We introduce a model for provable data possession (PDP) that allows a client that has stored data at an untrusted server to verify that the server possesses the original data without retrieving it. The model generates probabilistic proofs of possession by sampling random sets of blocks from the server, which drastically reduces I/O costs. The client maintains a constant amount of metadata to verify the proof. The challenge/response protocol transmits a small, constant amount of data, which minimizes network communication. Thus, the PDP model for remote data checking supports large data sets in widely-distributed storage systems.We present two provably-secure PDP schemes that are more efficient than previous solutions, even when compared with schemes that achieve weaker guarantees. In particular, the overhead at the server is low (or even constant), as opposed to linear in the size of the data. Experiments using our implementation verify the practicality of PDP and reveal that the performance of PDP is bounded by disk I/O and not by cryptographic computation.

We introduce a model for provable data possession (PDP) that can be used for remote data checking: A client that has stored data at an untrusted server can verify that the server possesses the original data without retrieving it. The model generates probabilistic proofs of possession by sampling random sets of blocks from the server, which drastically reduces I/O costs. The client maintains a constant amount of metadata to verify the proof. The challenge/response protocol transmits a small, constant amount of data, which minimizes network communication. Thus, the PDP model for remote data checking is lightweight and supports large data sets in distributed storage systems. The model is also robust in that it incorporates mechanisms for mitigating arbitrary amounts of data corruption.We present two provably-secure PDP schemes that are more efficient than previous solutions. In particular, the overhead at the server is low (or even constant), as opposed to linear in the size of the data. We then propose a generic transformation that adds robustness to any remote data checking scheme based on spot checking. Experiments using our implementation verify the practicality of PDP and reveal that the performance of PDP is bounded by disk I/O and not by cryptographic computation. Finally, we conduct an in-depth experimental evaluation to study the tradeoffs in performance, security, and space overheads when adding robustness to a remote data checking scheme.

We provide a design and implementation of self-protecting electronic medical records (EMRs) using attribute-based encryption on mobile devices. Our system allows healthcare organizations to export EMRs to locations outside of their trust boundary. In contrast to previous approaches, our solution is designed to maintain EMR availability even when providers are o✏ine, i.e., where network connectivity is not available. To balance the needs of emergency care and patient privacy, our system is designed to provide fine-grained encryption and is able to protect individual items within an EMR, where each encrypted item may have its own access control policy. We implemented a prototype system using a new key-and ciphertext-policy attribute-based encryption library that we developed. Our implementation, which includes an iPhone app for storing and managing EMRs offline, allows for flexible and automated policy generation. An evaluation of our design shows that our ABE library performs well, has acceptable storage requirements, and is practical and usable on modern smartphones.

The ext3cow file system, built on the popular ext3 file system, provides an open-source file versioning and snapshot platform for compliance with the versioning and audtitability requirements of recent electronic record retention legislation. Ext3cow provides a time-shifting interface that permits a real-time and continuous view of data in the past. Time-shifting does not pollute the file system namespace nor require snapshots to be mounted as a separate file system. Further, ext3cow is implemented entirely in the file system space and, therefore, does not modify kernel interfaces or change the operation of other file systems. Ext3cow takes advantage of the fine-grained control of on-disk and in-memory data available only to a file system, resulting in minimal degradation of performance and functionality. Experimental results confirm this hypothesis; ext3cow performs comparably to ext3 on many benchmarks and on trace-driven experiments.

We introduce and analyze a general framework for authentically binding data to a location while providing strong assurances against cloud storage providers that (either accidentally or maliciously) attempt to re-locate cloud data. We then evaluate a preliminary solution in this framework that combines constraint-based host geolocation with proofs of data possession, called constraint-based data geolocation (CBDG). We evaluate CBDG using a combination of experiments with PlanetLab and real cloud storage services, demonstrating that we can bind fetched data to the location originally hosting it with high precision. We geolocate data hosted on the majority of our PlanetLab targets to regions no larger than 118,000 km 2 , and we geolocate data hosted on Amazon S3 to an area no larger than 12,000 km 2 , su ciently small to identify the state or service region.