Social networks provide a rich data source for researchers that can be accessed in a comparatively effortless way. As data and text mining methods such as Sentiment Analysis are becoming increasingly refined, the wealth of social network data opens up entirely new possibilities for exploring specific in-depth research questions. In this paper an approach towards the retrieval, analysis and interpretation of social network data for research purposes is developed. The data is filtered according to relevant criteria and analyzed using Sentiment Analysis tools tailored specifically to the data source. The approach is verified by applying it to two example research questions, confirming past findings on cultural and gender differences in sentiment expression.
{"title":"Sentiment Analysis of Twitter Data: Towards Filtering, Analyzing and Interpreting Social Network Data","authors":"L. Branz, P. Brockmann","doi":"10.1145/3210284.3219769","DOIUrl":"https://doi.org/10.1145/3210284.3219769","url":null,"abstract":"Social networks provide a rich data source for researchers that can be accessed in a comparatively effortless way. As data and text mining methods such as Sentiment Analysis are becoming increasingly refined, the wealth of social network data opens up entirely new possibilities for exploring specific in-depth research questions. In this paper an approach towards the retrieval, analysis and interpretation of social network data for research purposes is developed. The data is filtered according to relevant criteria and analyzed using Sentiment Analysis tools tailored specifically to the data source. The approach is verified by applying it to two example research questions, confirming past findings on cultural and gender differences in sentiment expression.","PeriodicalId":412438,"journal":{"name":"Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127354395","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}
Evaluation of Distributed Complex Event Processing (CEP) systems is a rather challenging task. To simplify this task, we developed the open simulation framework for Distributed CEP, called DCEP-Sim. The goal of this tutorial is to facilitate the process of using DCEP-Sim. Since DCEP-Sim is designed and implemented in the popular network simulator ns-3 we introduce the most important concepts of ns-3. Simulations in ns-3 are configured and executed though a main program called an ns-3 script. We use a simple example script to explain how simulations with DCEP-Sim are set up and executed. To give an idea how DCEP-Sim can be adjusted to particular needs, we explain how DCEP-Sim can be adapted (e.g., through changing the workload and the network topology) and how new Distributed CEP solutions can be added by explaining how to add a new operator to DCEP-Sim.
{"title":"DCEP-Sim: An Open Simulation Framework for Distributed CEP: Introduction for Users and Prospective Developers","authors":"Fabrice Starks, Stein Kristiansen, T. Plagemann","doi":"10.1145/3210284.3219501","DOIUrl":"https://doi.org/10.1145/3210284.3219501","url":null,"abstract":"Evaluation of Distributed Complex Event Processing (CEP) systems is a rather challenging task. To simplify this task, we developed the open simulation framework for Distributed CEP, called DCEP-Sim. The goal of this tutorial is to facilitate the process of using DCEP-Sim. Since DCEP-Sim is designed and implemented in the popular network simulator ns-3 we introduce the most important concepts of ns-3. Simulations in ns-3 are configured and executed though a main program called an ns-3 script. We use a simple example script to explain how simulations with DCEP-Sim are set up and executed. To give an idea how DCEP-Sim can be adjusted to particular needs, we explain how DCEP-Sim can be adapted (e.g., through changing the workload and the network topology) and how new Distributed CEP solutions can be added by explaining how to add a new operator to DCEP-Sim.","PeriodicalId":412438,"journal":{"name":"Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115016155","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}
To fully exploit the capabilities of sensors in real life, especially cameras, smart camera surveillance requires the cooperation from both domain experts in computer vision and systems. Existing alert-based smart surveillance is only capable of tracking a limited number of suspicious objects, while in most real-life applications, we often do not know the perpetrator ahead of time for tracking their activities in advance. In this work, we propose a radically different approach to smart surveillance for vehicle tracking. Specifically, we explore a smart camera surveillance system aimed at tracking all vehicles in real time. The insight is not to store the raw videos, but to store the space-time trajectories of the vehicles. Since vehicle tracking is a continuous and geo-distributed task, we assume a geo-distributed Fog computing infrastructure as the execution platform for our system. To bound the storage space for storing the trajectories on each Fog node (serving the computational needs of a camera), we focus on the activities of vehicles in the vicinity of a given camera in a specific geographic region instead of the time dimension, and the fact that every vehicle has a "finite" lifetime. To bound the computational and network communication requirements for detection, re-identification, and inter-node communication, we propose novel techniques, namely, forward and backward propagation that reduces the latency for the operations and the communication overhead. STTR is a system for smart surveillance that we have built embodying these ideas. For evaluation, we develop a toolkit upon SUMO to emulate camera detections from traffic flow and adopt MaxiNet to emulate the fog computing infrastructure on Microsoft Azure.
