Declustering techniques reduce query response times through parallel I/O by distributing data among multiple devices. Recently, replication based approaches were proposed to further reduce the response time. All of the replication based schemes assume that replication is done at a single site. In this paper, we consider replicated data stored at multiple sites. We formulate multi-site retrieval problem as a maximum flow problem and solve it using maximum flow techniques. We propose a low complexity online algorithm for the problem. We investigate the proposed scheme using various replication schemes, query types and query loads. Proposed scheme can easily be extended to nonuniform data and to any number of sites. Experimental results show that replication using orthogonal allocation performs the best under various settings.
{"title":"Multi-Site Retrieval of Declustered Data","authors":"A. Tosun","doi":"10.1109/ICDCS.2008.72","DOIUrl":"https://doi.org/10.1109/ICDCS.2008.72","url":null,"abstract":"Declustering techniques reduce query response times through parallel I/O by distributing data among multiple devices. Recently, replication based approaches were proposed to further reduce the response time. All of the replication based schemes assume that replication is done at a single site. In this paper, we consider replicated data stored at multiple sites. We formulate multi-site retrieval problem as a maximum flow problem and solve it using maximum flow techniques. We propose a low complexity online algorithm for the problem. We investigate the proposed scheme using various replication schemes, query types and query loads. Proposed scheme can easily be extended to nonuniform data and to any number of sites. Experimental results show that replication using orthogonal allocation performs the best under various settings.","PeriodicalId":240205,"journal":{"name":"2008 The 28th International Conference on Distributed Computing Systems","volume":"62 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131038759","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}
Resource discovery is an important process for finding suitable nodes that satisfy application requirements in large loosely-coupled distributed systems. Besides inter-node heterogeneity, many of these systems also show a high degree of intra-node dynamism, so that selecting nodes based only on their recently observed resource capacities for scalability reasons can lead to poor deployment decisions resulting in application failures or migration overheads. In this paper, we propose the notion of a resource bundle - a representative resource usage distribution for a group of nodes with similar resource usage patterns - that employs two complementary techniques to overcome the limitations of existing techniques: resource usage histograms to provide statistical guarantees for resource capacities, and clustering-based resource aggregation to achieve scalability. Using trace-driven simulations and data analysis of a month-long Planet Lab trace, we show that resource bundles are able to provide high accuracy for statistical resource discovery (up to 56% better precision than using only recent values), while achieving high scalability (up to 55% fewer messages than a non-aggregation algorithm). We also show that resource bundles are ideally suited for identifying group-level characteristics such as finding load hot spots and estimating total group capacity (within 8% of actual values).
{"title":"Resource Bundles: Using Aggregation for Statistical Wide-Area Resource Discovery and Allocation","authors":"Michael Cardosa, A. Chandra","doi":"10.1109/ICDCS.2008.37","DOIUrl":"https://doi.org/10.1109/ICDCS.2008.37","url":null,"abstract":"Resource discovery is an important process for finding suitable nodes that satisfy application requirements in large loosely-coupled distributed systems. Besides inter-node heterogeneity, many of these systems also show a high degree of intra-node dynamism, so that selecting nodes based only on their recently observed resource capacities for scalability reasons can lead to poor deployment decisions resulting in application failures or migration overheads. In this paper, we propose the notion of a resource bundle - a representative resource usage distribution for a group of nodes with similar resource usage patterns - that employs two complementary techniques to overcome the limitations of existing techniques: resource usage histograms to provide statistical guarantees for resource capacities, and clustering-based resource aggregation to achieve scalability. Using trace-driven simulations and data analysis of a month-long Planet Lab trace, we show that resource bundles are able to provide high accuracy for statistical resource discovery (up to 56% better precision than using only recent values), while achieving high scalability (up to 55% fewer messages than a non-aggregation algorithm). We also show that resource bundles are ideally suited for identifying group-level characteristics such as finding load hot spots and estimating total group capacity (within 8% of actual values).","PeriodicalId":240205,"journal":{"name":"2008 The 28th International Conference on Distributed Computing Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126643777","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 central problem in sensor network security is that sensors are susceptible to physical capture attacks. Once a sensor is compromised, the adversary can easily launch clone attacks by replicating the compromised node, distributing the clones throughout the network, and starting a variety of insider attacks. Previous works against clone attacks suffer from either a high communication/storage overhead or a poor detection accuracy. In this paper, we propose a novel scheme for detecting clone attacks in sensor networks, which computes for each sensor a social fingerprint by extracting the neighborhood characteristics, and verifies the legitimacy of the originator for each message by checking the enclosed fingerprint. The fingerprint generation is based on the superimposed s-disjunct code, which incurs a very light communication and computation overhead. The fingerprint verification is conducted at both the base station and the neighboring sensors, which ensures a high detection probability. The security and performance analysis indicate that our algorithm can identify clone attacks with a high detection probability at the cost of a low computation/communication/storage overhead. To our best knowledge, our scheme is the first to provide realtime detection of clone attacks in an effective and efficient way.
