Weave: Efficient and Expressive Oblivious Analytics at Scale

Abstract

Distributed cloud analytics can leak sensitive information through memory access patterns even when computation runs in trusted enclaves and all data and communication are encrypted. Prior protections often impose overheads logarithmic in dataset size and restrict the jobs that can run. Weave combines noise injection with hardware memory isolation to reduce network and compute overheads to a constant factor while preserving expressive analytics. It adds dataset- and workload-aware optimizations without weakening its guarantees and reduces end-to-end execution time by 4–10 times over prior state of the art on large real-world datasets.

BibTeX

@inproceedings{weave,
  author = {Soleimani, Mahdi and Jia, Grace and Khandelwal, Anurag},
  title = {{Weave: Efficient and Expressive Oblivious Analytics at Scale}},
  booktitle = {USENIX OSDI},
  month = jul,
  year = {2025}
}