{"title":"STTR","authors":"Zhuangdi Xu, Harshit Gupta, U. Ramachandran","doi":"10.1145/3210284.3210291","DOIUrl":"https://doi.org/10.1145/3210284.3210291","url":null,"abstract":"To fully exploit the capabilities of sensors in real life, especially cameras, smart camera surveillance requires the cooperation from both domain experts in computer vision and systems. Existing alert-based smart surveillance is only capable of tracking a limited number of suspicious objects, while in most real-life applications, we often do not know the perpetrator ahead of time for tracking their activities in advance. In this work, we propose a radically different approach to smart surveillance for vehicle tracking. Specifically, we explore a smart camera surveillance system aimed at tracking all vehicles in real time. The insight is not to store the raw videos, but to store the space-time trajectories of the vehicles. Since vehicle tracking is a continuous and geo-distributed task, we assume a geo-distributed Fog computing infrastructure as the execution platform for our system. To bound the storage space for storing the trajectories on each Fog node (serving the computational needs of a camera), we focus on the activities of vehicles in the vicinity of a given camera in a specific geographic region instead of the time dimension, and the fact that every vehicle has a \"finite\" lifetime. To bound the computational and network communication requirements for detection, re-identification, and inter-node communication, we propose novel techniques, namely, forward and backward propagation that reduces the latency for the operations and the communication overhead. STTR is a system for smart surveillance that we have built embodying these ideas. For evaluation, we develop a toolkit upon SUMO to emulate camera detections from traffic flow and adopt MaxiNet to emulate the fog computing infrastructure on Microsoft Azure.","PeriodicalId":412438,"journal":{"name":"Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122633322","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 Internet of Things (IoT) envisions a huge number of networked sensors connected to the internet. These sensors collect large streams of data which serve as input to wide range of IoT applications and services such as e-health, e-commerce, and automotive services. Complex Event Processing (CEP) is a powerful tool that transforms streams of raw sensor data into meaningful information required by these IoT services. Often these streams of data collected by sensors carry privacy-sensitive information about the user. Thus, protecting privacy is of paramount importance in IoT services based on CEP. In this paper we present a novel pattern-level access control mechanism for CEP based services that conceals private information while minimizing the impact on useful non-sensitive information required by the services to provide a certain quality of service (QoS). The idea is to reorder events from the event stream to conceal privacy-sensitive event patterns while preserving non-privacy sensitive event patterns to maximize QoS. We propose two approaches, namely an ILP-based approach and a graph-based approach, calculating an optimal reordering of events. Our evaluation results show that these approaches are effective in concealing private patterns without significant loss of QoS.
