LETIndex: A Secure Learned Index with TEE
Summary: LETIndex is a secure dynamic learned index for TEE databases, combining LSM-structured PGM models with adaptive prefetching. It reduces enclave context switches and disk I/O under constrained enclave memory while supporting lookups, range queries, and updates. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Shuting Cao (Tsinghua University)
- 2. Zeping Niu (Tsinghua University)
- 3. Guoliang Li (Tsinghua University)
BibTeX Citation
@article{cao_vldb25,
title = {{LETIndex: A Secure Learned Index with TEE}},
author = {Cao, Shuting and Niu, Zeping and Li, Guoliang},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {12},
pages = {5403--5406},
doi = {10.14778/3750601.3750682},
url = {https://doi.org/10.14778/3750601.3750682},
year = {2025}
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 43 | The Case for Learned Index Structures | 2018 | SIGMOD | 0.00046060254 |
| 477 | The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds | 2020 | VLDB | 0.00017851226 |
| 847 | Benchmarking Learned Indexes | 2021 | VLDB | 0.0001365768 |
| 3,464 | Building Enclave-Native Storage Engines for Practical Encrypted Databases | 2021 | VLDB | 7.3916399e-05 |
| 3,792 | Learned Index: A Comprehensive Experimental Evaluation | 2023 | VLDB | 7.1220982e-05 |
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