Dynamic provable data possession

TLDR

Provable data possession (PDP) lets a client store data on an untrusted server, keep only small metadata, and later request proofs that the data remains intact without downloading it. This work extends PDP to dynamic data, enabling efficient integrity proofs for files that can be updated on untrusted servers. The authors employ rank‑based authenticated dictionaries and show how the DPDP scheme can be integrated into outsourced file systems and version control systems such as CVS. Dynamic updates incur only a logarithmic overhead compared to O(1) for static files, and experiments show a 415 KB proof size and 30 ms overhead for a 1 GB file, indicating negligible practical slowdown.

Abstract

We consider the problem of efficiently proving the integrity of data stored at untrusted servers. 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(logn) (or O(nelog 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).