{"title":"Preserving Privacy and Quality of Service in Complex Event Processing through Event Reordering","authors":"S. Palanisamy, Frank Dürr, M. Tariq, K. Rothermel","doi":"10.1145/3210284.3210296","DOIUrl":"https://doi.org/10.1145/3210284.3210296","url":null,"abstract":"The Internet of Things (IoT) envisions a huge number of networked sensors connected to the internet. These sensors collect large streams of data which serve as input to wide range of IoT applications and services such as e-health, e-commerce, and automotive services. Complex Event Processing (CEP) is a powerful tool that transforms streams of raw sensor data into meaningful information required by these IoT services. Often these streams of data collected by sensors carry privacy-sensitive information about the user. Thus, protecting privacy is of paramount importance in IoT services based on CEP. In this paper we present a novel pattern-level access control mechanism for CEP based services that conceals private information while minimizing the impact on useful non-sensitive information required by the services to provide a certain quality of service (QoS). The idea is to reorder events from the event stream to conceal privacy-sensitive event patterns while preserving non-privacy sensitive event patterns to maximize QoS. We propose two approaches, namely an ILP-based approach and a graph-based approach, calculating an optimal reordering of events. Our evaluation results show that these approaches are effective in concealing private patterns without significant loss of QoS.","PeriodicalId":412438,"journal":{"name":"Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121284822","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}
Joris van Rooij, Vincenzo Gulisano, M. Papatriantafilou
Smart Grids and Advanced Metering Infrastructures are rapidly replacing traditional energy grids. The cumulative computational power of their IT devices, which can be leveraged to continuously monitor the state of the grid, is nonetheless vastly underused. This paper provides evidence of the potential of streaming analysis run at smart grid devices. We propose a structural component, which we name LoCoVolt (Local Comparison of Voltages), that is able to detect in a distributed fashion malfunctioning smart meters, which report erroneous information about the power quality. This is achieved by comparing the voltage readings of meters that, because of their proximity in the network, are expected to report readings following similar trends. Having this information can allow utilities to react promptly and thus increase timeliness, quality and safety of their services to society and, implicitly, their business value. As we show, based on our implementation on Apache Flink and the evaluation conducted with resource-constrained hardware (i.e., with capacity similar to that of hardware in smart grids) and data from a real-world network, the streaming paradigm can deliver efficient and effective monitoring tools and thus achieve the desired goals with almost no additional computational cost.
{"title":"LoCoVolt: Distributed Detection of Broken Meters in Smart Grids through Stream Processing","authors":"Joris van Rooij, Vincenzo Gulisano, M. Papatriantafilou","doi":"10.1145/3210284.3210298","DOIUrl":"https://doi.org/10.1145/3210284.3210298","url":null,"abstract":"Smart Grids and Advanced Metering Infrastructures are rapidly replacing traditional energy grids. The cumulative computational power of their IT devices, which can be leveraged to continuously monitor the state of the grid, is nonetheless vastly underused. This paper provides evidence of the potential of streaming analysis run at smart grid devices. We propose a structural component, which we name LoCoVolt (Local Comparison of Voltages), that is able to detect in a distributed fashion malfunctioning smart meters, which report erroneous information about the power quality. This is achieved by comparing the voltage readings of meters that, because of their proximity in the network, are expected to report readings following similar trends. Having this information can allow utilities to react promptly and thus increase timeliness, quality and safety of their services to society and, implicitly, their business value. As we show, based on our implementation on Apache Flink and the evaluation conducted with resource-constrained hardware (i.e., with capacity similar to that of hardware in smart grids) and data from a real-world network, the streaming paradigm can deliver efficient and effective monitoring tools and thus achieve the desired goals with almost no additional computational cost.","PeriodicalId":412438,"journal":{"name":"Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130842820","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}
Oleh Bodunov, Florian Schmidt, André Martin, Andrey Brito, C. Fetzer
In this paper, we present our approach for solving the DEBS Grand Challenge 2018. The challenge asks to provide a prediction for (i) a destination and the (ii) arrival time of ships in a streaming-fashion using Geo-spatial data in the maritime context. Novel aspects of our approach include the use of ensemble learning based on Random Forest, Gradient Boosting Decision Trees (GBDT), XGBoost Trees and Extremely Randomized Trees (ERT) in order to provide a prediction for a destination while for the arrival time, we propose the use of Feed-forward Neural Networks. In our evaluation, we were able to achieve an accuracy of 97% for the port destination classification problem and 90% (in minutes) for the ETA prediction.