{"title":"Real-Time Detection of Clone Attacks in Wireless Sensor Networks","authors":"Kai Xing, Fang Liu, Xiuzhen Cheng, D. Du","doi":"10.1109/ICDCS.2008.55","DOIUrl":"https://doi.org/10.1109/ICDCS.2008.55","url":null,"abstract":"A central problem in sensor network security is that sensors are susceptible to physical capture attacks. Once a sensor is compromised, the adversary can easily launch clone attacks by replicating the compromised node, distributing the clones throughout the network, and starting a variety of insider attacks. Previous works against clone attacks suffer from either a high communication/storage overhead or a poor detection accuracy. In this paper, we propose a novel scheme for detecting clone attacks in sensor networks, which computes for each sensor a social fingerprint by extracting the neighborhood characteristics, and verifies the legitimacy of the originator for each message by checking the enclosed fingerprint. The fingerprint generation is based on the superimposed s-disjunct code, which incurs a very light communication and computation overhead. The fingerprint verification is conducted at both the base station and the neighboring sensors, which ensures a high detection probability. The security and performance analysis indicate that our algorithm can identify clone attacks with a high detection probability at the cost of a low computation/communication/storage overhead. To our best knowledge, our scheme is the first to provide realtime detection of clone attacks in an effective and efficient way.","PeriodicalId":240205,"journal":{"name":"2008 The 28th International Conference on Distributed Computing Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125630479","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 coding is a promising enhancement of routing to improve network throughput and provide high reliability. It allows a node to generate output messages by encoding its received messages. Peer-to-peer networks are a perfect place to apply network coding due to two reasons: the topology of a peer-to-peer network is constructed arbitrarily, thus it is easy to tailor the topology to facilitate network coding; the nodes in a peer-to-peer network are end hosts which can perform more complex operations such as decoding and encoding than simply storing and forwarding messages. In this paper, we propose a scheme to apply network coding to peer-to-peer file sharing which employs a peer-to-peer network to distribute files resided in a web server or a file server. The scheme exploits a special type of network topology called combination network. It is proved that combination networks can achieve unbounded network coding gain measured by the ratio of network throughput with network coding to that without network coding. The scheme encodes a file into multiple messages and divides peers into multiple groups with each group responsible for relaying one of the messages. The encoding scheme is designed to satisfy the property that any subset of the messages can be used to decode the original file as long as the size of the subset is sufficiently large. To meet this requirement, we first define a deterministic linear network coding scheme which satisfies the desired property, then we connect peers in the same group to flood the corresponding message, and connect peers in different groups to distribute messages for decoding. Moreover, the scheme can be readily extended to support topology awareness to further improve system performance in terms of throughput, reliability and link stress. Our simulation results show that the new scheme can achieve 15%-20% higher throughput than Narada which does not employ network coding. In addition, it achieves good reliability and robustness to link failure or churn.
