Pub Date : 2024-04-24DOI: 10.1109/tetc.2024.3390435
Stefano Longari, Jacopo Jannone, Mario Polino, Michele Carminati, Andrea Zanchettin, Mara Tanelli, Stefano Zanero
{"title":"Janus: A Trusted Execution Environment Approach for Attack Detection in Industrial Robot Controllers","authors":"Stefano Longari, Jacopo Jannone, Mario Polino, Michele Carminati, Andrea Zanchettin, Mara Tanelli, Stefano Zanero","doi":"10.1109/tetc.2024.3390435","DOIUrl":"https://doi.org/10.1109/tetc.2024.3390435","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"2015 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140801415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-04-17DOI: 10.1109/tetc.2024.3387558
Md Rasid Ali, Debranjan Pal, Abhijit Das, Dipanwita Roy Chowdhury
{"title":"HARPOCRATES: An Approach Towards Efficient Encryption of Data-at-rest","authors":"Md Rasid Ali, Debranjan Pal, Abhijit Das, Dipanwita Roy Chowdhury","doi":"10.1109/tetc.2024.3387558","DOIUrl":"https://doi.org/10.1109/tetc.2024.3387558","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"1 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140612836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-04-16DOI: 10.1109/tetc.2024.3387119
Xiao-Yan Li, Jou-Ming Chang
{"title":"LP-Star : Embedding Longest Paths into Star Networks with Large-Scale Missing Edges under an Emerging Assessment Model","authors":"Xiao-Yan Li, Jou-Ming Chang","doi":"10.1109/tetc.2024.3387119","DOIUrl":"https://doi.org/10.1109/tetc.2024.3387119","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"19 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140612684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-04-16DOI: 10.1109/tetc.2024.3387026
Daniel Casanueva-Morato, Alvaro Ayuso-Martinez, J. P. Dominguez-Morales, Angel Jimenez-Fernandez, Gabriel Jimenez-Moreno
{"title":"A Bio-inspired Implementation of A Sparse-learning Spike-based Hippocampus Memory Model","authors":"Daniel Casanueva-Morato, Alvaro Ayuso-Martinez, J. P. Dominguez-Morales, Angel Jimenez-Fernandez, Gabriel Jimenez-Moreno","doi":"10.1109/tetc.2024.3387026","DOIUrl":"https://doi.org/10.1109/tetc.2024.3387026","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"34 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140612832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-04-16DOI: 10.1109/tetc.2024.3386893
Sangwoo Hwang, Jaeha Kung
{"title":"One-Spike SNN: Single-Spike Phase Coding With Base Manipulation for ANN-to-SNN Conversion Loss Minimization","authors":"Sangwoo Hwang, Jaeha Kung","doi":"10.1109/tetc.2024.3386893","DOIUrl":"https://doi.org/10.1109/tetc.2024.3386893","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"166 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140612834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-04-15DOI: 10.1109/tetc.2024.3386803
Kun-chang Li, Peng-bo Wang, Run-hua Shi
{"title":"A Novel Privacy-Preserving Range Query Scheme with Permissioned Blockchain for Smart Grid","authors":"Kun-chang Li, Peng-bo Wang, Run-hua Shi","doi":"10.1109/tetc.2024.3386803","DOIUrl":"https://doi.org/10.1109/tetc.2024.3386803","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"2 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140567824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-04-04DOI: 10.1109/TETC.2024.3383321
Alfonso Sánchez-Macián;Jorge Martínez;Pedro Reviriego;Shanshan Liu;Fabrizio Lombardi
Estimating the frequency of elements in a data stream and identifying the elements that appear many times (also known as heavy hitters) are needed in many applications such as traffic monitoring in networks or popularity estimate in web and social networks. The Count-Min Sketch (CMS) is probably one of the most widely used algorithms for frequency estimate. The CMS uses a sub-linear space to provide queries for data streams and retrieve an approximate value for the frequency of events. It has been used in many different applications and scenarios, making its security and privacy a matter of interest. This paper considers the privacy of the CMS and presents an algorithm to extract the most frequent elements (also known as top-K) and their estimate from a CMS. This is possible for universes of a limited size; when the attacker has access to the sketch, its hash functions and the counters at a specific point of time. The algorithm is tested using CAIDA traces showing that it is able to retrieve the group of top-K elements with an acceptable percentage of false positives and negatives. The results improve with the size of the sketch and for smaller values of K, indicating that in some practical settings an attacker can extract substantial information about the top-K elements from the sketch. The code used in the simulation is provided in a public open-source repository to facilitate reproducing our results and extending the ideas presented in this paper.
