourchi Moghaddam, Andreas Edal Pedersen, William Walter Lillebroe Bolding, T. Worm","doi":"10.1145/3555776.3577869","DOIUrl":"https://doi.org/10.1145/3555776.3577869","url":null,"abstract":"The Single Sign-On (SSO) method eases the authentication and authorization process. The solution substantially impacts the users' experience since they only need to authenticate once to access multiple services without re-authenticating. This paper adopts an incremental prototyping approach to develop an SSO system. The research reveals that while SSO improves users' quality of experience, it could imply performance and security issues if traditional architectures are adopted. Thus, a Microservices-based approach with containerization is subsequently proposed to overcome SSO's quality issues in practice. The SSO system is containerized using Docker and managed using Docker Compose. The results show a significant performance and security improvement.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"15 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87396157","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}
D. Sloan, E. Dombay, W. Sabiiti, B. Mtafya, Ognjen Arandelovic, Marios Zachariou
Microscopy analysis of sputum images for bacilli screening is a common method used for both diagnosis and therapy monitoring of tuberculosis (TB). Nonetheless, it is a challenging procedure, since sputum examination is time-consuming and needs highly competent personnel to provide accurate results which are important for clinical decision-making. In addition, manual fluorescence microscopy examination of sputum samples for tuberculosis diagnosis and treatment monitoring is a subjective operation. In this work, we automate the process of examining fields of view (FOVs) of TB bacteria in order to determine the lipid content, and bacterial length and width. We propose a modified version of the UNet model to rapidly localise potential bacteria inside a FOV. We introduce a novel method that uses Fourier descriptors to exclude contours that do not belong to the class of bacteria, hence minimising the amount of false positives. Finally, we propose a new feature as a means of extracting a representation fed into a support vector multi-regressor in order to estimate the length and width of each bacterium. Using a real-world data corpus, the proposed method i) outperformed previous methods, and ii) estimated the cell length and width with a root mean square error of less than 0.01%.
{"title":"Estimating Phenotypic Characteristics of Tuberculosis Bacteria","authors":"D. Sloan, E. Dombay, W. Sabiiti, B. Mtafya, Ognjen Arandelovic, Marios Zachariou","doi":"10.1145/3555776.3578609","DOIUrl":"https://doi.org/10.1145/3555776.3578609","url":null,"abstract":"Microscopy analysis of sputum images for bacilli screening is a common method used for both diagnosis and therapy monitoring of tuberculosis (TB). Nonetheless, it is a challenging procedure, since sputum examination is time-consuming and needs highly competent personnel to provide accurate results which are important for clinical decision-making. In addition, manual fluorescence microscopy examination of sputum samples for tuberculosis diagnosis and treatment monitoring is a subjective operation. In this work, we automate the process of examining fields of view (FOVs) of TB bacteria in order to determine the lipid content, and bacterial length and width. We propose a modified version of the UNet model to rapidly localise potential bacteria inside a FOV. We introduce a novel method that uses Fourier descriptors to exclude contours that do not belong to the class of bacteria, hence minimising the amount of false positives. Finally, we propose a new feature as a means of extracting a representation fed into a support vector multi-regressor in order to estimate the length and width of each bacterium. Using a real-world data corpus, the proposed method i) outperformed previous methods, and ii) estimated the cell length and width with a root mean square error of less than 0.01%.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"51 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85130465","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}
The application of deep learning-based (DL) network intrusion detection systems (NIDS) enables effective automated detection of cyberattacks. Such models can extract valuable features from high-dimensional and heterogeneous network traffic with minimal feature engineering and provide high accuracy detection rates. However, it has been shown that DL can be vulnerable to adversarial examples (AEs), which mislead classification decisions at inference time, and several works have shown that AEs are indeed a threat against DL-based NIDS. In this work, we argue that these threats are not necessarily realistic. Indeed, some general techniques used to generate AE manipulate features in a way that would be inconsistent with actual network traffic. In this paper, we first implement the main AE attacks selected from the literature (FGSM, BIM, PGD, NewtonFool, CW, DeepFool, EN, Boundary, HSJ, ZOO) for two different datasets (WSN-DS and BoT-IoT) and we compare their relative performance. We then analyze the perturbation generated by these attacks and use the metrics to establish a notion of "attack unrealism". We conclude that, for these datasets, some of these attacks are performant but not realistic.
