Emerging applications such as remote manipulation and remote robotic surgery require communication that is both timely and reliable, but the Internet natively supports only communication that is either completely reliable with no timeliness guarantees (e.g. TCP) or timely with best-effort reliability (e.g. UDP). We present an overlay transport service that can provide highly reliable communication while meeting stringent timeliness guarantees (e.g. 130ms round-trip latency across the US) over the Internet. To enable routing schemes that can support the necessary timeliness and reliability, we introduce dissemination graphs, providing a unified framework for specifying routing schemes ranging from a single path, to multiple disjoint paths, to arbitrary graphs. We conduct an extensive analysis of real-world network data, finding that a routing approach using two disjoint paths performs well in most cases, and that cases where two disjoint paths do not perform well typically involve problems around a source or destination. Based on this analysis, we develop a timely dissemination-graph-based routing method that can add targeted redundancy in problematic areas of the network. This approach can cover over 99% of the performance gap between a traditional single-path approach and an optimal (but prohibitively expensive) scheme, while two dynamic disjoint paths cover about 70% of this gap, and two static disjoint paths cover about 45%. This performance improvement is obtained at a cost increase of about 2% over two disjoint paths.
{"title":"Timely, Reliable, and Cost-Effective Internet Transport Service Using Dissemination Graphs","authors":"Amy Babay, Emily Wagner, M. Dinitz, Y. Amir","doi":"10.1109/ICDCS.2017.63","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.63","url":null,"abstract":"Emerging applications such as remote manipulation and remote robotic surgery require communication that is both timely and reliable, but the Internet natively supports only communication that is either completely reliable with no timeliness guarantees (e.g. TCP) or timely with best-effort reliability (e.g. UDP). We present an overlay transport service that can provide highly reliable communication while meeting stringent timeliness guarantees (e.g. 130ms round-trip latency across the US) over the Internet. To enable routing schemes that can support the necessary timeliness and reliability, we introduce dissemination graphs, providing a unified framework for specifying routing schemes ranging from a single path, to multiple disjoint paths, to arbitrary graphs. We conduct an extensive analysis of real-world network data, finding that a routing approach using two disjoint paths performs well in most cases, and that cases where two disjoint paths do not perform well typically involve problems around a source or destination. Based on this analysis, we develop a timely dissemination-graph-based routing method that can add targeted redundancy in problematic areas of the network. This approach can cover over 99% of the performance gap between a traditional single-path approach and an optimal (but prohibitively expensive) scheme, while two dynamic disjoint paths cover about 70% of this gap, and two static disjoint paths cover about 45%. This performance improvement is obtained at a cost increase of about 2% over two disjoint paths.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134096598","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}
Distributed Denial of Service (DDoS) attacks are some of the most persistent threats on the Internet today. The evolution of DDoS attacks calls for an in-depth analysis of those attacks. A better understanding of the attackers’ behavior can provide insights to unveil patterns and strategies utilized by attackers. The prior art on the attackers’ behavior analysis often falls in two aspects: it assumes that adversaries are static, and makes certain simplifying assumptions on their behavior, which often are not supported by real attack data. In this paper, we take a data-driven approach to designing and validating three DDoS attack models from temporal (e.g., attack magnitudes), spatial (e.g., attacker origin), and spatiotemporal (e.g., attack inter-launching time) perspectives. We design these models based on the analysis of traces consisting of more than 50,000 verified DDoS attacks from industrial mitigation operations. Each model is also validated by testing its effectiveness in accurately predicting future DDoS attacks. Comparisons against simple intuitive models further show that our models can more accurately capture the essential features of DDoS attacks.
