A. Trivedi, Patrick Stuedi, B. Metzler, Clemens Lutz, M. Schmatz, T. Gross
Distributed DRAM stores have become an attractive option for providing fast data accesses to analytics applications. To accelerate the performance of these stores, researchers have proposed using RDMA technology. RDMA offers high bandwidth and low latency data access by carefully separating resource setup from IO operations, and making IO operations fast by using rich network semantics and offloading. Despite recent interest, leveraging the full potential of RDMA in a distributed environment remains a challenging task. In this paper, we present RDMA Store or RStore, a DRAM-based data store that delivers high performance by extending RDMA's separation philosophy to a distributed setting. RStore achieves high aggregate bandwidth (705 Gb/s) and close-to-hardware latency on our 12-machine testbed. We developed a distributed graph processing framework and a Key-Value sorter using RStore's unique memory-like API. The graph processing framework, which relies on RStore for low-latency graph access, outperforms state-of-the-art systems by margins of 2.6 -- 4.2× when calculating Page Rank. The Key-Value sorter can sort 256 GB of data in 31.7 sec, which is 8× better than Hadoop TeraSort in a similar setting.
{"title":"RStore: A Direct-Access DRAM-based Data Store","authors":"A. Trivedi, Patrick Stuedi, B. Metzler, Clemens Lutz, M. Schmatz, T. Gross","doi":"10.1109/ICDCS.2015.74","DOIUrl":"https://doi.org/10.1109/ICDCS.2015.74","url":null,"abstract":"Distributed DRAM stores have become an attractive option for providing fast data accesses to analytics applications. To accelerate the performance of these stores, researchers have proposed using RDMA technology. RDMA offers high bandwidth and low latency data access by carefully separating resource setup from IO operations, and making IO operations fast by using rich network semantics and offloading. Despite recent interest, leveraging the full potential of RDMA in a distributed environment remains a challenging task. In this paper, we present RDMA Store or RStore, a DRAM-based data store that delivers high performance by extending RDMA's separation philosophy to a distributed setting. RStore achieves high aggregate bandwidth (705 Gb/s) and close-to-hardware latency on our 12-machine testbed. We developed a distributed graph processing framework and a Key-Value sorter using RStore's unique memory-like API. The graph processing framework, which relies on RStore for low-latency graph access, outperforms state-of-the-art systems by margins of 2.6 -- 4.2× when calculating Page Rank. The Key-Value sorter can sort 256 GB of data in 31.7 sec, which is 8× better than Hadoop TeraSort in a similar setting.","PeriodicalId":129182,"journal":{"name":"2015 IEEE 35th International Conference on Distributed Computing Systems","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131069625","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}
Bruhadeshwar Bezawada, A. Liu, Bargav Jayaraman, Ann L. Wang, Rui Li
Cloud computing has become indispensable in providing highly reliable data services to users. But, there are major concerns about the privacy of the data stored on cloud servers. While encryption of data provides sufficient protection, it is challenging to support rich querying functionality, such as string matching, over the encrypted data. In this work, we present the first ever symmetric key based approach to support privacy preserving string matching in cloud computing. We describe an efficient and accurate indexing structure, the PASS tree, which can execute a string pattern query in logarithmic time complexity over a set of data items. The PASS tree provides strong privacy guarantees against attacks from a semi-honest adversary. We have comprehensively evaluated our scheme over large real-life data, such as Wikipedia and Enron documents, containing up to 100000 keywords, and show that our algorithms achieve pattern search in less than a few milliseconds with 100% accuracy. Furthermore, we also describe a relevance ranking algorithm to return the most relevant documents to the user based on the pattern query. Our ranking algorithm achieves 90%+ above precision in ranking the returned documents.