{"title":"Peer-to-Peer File Sharing Based on Network Coding","authors":"Min Yang, Yuanyuan Yang","doi":"10.1109/ICDCS.2008.52","DOIUrl":"https://doi.org/10.1109/ICDCS.2008.52","url":null,"abstract":"Network coding is a promising enhancement of routing to improve network throughput and provide high reliability. It allows a node to generate output messages by encoding its received messages. Peer-to-peer networks are a perfect place to apply network coding due to two reasons: the topology of a peer-to-peer network is constructed arbitrarily, thus it is easy to tailor the topology to facilitate network coding; the nodes in a peer-to-peer network are end hosts which can perform more complex operations such as decoding and encoding than simply storing and forwarding messages. In this paper, we propose a scheme to apply network coding to peer-to-peer file sharing which employs a peer-to-peer network to distribute files resided in a web server or a file server. The scheme exploits a special type of network topology called combination network. It is proved that combination networks can achieve unbounded network coding gain measured by the ratio of network throughput with network coding to that without network coding. The scheme encodes a file into multiple messages and divides peers into multiple groups with each group responsible for relaying one of the messages. The encoding scheme is designed to satisfy the property that any subset of the messages can be used to decode the original file as long as the size of the subset is sufficiently large. To meet this requirement, we first define a deterministic linear network coding scheme which satisfies the desired property, then we connect peers in the same group to flood the corresponding message, and connect peers in different groups to distribute messages for decoding. Moreover, the scheme can be readily extended to support topology awareness to further improve system performance in terms of throughput, reliability and link stress. Our simulation results show that the new scheme can achieve 15%-20% higher throughput than Narada which does not employ network coding. In addition, it achieves good reliability and robustness to link failure or churn.","PeriodicalId":240205,"journal":{"name":"2008 The 28th International Conference on Distributed Computing Systems","volume":"268 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134092056","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 new deadlock-free fully adaptive routing algorithm is proposed for 2-dimensional tori with only two virtual channels. The deadlock avoidance technique is presented based on a new virtual network partitioning scheme. Unlike the previous virtual network partitioning schemes, the new method allows all virtual networks to share some common virtual channels. Two virtual channels should be the lower bound for fully adaptive deadlock-free routing in tori because the dimension order routing for 2-dimensional tori also needs two virtual channels. The proposed virtual network partitioning scheme can avoid all potential deadlocks and provides fully adaptive routing. Enough theoretical analyses on the proposed virtual network partitioning scheme are presented. Sufficient simulation results are presented to demonstrate the effectiveness of the proposed algorithm by comparing with the dimension-order routing, two partially adaptive routing schemes, Duato's protocol and the load-balanced routing algorithm GOAL.
{"title":"Deadlock-Free Fully Adaptive Routing in 2-Dimensional Tori Based on New Virtual Network Partitioning Scheme","authors":"D. Xiang, Yi Pan, Qi Wang, Zhen Chen","doi":"10.1109/ICDCS.2008.106","DOIUrl":"https://doi.org/10.1109/ICDCS.2008.106","url":null,"abstract":"A new deadlock-free fully adaptive routing algorithm is proposed for 2-dimensional tori with only two virtual channels. The deadlock avoidance technique is presented based on a new virtual network partitioning scheme. Unlike the previous virtual network partitioning schemes, the new method allows all virtual networks to share some common virtual channels. Two virtual channels should be the lower bound for fully adaptive deadlock-free routing in tori because the dimension order routing for 2-dimensional tori also needs two virtual channels. The proposed virtual network partitioning scheme can avoid all potential deadlocks and provides fully adaptive routing. Enough theoretical analyses on the proposed virtual network partitioning scheme are presented. Sufficient simulation results are presented to demonstrate the effectiveness of the proposed algorithm by comparing with the dimension-order routing, two partially adaptive routing schemes, Duato's protocol and the load-balanced routing algorithm GOAL.","PeriodicalId":240205,"journal":{"name":"2008 The 28th International Conference on Distributed Computing Systems","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121475048","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}
Connected dominating set (CDS) has a wide range of applications in wireless ad hoc networks. A number of distributed algorithms for constructing a small CDS in wireless ad hoc networks have been proposed in the literature. The majority of these distributed algorithms follow a general two-phased approach. The first phase constructs a dominating set, and the second phase selects additional nodes to interconnect the nodes in the dominating set. In this paper, we prove that the approximation ratio of the two-phased algorithm in [10] is at most 7 1/3, improving upon the previous best-known approximation ratio of 7.6 due to [12]. We also propose a new two-phased approximation algorithm and prove that its approximation ratio is at most 6 7/18. Our analyses exploit an improved upper bound on the number independent points that can be packed in the neighborhood of a connected finite planar set.