{"title":"Real-time Destination and ETA Prediction for Maritime Traffic","authors":"Oleh Bodunov, Florian Schmidt, André Martin, Andrey Brito, C. Fetzer","doi":"10.1145/3210284.3220502","DOIUrl":"https://doi.org/10.1145/3210284.3220502","url":null,"abstract":"In this paper, we present our approach for solving the DEBS Grand Challenge 2018. The challenge asks to provide a prediction for (i) a destination and the (ii) arrival time of ships in a streaming-fashion using Geo-spatial data in the maritime context. Novel aspects of our approach include the use of ensemble learning based on Random Forest, Gradient Boosting Decision Trees (GBDT), XGBoost Trees and Extremely Randomized Trees (ERT) in order to provide a prediction for a destination while for the arrival time, we propose the use of Feed-forward Neural Networks. In our evaluation, we were able to achieve an accuracy of 97% for the port destination classification problem and 90% (in minutes) for the ETA prediction.","PeriodicalId":412438,"journal":{"name":"Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132762784","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 data stream processing systems need to efficiently handle large volumes of data in near real-time. To achieve this, the schedulers within such systems minimise the data movement between highly communicating tasks, improving system throughput. However, finding an optimal schedule for these systems is NP-hard. In this research, we propose a heuristic scheduling algorithm which reliably and efficiently finds the highly communicating tasks by exploiting graph partitioning algorithms and a mathematical optimisation software package. We evaluate our scheduler with two popular existing schedulers R-Storm and Aniello et al.'s 'Online scheduler' using two real-world applications and show that our proposed scheduler outperforms R-Storm, increasing throughput by between 3% and 30% and Online scheduler by 20--86% as a result of finding a more efficient schedule.
{"title":"Iterative Scheduling for Distributed Stream Processing Systems","authors":"Leila Eskandari, J. Mair, Zhiyi Huang, D. Eyers","doi":"10.1145/3210284.3219768","DOIUrl":"https://doi.org/10.1145/3210284.3219768","url":null,"abstract":"Nowadays data stream processing systems need to efficiently handle large volumes of data in near real-time. To achieve this, the schedulers within such systems minimise the data movement between highly communicating tasks, improving system throughput. However, finding an optimal schedule for these systems is NP-hard. In this research, we propose a heuristic scheduling algorithm which reliably and efficiently finds the highly communicating tasks by exploiting graph partitioning algorithms and a mathematical optimisation software package. We evaluate our scheduler with two popular existing schedulers R-Storm and Aniello et al.'s 'Online scheduler' using two real-world applications and show that our proposed scheduler outperforms R-Storm, increasing throughput by between 3% and 30% and Online scheduler by 20--86% as a result of finding a more efficient schedule.","PeriodicalId":412438,"journal":{"name":"Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126580346","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}
Valentin Rosca, Emanuel Onica, Paul Diac, Ciprian Amariei
The DEBS Grand Challenge 2018 is set in the context of maritime route prediction. Vessel routes are modeled as streams of Automatic Identification System (AIS) data points selected from real-world tracking data. The challenge requires to correctly estimate the destination ports and arrival times of vessel trips, as early as possible. Our proposed solution partitions the training vessel routes by reported destination port and uses a nearest neighbor search to find the training routes that are closer to the query AIS point. Particular improvements have been included as well, such as a way to avoid changing the predicted ports frequently within one query route and automating the parameters tuning by the use of a genetic algorithm. This leads to significant improvements on the final score.