{"title":"On the Privacy of the Count-Min Sketch: Extracting the Top-K Elements","authors":"Alfonso Sánchez-Macián;Jorge Martínez;Pedro Reviriego;Shanshan Liu;Fabrizio Lombardi","doi":"10.1109/TETC.2024.3383321","DOIUrl":"10.1109/TETC.2024.3383321","url":null,"abstract":"Estimating the frequency of elements in a data stream and identifying the elements that appear many times (also known as heavy hitters) are needed in many applications such as traffic monitoring in networks or popularity estimate in web and social networks. The Count-Min Sketch (CMS) is probably one of the most widely used algorithms for frequency estimate. The CMS uses a sub-linear space to provide queries for data streams and retrieve an approximate value for the frequency of events. It has been used in many different applications and scenarios, making its security and privacy a matter of interest. This paper considers the privacy of the CMS and presents an algorithm to extract the most frequent elements (also known as top-K) and their estimate from a CMS. This is possible for universes of a limited size; when the attacker has access to the sketch, its hash functions and the counters at a specific point of time. The algorithm is tested using CAIDA traces showing that it is able to retrieve the group of top-K elements with an acceptable percentage of false positives and negatives. The results improve with the size of the sketch and for smaller values of K, indicating that in some practical settings an attacker can extract substantial information about the top-K elements from the sketch. The code used in the simulation is provided in a public open-source repository to facilitate reproducing our results and extending the ideas presented in this paper.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 4","pages":"1056-1065"},"PeriodicalIF":5.1,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10492661","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140567825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The integration of graph structures in diverse domains has recently garnered substantial attention, presenting a paradigm shift from classical euclidean representations. This new trend is driven by the advent of novel algorithms that can capture complex relationships through a class of neural architectures: the Graph Neural Networks (GNNs) [1], [2]. These networks are adept at handling data that can be effectively modeled as graphs, introducing a new representation learning paradigm. The significance of GNNs extends to several domains, including computer vision [3], [4], natural language processing [5], chemistry/biology [6], physics [7], traffic networks [8], and recommendation systems [9].
{"title":"Guest Editorial Emerging Trends and Advances in Graph-Based Methods and Applications","authors":"Alessandro D'Amelio;Jianyi Lin;Jean-Yves Ramel;Raffaella Lanzarotti","doi":"10.1109/TETC.2024.3374581","DOIUrl":"https://doi.org/10.1109/TETC.2024.3374581","url":null,"abstract":"The integration of graph structures in diverse domains has recently garnered substantial attention, presenting a paradigm shift from classical euclidean representations. This new trend is driven by the advent of novel algorithms that can capture complex relationships through a class of neural architectures: the Graph Neural Networks (GNNs) [1], [2]. These networks are adept at handling data that can be effectively modeled as graphs, introducing a new representation learning paradigm. The significance of GNNs extends to several domains, including computer vision [3], [4], natural language processing [5], chemistry/biology [6], physics [7], traffic networks [8], and recommendation systems [9].","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 1","pages":"122-125"},"PeriodicalIF":5.9,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10474156","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140161144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-18DOI: 10.1109/TETC.2024.3369288
Alberto Bosio;Ronald F. DeMara;Deliang Fan;Nima TaheriNejad
Computer architecture stands at an important crossroad to surmount vital performance challenges. For more than four decades, the performance of general purpose computing systems has been improving by 20–50% per year [1]. In the last decade, this number has dropped to less than 7% per year. Most recently, that rate has slowed to only 3% per year. [1]. The demand for performance improvement, however, keeps increasing and diversifies within new application domains. This higher performance, however, often has to come at a lower power consumption cost too, adding to the complexity of the task of architectural design space optimization. Both today's computer architectures and device technologies (used to manufacture them) are facing major challenges to achieve the performance demands required by complex applications such as Artificial Intelligence (AI). The complexity stems from the extremely high number of operations to be computed and the involved amount of data.
{"title":"Guest Editorial IEEE Transactions on Emerging Topics in Special Section on Emerging In-Memory Computing Architectures and Applications","authors":"Alberto Bosio;Ronald F. DeMara;Deliang Fan;Nima TaheriNejad","doi":"10.1109/TETC.2024.3369288","DOIUrl":"https://doi.org/10.1109/TETC.2024.3369288","url":null,"abstract":"Computer architecture stands at an important crossroad to surmount vital performance challenges. For more than four decades, the performance of general purpose computing systems has been improving by 20–50% per year [1]. In the last decade, this number has dropped to less than 7% per year. Most recently, that rate has slowed to only 3% per year. [1]. The demand for performance improvement, however, keeps increasing and diversifies within new application domains. This higher performance, however, often has to come at a lower power consumption cost too, adding to the complexity of the task of architectural design space optimization. Both today's computer architectures and device technologies (used to manufacture them) are facing major challenges to achieve the performance demands required by complex applications such as Artificial Intelligence (AI). The complexity stems from the extremely high number of operations to be computed and the involved amount of data.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 1","pages":"4-6"},"PeriodicalIF":5.9,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10474152","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140161235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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