{"title":"Two-Phased Approximation Algorithms for Minimum CDS in Wireless Ad Hoc Networks","authors":"P. Wan, Lixin Wang, Frances F. Yao","doi":"10.1109/ICDCS.2008.15","DOIUrl":"https://doi.org/10.1109/ICDCS.2008.15","url":null,"abstract":"Connected dominating set (CDS) has a wide range of applications in wireless ad hoc networks. A number of distributed algorithms for constructing a small CDS in wireless ad hoc networks have been proposed in the literature. The majority of these distributed algorithms follow a general two-phased approach. The first phase constructs a dominating set, and the second phase selects additional nodes to interconnect the nodes in the dominating set. In this paper, we prove that the approximation ratio of the two-phased algorithm in [10] is at most 7 1/3, improving upon the previous best-known approximation ratio of 7.6 due to [12]. We also propose a new two-phased approximation algorithm and prove that its approximation ratio is at most 6 7/18. Our analyses exploit an improved upper bound on the number independent points that can be packed in the neighborhood of a connected finite planar set.","PeriodicalId":240205,"journal":{"name":"2008 The 28th International Conference on Distributed Computing Systems","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126676974","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}
Understanding server capacity is crucial for system capacity planning, configuration, and QoS-aware resource management. Conventional stress testing approaches measure the server capacity in terms of application-level performance metrics like response time and throughput. They are limited in measurement accuracy and timeliness. In a multitier website, resource bottleneck often shifts between tiers as client access pattern changes. This makes the capacity measurement even more challenging. This paper presents a measurement approach based on hardware performance counter metrics. The approach uses machine learning techniques to infer application-level performance at each tier. A coordinated predictor is induced over individual tier models to estimate system-wide performance and identify the bottleneck when the system becomes overloaded. Experimental results demonstrate that this approach is able to achieve an overload prediction accuracy of higher than 90% for a priori known input traffic patterns and over 85% accuracy even for traffic causing frequent bottleneck shifting. It costs less than 0.5% runtime overhead for data collection and no more than 50 ms for each on-line decision.
{"title":"Online Measurement of the Capacity of Multi-Tier Websites Using Hardware Performance Counters","authors":"J. Rao, Chengzhong Xu","doi":"10.1109/ICDCS.2008.97","DOIUrl":"https://doi.org/10.1109/ICDCS.2008.97","url":null,"abstract":"Understanding server capacity is crucial for system capacity planning, configuration, and QoS-aware resource management. Conventional stress testing approaches measure the server capacity in terms of application-level performance metrics like response time and throughput. They are limited in measurement accuracy and timeliness. In a multitier website, resource bottleneck often shifts between tiers as client access pattern changes. This makes the capacity measurement even more challenging. This paper presents a measurement approach based on hardware performance counter metrics. The approach uses machine learning techniques to infer application-level performance at each tier. A coordinated predictor is induced over individual tier models to estimate system-wide performance and identify the bottleneck when the system becomes overloaded. Experimental results demonstrate that this approach is able to achieve an overload prediction accuracy of higher than 90% for a priori known input traffic patterns and over 85% accuracy even for traffic causing frequent bottleneck shifting. It costs less than 0.5% runtime overhead for data collection and no more than 50 ms for each on-line decision.","PeriodicalId":240205,"journal":{"name":"2008 The 28th International Conference on Distributed Computing Systems","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117226733","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}
Yue Zhang, B. Liu, Lei Shi, Jingnan Yao, L. Bhuyan
Efficiency and effectiveness are always the emphases of a scheduler, for both link and processor scheduling. Well-known scheduling algorithms such as surplus round robin (SRR) and elastic round robin (ERR) suffer from two fold shortcomings: 1) additional pre-processing queuing delay and post-processing resequencing delay are incurred due to the lack of short-term load-balancing; 2) bursty scheduling is caused due to blind preservation of scheduling history under non-backlogged traffic. In this paper, we propose a quantum-adaptive scheduling (QAS) algorithm, which: 1) synchronizes all the quanta in a fine-grained manner and, 2) adjusts the quanta intelligently based on processor utilization. We theoretically prove that the queuing fairness bound (QFB) for QAS is one third tighter than SRR and ERR. This result approaches the optimal value as obtained in shortest queue first (SQF) algorithm, while still maintaining O(1) complexity. Trace-driven simulations show that QAS reduces average packet delay by 18%~24% while cutting down the resequencing buffer size by more than 40% compared to SRR and ERR.