{"title":"An Adversary-Centric Behavior Modeling of DDoS Attacks","authors":"An Wang, Aziz Mohaisen, Songqing Chen","doi":"10.1109/ICDCS.2017.213","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.213","url":null,"abstract":"Distributed Denial of Service (DDoS) attacks are some of the most persistent threats on the Internet today. The evolution of DDoS attacks calls for an in-depth analysis of those attacks. A better understanding of the attackers’ behavior can provide insights to unveil patterns and strategies utilized by attackers. The prior art on the attackers’ behavior analysis often falls in two aspects: it assumes that adversaries are static, and makes certain simplifying assumptions on their behavior, which often are not supported by real attack data. In this paper, we take a data-driven approach to designing and validating three DDoS attack models from temporal (e.g., attack magnitudes), spatial (e.g., attacker origin), and spatiotemporal (e.g., attack inter-launching time) perspectives. We design these models based on the analysis of traces consisting of more than 50,000 verified DDoS attacks from industrial mitigation operations. Each model is also validated by testing its effectiveness in accurately predicting future DDoS attacks. Comparisons against simple intuitive models further show that our models can more accurately capture the essential features of DDoS attacks.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134050864","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}
Trajectory clustering techniques help discover interesting insights from moving object data, including common routes for people and vehicles, anomalous sub-trajectories, etc. Existing trajectory clustering techniques fail to take in to account the uncertainty present in location data. In this paper, we investigate the problem of clustering trajectory data and propose a novel algorithm for clustering similar full and sub-trajectories together while modeling uncertainty in this data. We describe the necessary pre-processing techniques for clustering trajectory data, namely techniques to discretize raw location data using Possible World semantics to capture the inherent uncertainty in location data, and to segment full trajectories in to meaningful sub-trajectories. As a baseline, we extend the well known K-means algorithm to cluster trajectory data. We then describe and evaluate a new trajectory clustering algorithm, SOM-TC (Self-Organizing Map Based Trajectory Clustering), that is inspired from the self-organizing map technique and is at least 4x faster than the baseline K-means and current density based clustering approaches.
{"title":"SOM-TC: Self-Organizing Map for Hierarchical Trajectory Clustering","authors":"P. Dewan, R. Ganti, M. Srivatsa","doi":"10.1109/ICDCS.2017.244","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.244","url":null,"abstract":"Trajectory clustering techniques help discover interesting insights from moving object data, including common routes for people and vehicles, anomalous sub-trajectories, etc. Existing trajectory clustering techniques fail to take in to account the uncertainty present in location data. In this paper, we investigate the problem of clustering trajectory data and propose a novel algorithm for clustering similar full and sub-trajectories together while modeling uncertainty in this data. We describe the necessary pre-processing techniques for clustering trajectory data, namely techniques to discretize raw location data using Possible World semantics to capture the inherent uncertainty in location data, and to segment full trajectories in to meaningful sub-trajectories. As a baseline, we extend the well known K-means algorithm to cluster trajectory data. We then describe and evaluate a new trajectory clustering algorithm, SOM-TC (Self-Organizing Map Based Trajectory Clustering), that is inspired from the self-organizing map technique and is at least 4x faster than the baseline K-means and current density based clustering approaches.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"15 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133309227","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}
Chenxi Qiu, A. Squicciarini, S. Rajtmajer, James Caverlee
Crowdsourcing sites heavily rely on paid workers to ensure completion of tasks. Yet, designing a pricing strategies able to incentivize users' quality and retention is non trivial. Existing payment strategies either simply set a fixed payment per task without considering changes in workers' behaviors, or rule out poor quality responses and workers based on coarse criteria. Hence, task requesters may be investing significantly in work that is inaccurate or even misleading. In this paper, we design a dynamic contract to incentivize high-quality work. Our proposed approach offers a theoretically proven algorithm to calculate the contract for each worker in a cost-efficient manner. In contrast to existing work, our contract design is not only adaptive to changes in workers' behavior, but also adjusts pricing policy in the presence of malicious behavior. Both theoretical and experimental analysis over real Amazon review traces show that our contract design can achieve a near optimal solution. Furthermore, experimental results demonstrate that our contract design 1) can promote high-quality work and prevent malicious behavior, and 2) outperforms the intuitive strategy of excluding all malicious workers in terms of the requester's utility.
