EncChain: Enhancing Large Language Model Applications with Advanced Privacy Preservation Techniques
Summary: EncChain protects LLM applications by encrypting both knowledge bases and user interactions, while combining confidential computing and fine-grained access control. Its Python package integrates secure environments and remote attestation to verify deployment integrity. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Zhe Fu (Alibaba)
- 2. Mo Sha (Alibaba)
- 3. Yiran Li (Alibaba)
- 4. Huorong Li (Alibaba)
- 5. Yubing Ma (Alibaba)
- 6. Sheng Wang (Alibaba)
- 7. Feifei Li (Alibaba)
BibTeX Citation
@article{fu_vldb24,
title = {{EncChain: Enhancing Large Language Model Applications with Advanced Privacy Preservation Techniques}},
author = {Fu, Zhe and Sha, Mo and Li, Yiran and Li, Huorong and Ma, Yubing and Wang, Sheng and Li, Feifei},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {12},
pages = {4413--4416},
doi = {10.14778/3685800.3685888},
url = {https://doi.org/10.14778/3685800.3685888},
year = {2024}
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Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,386 | Operon: An Encrypted Database for Ownership-Preserving Data Management | 2022 | VLDB | 5.8899805e-05 |
| 11,422 | TEE-based General-purpose Computational Backend for Secure Delegated Data Processing | 2023 | SIGMOD | 5.093636e-05 |
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