Fast Fully Oblivious Compaction and Shuffling
Sajin Sasy, Aaron Johnson, Ian Goldberg
2022年份
13被引次数
15顶会引用
摘要
Several privacy-preserving analytics frameworks have been proposed that use trusted execution environments (TEEs) like Intel SGX. Such frameworks often use compaction and shuffling as core primitives. However, due to advances in TEE side-channel attacks, these primitives, and the applications that use them, should be fully oblivious; that is, perform instruction sequences and memory accesses that do not depend on the secret inputs. Such obliviousness would eliminate the threat of leaking private information through memory or timing side channels, but achieving it naively can result in a significant performance cost.
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引用它的顶会 Paper15
- Doquet: Differentially Oblivious Range and Join Queries with Private Data StructuresLina Qiu, Georgios Kellaris, Nikos Mamoulis, Kobbi Nissim 等VLDB 2023 · 被引用 14 次
- Waks-On/Waks-Off: Fast Oblivious Offline/Online Shuffling and Sorting with Waksman NetworksSajin Sasy, Aaron Johnson, Ian GoldbergCCS 2023 · 被引用 6 次
- Secure Sampling for Approximate Multi-party Query ProcessingQiyao Luo, Yilei Wang, Ke Yi, Sheng Wang 等SIGMOD 2024 · 被引用 3 次
- SPECIAL: Synopsis Assisted Secure Collaborative AnalyticsChenghong Wang, Lina Qiu, Johes Bater, Yukui LuoVLDB 2025 · 被引用 3 次
- sigma-rs: A Modular Approach for Keyed-Verification Anonymous CredentialsMichele Orrù, Lindsey Tulloch, Victor Snyder-Graf, Ian GoldbergUSENIX Security 2026 · 被引用 2 次
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