{"title":"Realism versus Performance for Adversarial Examples Against DL-based NIDS","authors":"Huda Ali Alatwi, C. Morisset","doi":"10.1145/3555776.3577671","DOIUrl":"https://doi.org/10.1145/3555776.3577671","url":null,"abstract":"The application of deep learning-based (DL) network intrusion detection systems (NIDS) enables effective automated detection of cyberattacks. Such models can extract valuable features from high-dimensional and heterogeneous network traffic with minimal feature engineering and provide high accuracy detection rates. However, it has been shown that DL can be vulnerable to adversarial examples (AEs), which mislead classification decisions at inference time, and several works have shown that AEs are indeed a threat against DL-based NIDS. In this work, we argue that these threats are not necessarily realistic. Indeed, some general techniques used to generate AE manipulate features in a way that would be inconsistent with actual network traffic. In this paper, we first implement the main AE attacks selected from the literature (FGSM, BIM, PGD, NewtonFool, CW, DeepFool, EN, Boundary, HSJ, ZOO) for two different datasets (WSN-DS and BoT-IoT) and we compare their relative performance. We then analyze the perturbation generated by these attacks and use the metrics to establish a notion of \"attack unrealism\". We conclude that, for these datasets, some of these attacks are performant but not realistic.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"198 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86228950","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}
Nowadays, more and more smart cities around the world are being built. As a part of the smart city, intelligent public transportation plays a very important role. Improving the quality of public transportation by reducing crowdedness and total transit time is a critical issue. To this end, we propose a bus operation prediction model based on deep learning techniques, and use this model to dynamically adjust the bus departure time to improve the bus service quality. Specifically, we first combine bus fare card data and open data, such as weather conditions and traffic accidents, to build models for predicting the number of passengers who board/alight the bus at a stop, the boarding and alighting time, and the bus running time between stops. Then we combine these models to predict the operation of the bus for deciding the best bus departure time within the bus departure interval. Experimental results on real-world data of Taichung City bus route #300 show that our approach to deciding the bus departure time is effective for improving its service quality.
{"title":"Improving the Quality of Public Transportation by Dynamically Adjusting the Bus Departure Time","authors":"Shuheng Cao, S. Thamrin, Arbee L. P. Chen","doi":"10.1145/3555776.3577596","DOIUrl":"https://doi.org/10.1145/3555776.3577596","url":null,"abstract":"Nowadays, more and more smart cities around the world are being built. As a part of the smart city, intelligent public transportation plays a very important role. Improving the quality of public transportation by reducing crowdedness and total transit time is a critical issue. To this end, we propose a bus operation prediction model based on deep learning techniques, and use this model to dynamically adjust the bus departure time to improve the bus service quality. Specifically, we first combine bus fare card data and open data, such as weather conditions and traffic accidents, to build models for predicting the number of passengers who board/alight the bus at a stop, the boarding and alighting time, and the bus running time between stops. Then we combine these models to predict the operation of the bus for deciding the best bus departure time within the bus departure interval. Experimental results on real-world data of Taichung City bus route #300 show that our approach to deciding the bus departure time is effective for improving its service quality.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"23 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86297252","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}
Learning to Rank is the task of learning a ranking function from a set of query-documents pairs. Generally, documents within a query are thousands but not all documents are informative for the learning phase. Different strategies were designed to select the most informative documents from the training set. However, most of them focused on reducing the size of the training set to speed up the learning phase, sacrificing effectiveness. A first attempt in this direction was achieved by Selective Gradient Boosting a learning algorithm that makes use of customisable sampling strategy to train effective ranking models. In this work, we propose a new sampling strategy called High_Low_Sampl for selecting negative examples applicable to Selective Gradient Boosting, without compromising model effectiveness. The proposed sampling strategy allows Selective Gradient Boosting to compose a new training set by selecting from the original one three document classes: the positive examples, high-ranked negative examples and low-ranked negative examples. The resulting dataset aims at minimizing the mis-ranking risk, i.e., enhancing the discriminative power of the learned model and maintaining generalisation to unseen instances. We demonstrated through an extensive experimental analysis on publicly available datasets, that the proposed selection algorithm is able to make the most of the negative examples within the training set and leads to models capable of obtaining statistically significant improvements in terms of NDCG, compared to the state of the art.