{"title":"Dynamic Contract Design for Heterogenous Workers in Crowdsourcing for Quality Control","authors":"Chenxi Qiu, A. Squicciarini, S. Rajtmajer, James Caverlee","doi":"10.1109/ICDCS.2017.187","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.187","url":null,"abstract":"Crowdsourcing sites heavily rely on paid workers to ensure completion of tasks. Yet, designing a pricing strategies able to incentivize users' quality and retention is non trivial. Existing payment strategies either simply set a fixed payment per task without considering changes in workers' behaviors, or rule out poor quality responses and workers based on coarse criteria. Hence, task requesters may be investing significantly in work that is inaccurate or even misleading. In this paper, we design a dynamic contract to incentivize high-quality work. Our proposed approach offers a theoretically proven algorithm to calculate the contract for each worker in a cost-efficient manner. In contrast to existing work, our contract design is not only adaptive to changes in workers' behavior, but also adjusts pricing policy in the presence of malicious behavior. Both theoretical and experimental analysis over real Amazon review traces show that our contract design can achieve a near optimal solution. Furthermore, experimental results demonstrate that our contract design 1) can promote high-quality work and prevent malicious behavior, and 2) outperforms the intuitive strategy of excluding all malicious workers in terms of the requester's utility.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129452094","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}
Social media constitute nowadays one of the most common communication mediums. Millions of users exploit them daily to share information with their community in the network via messages, referred as posts. The massive volume of information shared is extremely diverse and covers a vast spectrum of topics and interests. Automatically identifying the topics of the posts is of particular interest as this can assist in a variety of applications, such as event detection, trends discovery, expert finding etc. However, designing an automated system that requires no human agent participation to identify the topics covered in posts published in Online Social Networks (OSNs) presents manifold challenges. First, posts are unstructured and commonly short, limited to just a few characters. This prevents existing classification schemes to be directly applied in such cases, due to sparseness of the text. Second, new information emerges constantly, hence building a learning corpus from past posts may fail to capture the ever evolving information emerging in OSNs. To overcome the aforementioned limitations we have designed Pythia, an automated system for short text classification that exploits the Wikipedia structure and articles to identify the topics of the posts. The topic discovery is performed in two phases. In the first step, the system exploits Wikipedia categories and articles of the corresponding categories to build the training corpus for the suppervised learning. In the second step, the text of a given post is augmented using a text enrichment mechanism that extends the post with relevant Wikipedia articles. After the initial steps are performed, we deploy k-NN classifier to determine the topic(s) covered in the original post.
社交媒体是当今最常见的交流媒介之一。数以百万计的用户每天利用微博通过消息(即帖子)与他们在网络上的社区分享信息。共享的大量信息极其多样化，涵盖了广泛的主题和兴趣。自动识别帖子的主题是特别有趣的，因为这可以帮助各种应用程序，如事件检测，趋势发现，专家寻找等。然而，设计一个不需要人工参与的自动化系统来识别在线社交网络(OSNs)上发布的帖子所涵盖的主题，面临着多方面的挑战。首先，帖子没有结构，通常很短，只有几个字。由于文本的稀疏性，这阻止了现有的分类方案直接应用于这种情况。其次，新信息不断出现，因此从过去的帖子中构建学习语料库可能无法捕获osn中不断发展的信息。To overcome the aforementioned limitations we have designed Pythia, an automated system for short text classification that exploits the Wikipedia structure and articles to identify the topics of the posts. 主题发现分两个阶段进行。In the first step, the system exploits Wikipedia categories and articles of the corresponding categories to build the training corpus for the suppervised learning. 在第二步中，使用文本充实机制对给定文章的文本进行扩充，该机制用相关的Wikipedia文章扩展文章。在执行初始步骤之后，我们部署k-NN分类器来确定原始帖子中涵盖的主题。
{"title":"Pythia: A System for Online Topic Discovery of Social Media Posts","authors":"Juliana Litou, V. Kalogeraki","doi":"10.1109/ICDCS.2017.289","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.289","url":null,"abstract":"Social media constitute nowadays one of the most common communication mediums. Millions of users exploit them daily to share information with their community in the network via messages, referred as posts. The massive volume of information shared is extremely diverse and covers a vast spectrum of topics and interests. Automatically identifying the topics of the posts is of particular interest as this can assist in a variety of applications, such as event detection, trends discovery, expert finding etc. However, designing an automated system that requires no human agent participation to identify the topics covered in posts published in Online Social Networks (OSNs) presents manifold challenges. First, posts are unstructured and commonly