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).

Proofs of retrievability allow a client to store her data on a remote server (e.g., "in the cloud") and periodically execute an efficient audit protocol to check that all of the data is being maintained correctly and can be recovered from the server. For efficiency, the computation and communication of the server and client during an audit protocol should be significantly smaller than reading/transmitting the data in its entirety. Although the server is only asked to access a few locations of its storage during an audit, it must maintain full knowledge of all client data to be able to pass.Starting with the work of Juels and Kaliski (CCS '07), all prior solutions to this problem crucially assume that the client data is static and do not allow it to be efficiently updated. Indeed, they all store a redundant encoding of the data on the server, so that the server must delete a large fraction of its storage to 'lose' any actual content. Unfortunately, this means that even a single bit modification to the original data will need to modify a large fraction of the server storage, which makes updates highly inefficient. Overcoming this limitation was left as the main open problem by all prior works.In this work, we give the first solution providing proofs of retrievability for dynamic storage, where the client can perform arbitrary reads/writes on any location within her data by running an efficient protocol with the server. At any point in time, the client can execute an efficient audit protocol to ensure that the server maintains the latest version of the client data. The computation and communication complexity of the server and client in our protocols is only polylogarithmic in the size of the client's data. The starting point of our solution is to split up the data into small blocks and redundantly encode each block of data individually, so that an update inside any data block only affects a few codeword symbols. The main difficulty is to prevent the server from identifying and deleting too many codeword symbols belonging to any single data block. We do so by hiding where the various codeword symbols for any individual data block are stored on the server and when they are being accessed by the client, using the algorithmic techniques of oblivious RAM.

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.