{"title":"On the Effect of Low-Ranked Documents: A New Sampling Function for Selective Gradient Boosting","authors":"C. Lucchese, Federico Marcuzzi, S. Orlando","doi":"10.1145/3555776.3577597","DOIUrl":"https://doi.org/10.1145/3555776.3577597","url":null,"abstract":"Learning to Rank is the task of learning a ranking function from a set of query-documents pairs. Generally, documents within a query are thousands but not all documents are informative for the learning phase. Different strategies were designed to select the most informative documents from the training set. However, most of them focused on reducing the size of the training set to speed up the learning phase, sacrificing effectiveness. A first attempt in this direction was achieved by Selective Gradient Boosting a learning algorithm that makes use of customisable sampling strategy to train effective ranking models. In this work, we propose a new sampling strategy called High_Low_Sampl for selecting negative examples applicable to Selective Gradient Boosting, without compromising model effectiveness. The proposed sampling strategy allows Selective Gradient Boosting to compose a new training set by selecting from the original one three document classes: the positive examples, high-ranked negative examples and low-ranked negative examples. The resulting dataset aims at minimizing the mis-ranking risk, i.e., enhancing the discriminative power of the learned model and maintaining generalisation to unseen instances. We demonstrated through an extensive experimental analysis on publicly available datasets, that the proposed selection algorithm is able to make the most of the negative examples within the training set and leads to models capable of obtaining statistically significant improvements in terms of NDCG, compared to the state of the art.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"18 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81259953","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}
Liang-Chi Chen, Shu-Qi Yu, Chien-Chung Ho, Wei-Chen Wang, Yung-Chun Li
Conventional LSM tree designs delete data by inserting a delete mark to the specified key, and they thus it leaves several out-of-date values to the specified key on the LSM tree. As a result, the LSM tree encounters a serious data security issue due to the undeleted values when there arises the need for data sanitization. Sanitization is a time-consuming process that involves completely removing sensitive data from storage devices. Flash-based SSDs are widely used in many systems, but they lack an in-place update feature, which makes it difficult for LSM trees to maintain both privacy and performance on these devices. This work proposes an efficient sanitizable LSM-tree design for LSM-based key-value store over 3D NAND flash memories. Our proposed efficient sanitizable LSM-tree design focuses on integrating the processes of key-value pair updating and the execution of sanitization by exploiting our proposed influence-conscious programming method. The capability of the proposed design is evaluated by a series of experiments, for which we have very encouraging results.
{"title":"Efficient Sanitization Design for LSM-based Key-Value Store over 3D MLC NAND Flash","authors":"Liang-Chi Chen, Shu-Qi Yu, Chien-Chung Ho, Wei-Chen Wang, Yung-Chun Li","doi":"10.1145/3555776.3577780","DOIUrl":"https://doi.org/10.1145/3555776.3577780","url":null,"abstract":"Conventional LSM tree designs delete data by inserting a delete mark to the specified key, and they thus it leaves several out-of-date values to the specified key on the LSM tree. As a result, the LSM tree encounters a serious data security issue due to the undeleted values when there arises the need for data sanitization. Sanitization is a time-consuming process that involves completely removing sensitive data from storage devices. Flash-based SSDs are widely used in many systems, but they lack an in-place update feature, which makes it difficult for LSM trees to maintain both privacy and performance on these devices. This work proposes an efficient sanitizable LSM-tree design for LSM-based key-value store over 3D NAND flash memories. Our proposed efficient sanitizable LSM-tree design focuses on integrating the processes of key-value pair updating and the execution of sanitization by exploiting our proposed influence-conscious programming method. The capability of the proposed design is evaluated by a series of experiments, for which we have very encouraging results.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"11 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90538526","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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