short, limited to just a few characters. This prevents existing classification schemes to be directly applied in such cases, due to sparseness of the text. Second, new information emerges constantly, hence building a learning corpus from past posts may fail to capture the ever evolving information emerging in OSNs. To overcome the aforementioned limitations we have designed Pythia, an automated system for short text classification that exploits the Wikipedia structure and articles to identify the topics of the posts. The topic discovery is performed in two phases. In the first step, the system exploits Wikipedia categories and articles of the corresponding categories to build the training corpus for the suppervised learning. In the second step, the text of a given post is augmented using a text enrichment mechanism that extends the post with relevant Wikipedia articles. After the initial steps are performed, we deploy k-NN classifier to determine the topic(s) covered in the original post.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"75 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114104254","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}
Raziel Carvajal Gómez, I. Gonzalez-Herrera, Yérom-David Bromberg, Laurent Réveillère, E. Rivière
Broadcast is a fundamental operation in Mobile Ad-Hoc Networks (MANETs). A large variety of broadcast algorithms have been proposed. They differ in the way message forwarding between nodes is controlled, and in the level of information about the topology that this control requires. Deployment scenarios for MANETs vary widely, in particular in terms of nodes density and mobility. The choice of an algorithm depends on its expected coverage and energy cost, which are both impacted by the deployment context. In this work, we are interested in the comprehensive comparison of the costs and effectiveness of broadcast algorithms for MANETs depending on target environmental conditions. We describe the results of an experimental study of five algorithms, representative of the main design alternatives. Our study reveals that the best algorithm for a given situation, such as a high density and a stable network, is not necessarily the most appropriate for a different situation such as a sparse and mobile network. We identify the algorithms characteristics that are correlated with these differences and discuss the pros and cons of each design.
{"title":"Density and Mobility-Driven Evaluation of Broadcast Algorithms for MANETs","authors":"Raziel Carvajal Gómez, I. Gonzalez-Herrera, Yérom-David Bromberg, Laurent Réveillère, E. Rivière","doi":"10.1109/ICDCS.2017.240","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.240","url":null,"abstract":"Broadcast is a fundamental operation in Mobile Ad-Hoc Networks (MANETs). A large variety of broadcast algorithms have been proposed. They differ in the way message forwarding between nodes is controlled, and in the level of information about the topology that this control requires. Deployment scenarios for MANETs vary widely, in particular in terms of nodes density and mobility. The choice of an algorithm depends on its expected coverage and energy cost, which are both impacted by the deployment context. In this work, we are interested in the comprehensive comparison of the costs and effectiveness of broadcast algorithms for MANETs depending on target environmental conditions. We describe the results of an experimental study of five algorithms, representative of the main design alternatives. Our study reveals that the best algorithm for a given situation, such as a high density and a stable network, is not necessarily the most appropriate for a different situation such as a sparse and mobile network. We identify the algorithms characteristics that are correlated with these differences and discuss the pros and cons of each design.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129337896","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}
Amir Sinaeepourfard, Jordi García, X. Masip-Bruin, E. Marín-Tordera
Traditional smart city resources management rely on cloud based solutions to provide a centralized and rich set of open data. The advantages of cloud based frameworks are their ubiquity, (almost) unlimited resources capacity, cost efficiency, as well as elasticity. However, accessing data from the cloud implies large network traffic, high data latencies, and higher security risks. Alternatively, fog computing emerges as a promising technology to absorb these inconveniences. The use of devices at the edge provides closer computing facilities, reduces network traffic and latencies, and improves security. We have defined a new framework for data management in the context of smart city through a global fog to cloud management architecture; in this paper we present the data acquisition block. As a first experiment we estimate the network traffic during data collection, and compare it with a traditional real system. We also show the effectiveness of some basic data aggregation techniques in the model, such as redundant data elimination and data compression.