{"title":"Privacy Preserving String Matching for Cloud Computing","authors":"Bruhadeshwar Bezawada, A. Liu, Bargav Jayaraman, Ann L. Wang, Rui Li","doi":"10.1109/ICDCS.2015.68","DOIUrl":"https://doi.org/10.1109/ICDCS.2015.68","url":null,"abstract":"Cloud computing has become indispensable in providing highly reliable data services to users. But, there are major concerns about the privacy of the data stored on cloud servers. While encryption of data provides sufficient protection, it is challenging to support rich querying functionality, such as string matching, over the encrypted data. In this work, we present the first ever symmetric key based approach to support privacy preserving string matching in cloud computing. We describe an efficient and accurate indexing structure, the PASS tree, which can execute a string pattern query in logarithmic time complexity over a set of data items. The PASS tree provides strong privacy guarantees against attacks from a semi-honest adversary. We have comprehensively evaluated our scheme over large real-life data, such as Wikipedia and Enron documents, containing up to 100000 keywords, and show that our algorithms achieve pattern search in less than a few milliseconds with 100% accuracy. Furthermore, we also describe a relevance ranking algorithm to return the most relevant documents to the user based on the pattern query. Our ranking algorithm achieves 90%+ above precision in ranking the returned documents.","PeriodicalId":129182,"journal":{"name":"2015 IEEE 35th International Conference on Distributed Computing Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122413325","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}
Network monitoring is an essential component of network operation and, as the network size increases, it usually generates a significant overhead in large scale networks such as sensor and data center networks. In this paper, we show that measurement correlation often exhibited in real networks can be successfully exploited to reduce the network monitoring overhead. In particular, we propose an online adaptive measurement technique with which a subset of nodes are dynamically chosen as monitors while the measurements of the remaining nodes are estimated using the computed correlations. We propose an estimation framework based on jointly Gaussian distributed random variables, and formulate an optimization problem to select the monitors which minimize the estimation error under a total cost constraint. We show that the problem is NP-Hard and propose three efficient heuristics. In order to apply our framework to real-world networks, in which measurement distribution and correlation may significantly change over time, we also develop a learning based approach that automatically switches between learning and estimation phases using a change detection algorithm. Simulations carried out on two real traces from sensor networks and data centers show that our algorithms outperforms previous solutions based on compressed sensing and it is able to reduce the monitoring overhead by 50% while incurring a low estimation error. The results further demonstrate that applying the change detection algorithm reduces the estimation error up to two orders of magnitude.
{"title":"An Online Method for Minimizing Network Monitoring Overhead","authors":"S. Silvestri, Rahul Urgaonkar, M. Zafer, B. J. Ko","doi":"10.1109/ICDCS.2015.35","DOIUrl":"https://doi.org/10.1109/ICDCS.2015.35","url":null,"abstract":"Network monitoring is an essential component of network operation and, as the network size increases, it usually generates a significant overhead in large scale networks such as sensor and data center networks. In this paper, we show that measurement correlation often exhibited in real networks can be successfully exploited to reduce the network monitoring overhead. In particular, we propose an online adaptive measurement technique with which a subset of nodes are dynamically chosen as monitors while the measurements of the remaining nodes are estimated using the computed correlations. We propose an estimation framework based on jointly Gaussian distributed random variables, and formulate an optimization problem to select the monitors which minimize the estimation error under a total cost constraint. We show that the problem is NP-Hard and propose three efficient heuristics. In order to apply our framework to real-world networks, in which measurement distribution and correlation may significantly change over time, we also develop a learning based approach that automatically switches between learning and estimation phases using a change detection algorithm. Simulations carried out on two real traces from sensor networks and data centers show that our algorithms outperforms previous solutions based on compressed sensing and it is able to reduce the monitoring overhead by 50% while incurring a low estimation error. The results further demonstrate that applying the change detection algorithm reduces the estimation error up to two orders of magnitude.","PeriodicalId":129182,"journal":{"name":"2015 IEEE 35th International Conference on Distributed Computing Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132800330","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}
Cloud computing heralded an era where resources can be scaled up and down elastically and in an online manner. This paper initiates the study of cost-effective cloud resource allocation algorithms under price discounts, using a competitive analysis approach. We show that for a single resource, the online resource renting problem can be seen as a 2-dimensional variant of the classic online parking permit problem, and we formally introduce the PPP2 problem accordingly. Our main contribution is an online algorithm for PPP2 which achieves a deterministic competitive ratio of k (under a certain set of assumptions), where k is the number of resource bundles. This is almost optimal, as we also prove a lower bound of k/3 for any deterministic online algorithm. Our online algorithm makes use of an optimal offline algorithm, which may be of independent interest since it is the first optimal offline algorithm for the 1D and 2D versions of the parking permit problem. Finally, we show that our algorithms and results also generalize to multiple resources (i.e., Multi-dimensional parking permit problems).