{"title":"Quantum-Adaptive Scheduling for Multi-Core Network Processors","authors":"Yue Zhang, B. Liu, Lei Shi, Jingnan Yao, L. Bhuyan","doi":"10.1109/ICDCS.2008.63","DOIUrl":"https://doi.org/10.1109/ICDCS.2008.63","url":null,"abstract":"Efficiency and effectiveness are always the emphases of a scheduler, for both link and processor scheduling. Well-known scheduling algorithms such as surplus round robin (SRR) and elastic round robin (ERR) suffer from two fold shortcomings: 1) additional pre-processing queuing delay and post-processing resequencing delay are incurred due to the lack of short-term load-balancing; 2) bursty scheduling is caused due to blind preservation of scheduling history under non-backlogged traffic. In this paper, we propose a quantum-adaptive scheduling (QAS) algorithm, which: 1) synchronizes all the quanta in a fine-grained manner and, 2) adjusts the quanta intelligently based on processor utilization. We theoretically prove that the queuing fairness bound (QFB) for QAS is one third tighter than SRR and ERR. This result approaches the optimal value as obtained in shortest queue first (SQF) algorithm, while still maintaining O(1) complexity. Trace-driven simulations show that QAS reduces average packet delay by 18%~24% while cutting down the resequencing buffer size by more than 40% compared to SRR and ERR.","PeriodicalId":240205,"journal":{"name":"2008 The 28th International Conference on Distributed Computing Systems","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131094945","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}
Large-scale distributed systems provide an attractive scalable infrastructure for network applications. However,the loosely-coupled nature of this environment can make data access unpredictable, and in the limit, unavailable. We introduce the notion of accessibility to capture both availability and performance. An increasing number of data intensive applications require not only considerations of node computation power but also accessibility for adequate job allocations. For instance, selecting a node with intolerably slow connections can offset any benefit to running on a fast node. In this paper, we present accessibility-aware resource selection techniques by which it is possible to choose nodes that will have efficient data access to remote data sources. We show that the local data access observations collected from a node's neighbors are sufficient to characterize accessibility for that node. We then present resource selection heuristics guided by this principle, and show that they significantly out perform standard techniques. The suggested techniques are also shown to be stable even under churn despite the loss of prior observations.
{"title":"Accessibility-Based Resource Selection in Loosely-Coupled Distributed Systems","authors":"Jinoh Kim, A. Chandra, J. Weissman","doi":"10.1109/ICDCS.2008.43","DOIUrl":"https://doi.org/10.1109/ICDCS.2008.43","url":null,"abstract":"Large-scale distributed systems provide an attractive scalable infrastructure for network applications. However,the loosely-coupled nature of this environment can make data access unpredictable, and in the limit, unavailable. We introduce the notion of accessibility to capture both availability and performance. An increasing number of data intensive applications require not only considerations of node computation power but also accessibility for adequate job allocations. For instance, selecting a node with intolerably slow connections can offset any benefit to running on a fast node. In this paper, we present accessibility-aware resource selection techniques by which it is possible to choose nodes that will have efficient data access to remote data sources. We show that the local data access observations collected from a node's neighbors are sufficient to characterize accessibility for that node. We then present resource selection heuristics guided by this principle, and show that they significantly out perform standard techniques. The suggested techniques are also shown to be stable even under churn despite the loss of prior observations.","PeriodicalId":240205,"journal":{"name":"2008 The 28th International Conference on Distributed Computing Systems","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132042931","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}
S. Vimercati, S. Foresti, S. Jajodia, S. Paraboschi, P. Samarati
We present a simple, yet powerful, approach for the specification and enforcement of authorizations regulating data release among data holders collaborating in a distributed computation, to ensure that query processing discloses only data whose release has been explicitly authorized. Data disclosure is captured by means of profiles, associated with each data computation, that describe the information carried by the result. We also present an algorithm that, given a query plan, determines whether it can be safely executed and produces a safe execution strategy. The main advantage of our approach is its simplicity that, without impacting expressiveness, makes it nicely interoperable with current solutions for collaborative computations in distributed database systems.
{"title":"Controlled Information Sharing in Collaborative Distributed Query Processing","authors":"S. Vimercati, S. Foresti, S. Jajodia, S. Paraboschi, P. Samarati","doi":"10.1109/ICDCS.2008.62","DOIUrl":"https://doi.org/10.1109/ICDCS.2008.62","url":null,"abstract":"We present a simple, yet powerful, approach for the specification and enforcement of authorizations regulating data release among data holders collaborating in a distributed computation, to ensure that query processing discloses only data whose release has been explicitly authorized. Data disclosure is captured by means of profiles, associated with each data computation, that describe the information carried by the result. We also present an algorithm that, given a query plan, determines whether it can be safely executed and produces a safe execution strategy. The main advantage of our approach is its simplicity that, without impacting expressiveness, makes it nicely interoperable with current solutions for collaborative computations in distributed database systems.","PeriodicalId":240205,"journal":{"name":"2008 The 28th International Conference on Distributed Computing Systems","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122942604","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：