Cloud Storage Integrity at Scale: A Case for Dynamic Hash Trees
<|reference_start|>Cloud Storage Integrity at Scale: A Case for Dynamic Hash Trees: Merkle hash trees are the state-of-the-art method to protect the integrity of storage systems. However, using a hash tree can severely degrade performance, and prior works optimizing them have yet to yield a concrete understanding of the scalability of certain designs in the context of large-scale cloud storage systems. In this paper, we take a first-principles approach to analyzing hash tree performance for storage by introducing a definition of an optimal hash tree and a principled methodology for evaluating hash tree designs. We show that state-of-the-art designs are not scalable; they incur up to 40.1X slowdowns over an insecure baseline and deliver <50% of optimal performance across various experiments. We then exploit the characteristics of optimal hash trees to design Dynamic Hash Trees (DHTs), hash trees that can adapt to workload patterns on-the-fly, delivering >95% of optimal read and write performance and up to 4.2X speedups over the state-of-the art. Our novel methodology and DHT design provides a new foundation in the search for integrity mechanisms that can operate efficiently at scale.<|reference_end|>
@article{burke2024cloud,
    title={Cloud Storage Integrity at Scale: A Case for Dynamic Hash Trees},
    author={Quinn Burke, Ryan Sheatsley, Rachel King, Michael Swift, Patrick
  McDaniel},
    journal={arXiv preprint arXiv:2405.03830},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03830},
    primaryClass={cs.CR}
}