{"title":"A Novel Architecture for Efficient Fog to Cloud Data Management in Smart Cities","authors":"Amir Sinaeepourfard, Jordi García, X. Masip-Bruin, E. Marín-Tordera","doi":"10.1109/ICDCS.2017.202","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.202","url":null,"abstract":"Traditional smart city resources management rely on cloud based solutions to provide a centralized and rich set of open data. The advantages of cloud based frameworks are their ubiquity, (almost) unlimited resources capacity, cost efficiency, as well as elasticity. However, accessing data from the cloud implies large network traffic, high data latencies, and higher security risks. Alternatively, fog computing emerges as a promising technology to absorb these inconveniences. The use of devices at the edge provides closer computing facilities, reduces network traffic and latencies, and improves security. We have defined a new framework for data management in the context of smart city through a global fog to cloud management architecture; in this paper we present the data acquisition block. As a first experiment we estimate the network traffic during data collection, and compare it with a traditional real system. We also show the effectiveness of some basic data aggregation techniques in the model, such as redundant data elimination and data compression.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116228997","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}
Raluca Halalai, P. Felber, Anne-Marie Kermarrec, François Taïani
Erasure coding is an established data protection mechanism. It provides high resiliency with low storage overhead, which makes it very attractive to storage systems developers. Unfortunately, when used in a distributed setting, erasure coding hampers a storage system's performance, because it requires clients to contact several, possibly remote sites to retrieve their data. This has hindered the adoption of erasure coding in practice, limiting its use to cold, archival data. Recent research showed that it is feasible to use erasure coding for hot data as well, thus opening new perspectives for improving erasure-coded storage systems. In this paper, we address the problem of minimizing access latency in erasure-coded storage. We propose Agar-a novel caching system tailored for erasure-coded content. Agar optimizes the contents of the cache based on live information regarding data popularity and access latency to different data storage sites. Our system adapts a dynamic programming algorithm to optimize the choice of data blocks that are cached, using an approach akin to "Knapsack" algorithms. We compare Agar to the classical Least Recently Used and Least Frequently Used cache eviction policies, while varying the amount of data cached between a data chunk and a whole replica of the object. We show that Agar can achieve 16% to 41% lower latency than systems that use classical caching policies.
{"title":"Agar: A Caching System for Erasure-Coded Data","authors":"Raluca Halalai, P. Felber, Anne-Marie Kermarrec, François Taïani","doi":"10.1109/ICDCS.2017.97","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.97","url":null,"abstract":"Erasure coding is an established data protection mechanism. It provides high resiliency with low storage overhead, which makes it very attractive to storage systems developers. Unfortunately, when used in a distributed setting, erasure coding hampers a storage system's performance, because it requires clients to contact several, possibly remote sites to retrieve their data. This has hindered the adoption of erasure coding in practice, limiting its use to cold, archival data. Recent research showed that it is feasible to use erasure coding for hot data as well, thus opening new perspectives for improving erasure-coded storage systems. In this paper, we address the problem of minimizing access latency in erasure-coded storage. We propose Agar-a novel caching system tailored for erasure-coded content. Agar optimizes the contents of the cache based on live information regarding data popularity and access latency to different data storage sites. Our system adapts a dynamic programming algorithm to optimize the choice of data blocks that are cached, using an approach akin to \"Knapsack\" algorithms. We compare Agar to the classical Least Recently Used and Least Frequently Used cache eviction policies, while varying the amount of data cached between a data chunk and a whole replica of the object. We show that Agar can achieve 16% to 41% lower latency than systems that use classical caching policies.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"24 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123497911","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}
Smartphone users can conveniently install a set of apps that provide Location Based Service (LBS) from markets. These LBS-based apps facilitate users in many application scenarios, but they raise concerns on the breach of privacy related to location access. Smartphone users can hardly perceive location access, especially when it happens in background. In comparison to location access in foreground, location access in background could result in more serious privacy breach because it can continuously know a user's locations. In this paper, we study the problem of location access in background, and especially perform the first measurement of this background action on the Google app market. Our investigation demonstrates that many popular apps conduct location access in background within short intervals. This enables these apps to collect a user's location trace, from which the important personal information, Points of Interest (PoIs), can be recognized. We further extract a user's movement pattern from the PoIs, and utilize it to measure the extent of privacy breach. The measurement results also show that using the combination of movement pattern related metrics and the other PoI related metrics can help detect the privacy breach in an earlier manner than using either one of them alone.
