As storage-outsourcing services and resource-sharing networks have become popular, the problem of efficiently proving the integrity of data stored at untrusted servers has received increased attention. In the provable data possession (PDP) model, the client preprocesses the data and then sends it to an untrusted server for storage, while keeping a small amount of meta-data. The client later asks the server to prove that the stored data has not been tampered with or deleted (without downloading the actual data). However, the original PDP scheme applies only to static (or append-only) files.We present a definitional framework and efficient constructions for dynamic provable data possession (DPDP), which extends the PDP model to support provable updates to stored data. We use a new version of authenticated dictionaries based on rank information. The price of dynamic updates is a performance change from O(1) to O(log n) (or O(n ϵ log n)), for a file consisting of n blocks, while maintaining the same (or better, respectively) probability of misbehavior detection. Our experiments show that this slowdown is very low in practice (e.g., 415KB proof size and 30ms computational overhead for a 1GB file). We also show how to apply our DPDP scheme to outsourced file systems and version control systems (e.g., CVS).

As storage-outsourcing services and resource-sharing networks have become popular, the problem of efficiently proving the integrity of data stored at untrusted servers has received increased attention. In the provable data possession (PDP) model, the client preprocesses the data and then sends it to an untrusted server for storage, while keeping a small amount of meta-data. The client later asks the server to prove that the stored data has not been tampered with or deleted (without downloading the actual data). However, the original PDP scheme applies only to static (or append-only) files.We present a definitional framework and efficient constructions for dynamic provable data possession (DPDP), which extends the PDP model to support provable updates to stored data. We use a new version of authenticated dictionaries based on rank information. The price of dynamic updates is a performance change from O(1) to O(log n) (or O(n ǫ log n)), for a file consisting of n blocks, while maintaining the same (or better, respectively) probability of misbehavior detection. Our experiments show that this slowdown is very low in practice (e.g., 415KB proof size and 30ms computational overhead for a 1GB file). We also show how to apply our DPDP scheme to outsourced file systems and version control systems (e.g., CVS).

Peer-to-peer systems have been proposed for a wide variety of applications, including file-sharing, web caching, distributed computation, cooperative backup, and onion routing. An important motivation for such systems is self-scaling. That is, increased participation increases the capacity of the system. Unfortunately, this property is at risk from selfish participants. The decentralized nature of peer-to-peer systems makes accounting difficult. We show that e-cash can be a practical solution to the desire for accountability in peerto-peer systems while maintaining their ability to self-scale. No less important, e-cash is a natural fit for peer-to-peer systems that attempt to provide (or preserve) privacy for their participants. We show that e-cash can be used to provide accountability without compromising the existing privacy goals of a peer-to-peer system.We show how e-cash can be practically applied to a file sharing application. Our approach includes a set of novel cryptographic protocols that mitigate the computational and communication costs of anonymous e-cash transactions, and system design choices that further reduce overhead and distribute load. We conclude that provably secure, anonymous, and scalable peer-to-peer systems are within reach.

BlueGene/L is currently the world's fastest supercomputer. It consists of a large number of low power dual-processor compute nodes interconnected by high speed torus and collective networks. Because compute nodes do not have shared memory, MPI is the the natural programming model for this machine. The BlueGene/L MPI library is a port of MPICH2.In this paper we discuss the implementation of MPI collectives on BlueGene/L. The MPICH2 implementation of MPI collectives is based on point-to-point communication primitives. This turns out to be suboptimal for a number of reasons. Machine-optimized MPI collectives are necessary to harness the performance of BlueGene/L. We discuss these optimized MPI collectives, describing the algorithms and presenting performance results measured with targeted micro-benchmarks on real BlueGene/L hardware with up to 4096 compute nodes.

We describe different strategies a central authority, the boss, can use to distribute computation to untrusted contractors. Our problem is inspired by volunteer distributed computing projects such as SETI@home, which outsource computation to large numbers of participants. For many tasks, verifying a task's output requires as much work as computing it again; additionally, some tasks may produce certain outputs with greater probability than others. A selfish contractor may try to exploit these factors, by submitting potentially incorrect results and claiming a reward. Further, malicious contractors may respond incorrectly, to cause direct harm or to create additional overhead for result-checking.We consider the scenario where there is a credit system whereby users can be rewarded for good work and fined for cheating. We show how to set rewards and fines that incentivize proper behavior from rational contractors, and mitigate the damage caused by malicious contractors. We analyze two strategies: random double-checking by the boss, and hiring multiple contractors to perform the same job.We also present a bounty mechanism when multiple contractors are employed; the key insight is to give a reward to a contractor who catches another worker cheating. Furthermore, if we can assume that at least a small fraction h of the contractors are honest (1% − 10%), then we can provide graceful degradation for the accuracy of the system and the work the boss has to perform. This is much better than the Byzantine approach, which typically assumes h > 60%.

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