{"title":"Competitive Strategies for Online Cloud Resource Allocation with Discounts: The 2-Dimensional Parking Permit Problem","authors":"Xinhui Hu, Arne Ludwig, A. Richa, S. Schmid","doi":"10.1109/ICDCS.2015.18","DOIUrl":"https://doi.org/10.1109/ICDCS.2015.18","url":null,"abstract":"Cloud computing heralded an era where resources can be scaled up and down elastically and in an online manner. This paper initiates the study of cost-effective cloud resource allocation algorithms under price discounts, using a competitive analysis approach. We show that for a single resource, the online resource renting problem can be seen as a 2-dimensional variant of the classic online parking permit problem, and we formally introduce the PPP2 problem accordingly. Our main contribution is an online algorithm for PPP2 which achieves a deterministic competitive ratio of k (under a certain set of assumptions), where k is the number of resource bundles. This is almost optimal, as we also prove a lower bound of k/3 for any deterministic online algorithm. Our online algorithm makes use of an optimal offline algorithm, which may be of independent interest since it is the first optimal offline algorithm for the 1D and 2D versions of the parking permit problem. Finally, we show that our algorithms and results also generalize to multiple resources (i.e., Multi-dimensional parking permit problems).","PeriodicalId":129182,"journal":{"name":"2015 IEEE 35th International Conference on Distributed Computing Systems","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126324391","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}
Searchable Symmetric Encryption (SSE) encrypts data in such a way that they can be searched efficiently. Some recent SSE schemes allow modification of data, yet they may incur storage overhead to support parallelism in searching, or additional computation to minimize the potential leakage incurred by the update, both penalize the performance. Moreover, most of them consider only keyword search and not applicable to arbitrary structured data. In this work, we propose the first parallel and dynamic symmetric-key structured encryption, which supports query of encrypted data structure. Our scheme leverages the rather simple randomized binary search tree to achieve non-interactive queries and updates.
{"title":"Structured Encryption with Non-interactive Updates and Parallel Traversal","authors":"Russell W. F. Lai, Sherman S. M. Chow","doi":"10.1109/ICDCS.2015.104","DOIUrl":"https://doi.org/10.1109/ICDCS.2015.104","url":null,"abstract":"Searchable Symmetric Encryption (SSE) encrypts data in such a way that they can be searched efficiently. Some recent SSE schemes allow modification of data, yet they may incur storage overhead to support parallelism in searching, or additional computation to minimize the potential leakage incurred by the update, both penalize the performance. Moreover, most of them consider only keyword search and not applicable to arbitrary structured data. In this work, we propose the first parallel and dynamic symmetric-key structured encryption, which supports query of encrypted data structure. Our scheme leverages the rather simple randomized binary search tree to achieve non-interactive queries and updates.","PeriodicalId":129182,"journal":{"name":"2015 IEEE 35th International Conference on Distributed Computing Systems","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130577939","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}
Recent trend in cloud computing demands vast and ever increasing storage capacity for data centers. For many cloud service providers, much of the storage capacity demand is driven by cold and archival data, such as user uploaded contents, system logs, and backups. In this paper, we describe UStore, a hard disk based storage system designed for such workloads. We make the assumption that most data centers are already populated with computer servers and networking gears, and propose a solution to attach additional disks to these servers reliably at extremely low cost. The main component of UStore is a novel fat tree interconnect fabric to connect hard disks to existing servers and network infrastructure. To reduce cost, UStore leverages the mature commodity USB 3.0 technology to build the fabric, which has extremely low amortized cost per disk while still providing sufficient throughput to satisfy cold and archival workload. The software of the UStore system abstracts the system's physical topology and provides a consistent view of the storage capacity to the upper layer services such as distributed file systems or backup services. In a sense, UStore can be regarded as external USB hard disks designed for data centers.
