Decentralized Recovery for Survivable Storage Systems (CMU-CS-04-119)
Date of Original Version
Abstract or Table of Contents
Modern society has produced a wealth of data to preserve for the long term. Some data we keep for cultural benefit, in order to make it available to future generations, while other data we keep because of legal imperatives. One way to preserve such data is to store it using survivable storage systems. Survivable storage is distinct from reliable storage in that it tolerates confidentiality failures in which unauthorized users compromise component storage servers, as well as crash failures of servers. Thus, a survivable storage system can guarantee both the availability and the confidentiality of stored data.
Research into survivable storage systems investigates the use of m-of-n threshold sharing schemes to distribute data to servers, in which each server receives a share of the data. Any m shares can be used to reconstruct the data, but any m - 1 shares reveal no information about the data. The central thesis of this dissertation is that to truly preserve data for the long term, a system that uses threshold schemes must incorporate recovery protocols able to overcome server failures, adapt to changing availability or confidentiality requirements, and operate in a decentralized manner.
To support the thesis, I present the design and experimental performance analysis of a verifiable secret redistribution protocol for threshold sharing schemes. The protocol redistributes shares of data from old to new, possibly disjoint, sets of servers, such that new shares generated by redistribution cannot be combined with old shares to reconstruct the original data. The protocol is decentralized, and does not require intermediate reconstruction of the data; thus, one does not create a central point of failure or risk the exposure of the data during protocol execution. The protocol incorporates a verification capability that enables new servers to confirm that their shares can be used to reconstruct the original data.