{"title":"Location Privacy Breach: Apps Are Watching You in Background","authors":"Dachuan Liu, Xing Gao, Haining Wang","doi":"10.1109/ICDCS.2017.227","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.227","url":null,"abstract":"Smartphone users can conveniently install a set of apps that provide Location Based Service (LBS) from markets. These LBS-based apps facilitate users in many application scenarios, but they raise concerns on the breach of privacy related to location access. Smartphone users can hardly perceive location access, especially when it happens in background. In comparison to location access in foreground, location access in background could result in more serious privacy breach because it can continuously know a user's locations. In this paper, we study the problem of location access in background, and especially perform the first measurement of this background action on the Google app market. Our investigation demonstrates that many popular apps conduct location access in background within short intervals. This enables these apps to collect a user's location trace, from which the important personal information, Points of Interest (PoIs), can be recognized. We further extract a user's movement pattern from the PoIs, and utilize it to measure the extent of privacy breach. The measurement results also show that using the combination of movement pattern related metrics and the other PoI related metrics can help detect the privacy breach in an earlier manner than using either one of them alone.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128881711","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}
T. He, E. Ciftcioglu, Shiqiang Wang, Kevin S. Chan
In this paper, we consider user location privacy in mobile edge clouds (MECs). MECs are small clouds deployed at the network edge to offer cloud services close to mobile users, and many solutions have been proposed to maximize service locality by migrating services to follow their users. Co-location of a user and his service, however, implies that a cyber eavesdropper observing service migrations between MECs can localize the user up to one MEC coverage area, which can be fairly small (e.g., a femtocell). We consider using chaff services to defend against such an eavesdropper, with focus on strategies to control the chaffs. Assuming the eavesdropper performs maximum likelihood (ML) detection, we consider both heuristic strategies that mimic the user's mobility and optimized strategies designed to minimize the detection or tracking accuracy. We show that a single chaff controlled by the optimal strategy can drive the eavesdropper's tracking accuracy to zero when the user's mobility is sufficiently random. The efficacy of our solutions is verified through extensive simulations.
{"title":"Location Privacy in Mobile Edge Clouds","authors":"T. He, E. Ciftcioglu, Shiqiang Wang, Kevin S. Chan","doi":"10.1109/ICDCS.2017.39","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.39","url":null,"abstract":"In this paper, we consider user location privacy in mobile edge clouds (MECs). MECs are small clouds deployed at the network edge to offer cloud services close to mobile users, and many solutions have been proposed to maximize service locality by migrating services to follow their users. Co-location of a user and his service, however, implies that a cyber eavesdropper observing service migrations between MECs can localize the user up to one MEC coverage area, which can be fairly small (e.g., a femtocell). We consider using chaff services to defend against such an eavesdropper, with focus on strategies to control the chaffs. Assuming the eavesdropper performs maximum likelihood (ML) detection, we consider both heuristic strategies that mimic the user's mobility and optimized strategies designed to minimize the detection or tracking accuracy. We show that a single chaff controlled by the optimal strategy can drive the eavesdropper's tracking accuracy to zero when the user's mobility is sufficiently random. The efficacy of our solutions is verified through extensive simulations.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126323657","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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