{"title":"Predicting Destinations by Nearest Neighbor Search on Training Vessel Routes","authors":"Valentin Rosca, Emanuel Onica, Paul Diac, Ciprian Amariei","doi":"10.1145/3210284.3220509","DOIUrl":"https://doi.org/10.1145/3210284.3220509","url":null,"abstract":"The DEBS Grand Challenge 2018 is set in the context of maritime route prediction. Vessel routes are modeled as streams of Automatic Identification System (AIS) data points selected from real-world tracking data. The challenge requires to correctly estimate the destination ports and arrival times of vessel trips, as early as possible. Our proposed solution partitions the training vessel routes by reported destination port and uses a nearest neighbor search to find the training routes that are closer to the query AIS point. Particular improvements have been included as well, such as a way to avoid changing the predicted ports frequently within one query route and automating the parameters tuning by the use of a genetic algorithm. This leads to significant improvements on the final score.","PeriodicalId":412438,"journal":{"name":"Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114285474","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}
Popularly known for powering cryptocurrencies such as Bitcoin and Ethereum, blockchains is seen as a disruptive technology capable of impacting a wide variety of domains, ranging from finance to governance, by offering superior security, reliability, and transparency in a decentralized manner. In this tutorial presentation, we first study the original Bitcoin design, as well as Ethereum and Hyperledger, and reflect on their design from an academic perspective. We provide an overview of potential applications and associated research challenges, as well as a survey of ongoing research projects. We mention opportunities blockchain creates for event-based systems. Finally, we conclude with a walkthrough showing the process of developing a decentralized application (ĐSApp), using a popular Smart Contract language (Solidity) for the blockchain platform of Ethereum.
{"title":"Deconstructing Blockchains: Concepts, Systems, and Insights","authors":"Kaiwen Zhang, R. Vitenberg, H. Jacobsen","doi":"10.1145/3210284.3219502","DOIUrl":"https://doi.org/10.1145/3210284.3219502","url":null,"abstract":"Popularly known for powering cryptocurrencies such as Bitcoin and Ethereum, blockchains is seen as a disruptive technology capable of impacting a wide variety of domains, ranging from finance to governance, by offering superior security, reliability, and transparency in a decentralized manner. In this tutorial presentation, we first study the original Bitcoin design, as well as Ethereum and Hyperledger, and reflect on their design from an academic perspective. We provide an overview of potential applications and associated research challenges, as well as a survey of ongoing research projects. We mention opportunities blockchain creates for event-based systems. Finally, we conclude with a walkthrough showing the process of developing a decentralized application (ĐSApp), using a popular Smart Contract language (Solidity) for the blockchain platform of Ethereum.","PeriodicalId":412438,"journal":{"name":"Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133373908","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}
A crucial concern regarding cloud computing is the confidentiality of sensitive data being processed in the cloud. Trusted Execution Environments (TEEs), such as Intel Software Guard extensions (SGX), allow applications to run securely on an untrusted platform. However, using TEEs alone for stream processing is not enough to ensure privacy as network communication patterns may leak information about the data. This paper introduces two techniques -- anycast and multicast --for mitigating leakage at inter-stage communications in streaming applications according to a user-selected mitigation level. These techniques aim to achieve network data obliviousness, i.e., communication patterns should not depend on the data. We implement these techniques in an SGX-based stream processing system. We evaluate the latency and throughput overheads, and the data obliviousness using three benchmark applications. The results show that anycast scales better with input load and mitigation level, and provides better data obliviousness than multicast.
{"title":"Mitigating Network Side Channel Leakage for Stream Processing Systems in Trusted Execution Environments","authors":"Muhammad Bilal, Hassan Alsibyani, M. Canini","doi":"10.1145/3210284.3210286","DOIUrl":"https://doi.org/10.1145/3210284.3210286","url":null,"abstract":"A crucial concern regarding cloud computing is the confidentiality of sensitive data being processed in the cloud. Trusted Execution Environments (TEEs), such as Intel Software Guard extensions (SGX), allow applications to run securely on an untrusted platform. However, using TEEs alone for stream processing is not enough to ensure privacy as network communication patterns may leak information about the data. This paper introduces two techniques -- anycast and multicast --for mitigating leakage at inter-stage communications in streaming applications according to a user-selected mitigation level. These techniques aim to achieve network data obliviousness, i.e., communication patterns should not depend on the data. We implement these techniques in an SGX-based stream processing system. We evaluate the latency and throughput overheads, and the data obliviousness using three benchmark applications. The results show that anycast scales better with input load and mitigation level, and provides better data obliviousness than multicast.","PeriodicalId":412438,"journal":{"name":"Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130321585","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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