{"title":"UStore: A Low Cost Cold and Archival Data Storage System for Data Centers","authors":"Quanlu Zhang, Yafei Dai, Fengqian Li, Lintao Zhang","doi":"10.1109/ICDCS.2015.51","DOIUrl":"https://doi.org/10.1109/ICDCS.2015.51","url":null,"abstract":"Recent trend in cloud computing demands vast and ever increasing storage capacity for data centers. For many cloud service providers, much of the storage capacity demand is driven by cold and archival data, such as user uploaded contents, system logs, and backups. In this paper, we describe UStore, a hard disk based storage system designed for such workloads. We make the assumption that most data centers are already populated with computer servers and networking gears, and propose a solution to attach additional disks to these servers reliably at extremely low cost. The main component of UStore is a novel fat tree interconnect fabric to connect hard disks to existing servers and network infrastructure. To reduce cost, UStore leverages the mature commodity USB 3.0 technology to build the fabric, which has extremely low amortized cost per disk while still providing sufficient throughput to satisfy cold and archival workload. The software of the UStore system abstracts the system's physical topology and provides a consistent view of the storage capacity to the upper layer services such as distributed file systems or backup services. In a sense, UStore can be regarded as external USB hard disks designed for data centers.","PeriodicalId":129182,"journal":{"name":"2015 IEEE 35th International Conference on Distributed Computing Systems","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129184664","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}
Young Yoon, Nathan Robinson, Vinod Muthusamy, Sheila A. McIlraith, H. Jacobsen
Reconfiguring a topology is an important management technique to sustain high efficiency and robustness of an overlay. But, the problem of transforming the overlay from an old topology to a newly refined topology, at runtime, has received relatively little attention. The key challenge is to minimize the disruption that can be caused by topology transformation operations. Excessive disruption can be costly and harmful and thus it may hamper the decision to migrate to a better topology. To address this issue, we solve a problem of finding an appropriate sequence of steps to transform a topology that incurs the least service disruption. We refer to this problem as an incremental topology transformation (ITT) problem. The ITT problem can be formulated as an automated planning problem and can be solved with numerous off-the-shelf planning techniques. However, we found that state-of-the-art domain-independent planning techniques did not scale to solve large ITT problem instances. This shortcoming motivated us to develop a suite of planners that use novel domain-specific heuristics to guide the search for a solution. We empirically evaluated our planners on a wide range of topologies. Our results illustrate that our planners offer a viable solution to a diversity of ITT problems. We envision that our approach could eventually provide a compelling addition to the arsenal of techniques currently employed by the administrators of distributed overlay networks.
{"title":"Towards Planning the Transformation of Overlays","authors":"Young Yoon, Nathan Robinson, Vinod Muthusamy, Sheila A. McIlraith, H. Jacobsen","doi":"10.1109/ICDCS.2015.107","DOIUrl":"https://doi.org/10.1109/ICDCS.2015.107","url":null,"abstract":"Reconfiguring a topology is an important management technique to sustain high efficiency and robustness of an overlay. But, the problem of transforming the overlay from an old topology to a newly refined topology, at runtime, has received relatively little attention. The key challenge is to minimize the disruption that can be caused by topology transformation operations. Excessive disruption can be costly and harmful and thus it may hamper the decision to migrate to a better topology. To address this issue, we solve a problem of finding an appropriate sequence of steps to transform a topology that incurs the least service disruption. We refer to this problem as an incremental topology transformation (ITT) problem. The ITT problem can be formulated as an automated planning problem and can be solved with numerous off-the-shelf planning techniques. However, we found that state-of-the-art domain-independent planning techniques did not scale to solve large ITT problem instances. This shortcoming motivated us to develop a suite of planners that use novel domain-specific heuristics to guide the search for a solution. We empirically evaluated our planners on a wide range of topologies. Our results illustrate that our planners offer a viable solution to a diversity of ITT problems. We envision that our approach could eventually provide a compelling addition to the arsenal of techniques currently employed by the administrators of distributed overlay networks.","PeriodicalId":129182,"journal":{"name":"2015 IEEE 35th International Conference on Distributed Computing Systems","volume":"235 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116638390","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}
In order to decrease the sweep cycle and the number of mobile sensors required, we propose a route scheduling problem in this paper which is the first to consider the effect of sensing range. We prove that the Distance-Sensitive-Route Scheduling(DSRS) problem is NP-hard, and consider two different scenarios: the single kissing-point case and the general case. For different cases, We propose three corresponding approximation algorithms ROSE, G-ROSE, D-ROSE.
