Enabling Secure and Efficient Data Analytics Pipeline Evolution with Trusted Execution Environment
Summary: SecCask provides end-to-end confidentiality and integrity for evolving cloud analytics pipelines through TEE-based enclave/runtime management. Reusing and caching trusted runtimes cuts cold-start costs, reducing execution time 68.4% with 29.9% overhead. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
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Authors
- 1. Haotian Gao (National University of Singapore)
- 2. Cong Yue (National University of Singapore)
- 3. Tien Tuan Anh Dinh (Deakin University)
- 4. Zhiyong Huang (National University of Singapore)
- 5. Beng Chin Ooi (National University of Singapore)
BibTeX Citation
@article{gao_vldb23,
title = {{Enabling Secure and Efficient Data Analytics Pipeline Evolution with Trusted Execution Environment}},
author = {Gao, Haotian and Yue, Cong and Dinh, Tien Tuan Anh and Huang, Zhiyong and Ooi, Beng Chin},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {10},
pages = {2485--2498},
doi = {10.14778/3603581.3603589},
url = {https://doi.org/10.14778/3603581.3603589},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,426 | NeurStore: Efficient In-database Deep Learning Model Management System | 2026 | SIGMOD | 5.0723324e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,669 | Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities | 2021 | SIGMOD | 8.2546814e-05 |
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