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VC3: Trustworthy Data Analytics in the Cloud Using SGX
Open accessAbstract
We present VC3, the first system that allows users to run distributed MapReduce computations in the cloud while keeping their code and data secret, and ensuring the correctness and completeness of their results. VC3 runs on unmodified Hadoop, but crucially keeps Hadoop, the operating system and the hyper visor out of the TCB, thus, confidentiality and integrity are preserved even if these large components are compromised. VC3 relies on SGX processors to isolate memory regions on individual computers, and to deploy new protocols that secure distributed MapReduce computations. VC3 optionally enforces region self-integrity invariants for all MapReduce code running within isolated regions, to prevent attacks due to unsafe memory reads and writes. Experimental results on common benchmarks show that VC3 performs well compared with unprotected Hadoop: VC3's average runtime overhead is negligible for its base security guarantees, 4.5% with write integrity and 8% with read/write integrity.
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Cited by
SCONE: secure Linux containers with Intel SGX
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Machine Learning Models that Remember Too Much
Song, C., Ristenpart, T., & Shmatikov, V. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134077 · Cited by 526
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Prochlo: Strong Privacy for Analytics in the Crowd
Bittau, A., Erlingsson, Ú., Maniatis, P., Mironov, I., Raghunathan, A., Lie, D., Rudominer, M., Kode, U., Tinnes, J., & Seefeld, B. (2017) · Proceedings of the 26th Symposium on Operating Systems Principles · doi:10.1145/3132747.3132769 · Cited by 378
“In particular, for the types of data center analytics that would be performed by the ESA analyzer, there have been several proposals in the recent literature [20, 57, 67].”
Method
Metadata
- Authors
- Schuster, F., Costa, M., Fournet, C., Gkantsidis, C., Peinado, M., Mainar-Ruiz, G., & Russinovich, M.
- Venue
- 2015 IEEE Symposium on Security and Privacy
- Publisher
- IEEE
- Published
- 2015-05
- Pages
- 38-54
- ISSN
- 1081-6011 (ISSN-L)
- OpenAlex
- W1569778844
- Cited by
- 595
- References
- 2
- FWCI
- 42.42
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- Schuster, F., Costa, M., Fournet, C., Gkantsidis, C., Peinado, M., Mainar-Ruiz, G., & Russinovich, M. (2015). VC3: Trustworthy Data Analytics in the Cloud Using SGX. 2015 IEEE Symposium on Security and Privacy. https://doi.org/10.1109/sp.2015.10
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