{"title":"A Route Scheduling Algorithm for the Sweep Coverage Problem","authors":"Zhiyin Chen, S. Wu, Xudong Zhu, Xiaofeng Gao, Jian Gu, Guihai Chen","doi":"10.1109/ICDCS.2015.91","DOIUrl":"https://doi.org/10.1109/ICDCS.2015.91","url":null,"abstract":"In order to decrease the sweep cycle and the number of mobile sensors required, we propose a route scheduling problem in this paper which is the first to consider the effect of sensing range. We prove that the Distance-Sensitive-Route Scheduling(DSRS) problem is NP-hard, and consider two different scenarios: the single kissing-point case and the general case. For different cases, We propose three corresponding approximation algorithms ROSE, G-ROSE, D-ROSE.","PeriodicalId":129182,"journal":{"name":"2015 IEEE 35th International Conference on Distributed Computing Systems","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134381977","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}
In this paper, we address a promising application in the Vehicular Cyber-Physical Systems (VCPS) called roadside advertisement dissemination. Its application involves three elements: the drivers in the vehicles, Roadside Access Points (RAPs), and shopkeepers. The shopkeeper wants to attract as many customers as possible, through using RAPs to disseminate advertisements to the passing vehicles. Upon receiving an advertisement, the driver may detour towards the shop, depending on the detour distance. Given a fixed number of RAPs and the traffic distribution, our goal is to optimize the RAP placement for the shopkeeper to maximally attract potential customers. This application is a non-trivial extension of traditional coverage problems, the difference being that we use RAPs to cover the traffic flows. RAP placement algorithms may pose complex trade-offs. If we place RAPs at locations that can provide small detour distances to attract more customers, these locations may not necessarily be located in heavy traffic regions. While heavy traffic regions cover more flows, they can cause large detour distances, making shopping less attractive to customers. To balance this trade off, novel RAP placement algorithms are proposed. Since real-world traffic distributions exhibit unique patterns, here we further consider the Manhattan grid scenario and then propose corresponding near-optimal solutions. Real trace-driven experiments validate the competitive performance of the proposed algorithms.
{"title":"Optimizing Roadside Advertisement Dissemination in Vehicular Cyber-Physical Systems","authors":"Huanyang Zheng, Jie Wu","doi":"10.1109/ICDCS.2015.13","DOIUrl":"https://doi.org/10.1109/ICDCS.2015.13","url":null,"abstract":"In this paper, we address a promising application in the Vehicular Cyber-Physical Systems (VCPS) called roadside advertisement dissemination. Its application involves three elements: the drivers in the vehicles, Roadside Access Points (RAPs), and shopkeepers. The shopkeeper wants to attract as many customers as possible, through using RAPs to disseminate advertisements to the passing vehicles. Upon receiving an advertisement, the driver may detour towards the shop, depending on the detour distance. Given a fixed number of RAPs and the traffic distribution, our goal is to optimize the RAP placement for the shopkeeper to maximally attract potential customers. This application is a non-trivial extension of traditional coverage problems, the difference being that we use RAPs to cover the traffic flows. RAP placement algorithms may pose complex trade-offs. If we place RAPs at locations that can provide small detour distances to attract more customers, these locations may not necessarily be located in heavy traffic regions. While heavy traffic regions cover more flows, they can cause large detour distances, making shopping less attractive to customers. To balance this trade off, novel RAP placement algorithms are proposed. Since real-world traffic distributions exhibit unique patterns, here we further consider the Manhattan grid scenario and then propose corresponding near-optimal solutions. Real trace-driven experiments validate the competitive performance of the proposed algorithms.","PeriodicalId":129182,"journal":{"name":"2015 IEEE 35th International Conference on Distributed Computing Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116296313","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}
L. Kong, Liang He, Xiao-Yang Liu, Yu Gu, Minyou Wu, Xuemei Liu
Location based services have experienced an explosive growth and evolved from utilizing a single location to the whole trajectory. Due to the hardware and energy constraints, there are usually many missing data within a trajectory. In order to accurately recover the complete trajectory, crowdsensing provides a promising method. This method resorts to the correlation among multiple users' trajectories and the advanced compressive sensing technique, which significantly outperforms conventional interpolation methods on accuracy. However, as trajectories exposes users' daily activities, the privacy issue is a major concern in crowdsensing. While existing solutions independently tackle the accurate trajectory recovery and privacy issues, yet no single design is able to address these two challenges simultaneously. Therefore in this paper, we propose a novel Privacy Preserving Compressive Sensing (PPCS) scheme, which encrypts a trajectory with several other trajectories while maintaining the homomorphic obfuscation property for compressive sensing. Under PPCS, adversaries can only capture the encrypted data, so the user privacy is preserved. Furthermore, the homomorphic obfuscation property guarantees that the recovery accuracy of PPCS is comparable to the state-of-the-art compressive sensing design. Based on two publicly available traces with numerous users and long durations, we conduct extensive simulations to evaluate PPCS. The results demonstrate that PPCS achieves a high accuracy of <;53 m and a large distortion between the encrypted and the original trajectories (a commonly adopted metric of privacy strength) of >9,000 m even when up to 50% original data are missing.
{"title":"Privacy-Preserving Compressive Sensing for Crowdsensing Based Trajectory Recovery","authors":"L. Kong, Liang He, Xiao-Yang Liu, Yu Gu, Minyou Wu, Xuemei Liu","doi":"10.1109/ICDCS.2015.12","DOIUrl":"https://doi.org/10.1109/ICDCS.2015.12","url":null,"abstract":"Location based services have experienced an explosive growth and evolved from utilizing a single location to the whole trajectory. Due to the hardware and energy constraints, there are usually many missing data within a trajectory. In order to accurately recover the complete trajectory, crowdsensing provides a promising method. This method resorts to the correlation among multiple users' trajectories and the advanced compressive sensing technique, which significantly outperforms conventional interpolation methods on accuracy. However, as trajectories exposes users' daily activities, the privacy issue is a major concern in crowdsensing. While existing solutions independently tackle the accurate trajectory recovery and privacy issues, yet no single design is able to address these two challenges simultaneously. Therefore in this paper, we propose a novel Privacy Preserving Compressive Sensing (PPCS) scheme, which encrypts a trajectory with several other trajectories while maintaining the homomorphic obfuscation property for compressive sensing. Under PPCS, adversaries can only capture the encrypted data, so the user privacy is preserved. Furthermore, the homomorphic obfuscation property guarantees that the recovery accuracy of PPCS is comparable to the state-of-the-art compressive sensing design. Based on two publicly available traces with numerous users and long durations, we conduct extensive simulations to evaluate PPCS. The results demonstrate that PPCS achieves a high accuracy of <;53 m and a large distortion between the encrypted and the original trajectories (a commonly adopted metric of privacy strength) of >9,000 m even when up to 50% original data are missing.","PeriodicalId":129182,"journal":{"name":"2015 IEEE 35th International Conference on Distributed Computing Systems","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130395890","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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