TY - GEN
T1 - VIP-Bench
T2 - 1st International Symposium on Secure and Private Execution Environment Design, SEED 2021
AU - Biernacki, Lauren
AU - Demissie, Meron Zerihun
AU - Workneh, Kidus Birkayehu
AU - Namomsa, Galane Basha
AU - Gebremedhin, Plato
AU - Andargie, Fitsum Assamnew
AU - Reagen, Brandon
AU - Austin, Todd
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Privacy-enhanced computation enables the processing of encrypted data without exposing underlying sensitive information. Such technologies are extremely promising for the advancement of data privacy, as they remove plaintexts from the attackers' reach. However, each privacy technology provides varying degrees of computational capabilities and performance overheads, creating challenges for adoption. For example, some publicly available homomorphic encryption schemes are limited in expressiveness or cannot support deep computation without incurring significant overheads. This diversity warrants a benchmark suite that can adequately assess capability and performance while supporting a variety of privacy-enhanced software architectures. We propose VIP-Bench, a benchmark suite designed with varying operational complexity and computational depth to evaluate competing privacy frameworks fairly and directly. VIP-Bench defines a forward-looking privacy-enhanced computation model and then develops under that model an array of privacy-focused benchmarks. The benchmark set is designed to flexibly cover the whole range of expected computational power and capability, enabling VIP-Bench to evaluate the privacy-enhanced computation capabilities of both today and tomorrow.
AB - Privacy-enhanced computation enables the processing of encrypted data without exposing underlying sensitive information. Such technologies are extremely promising for the advancement of data privacy, as they remove plaintexts from the attackers' reach. However, each privacy technology provides varying degrees of computational capabilities and performance overheads, creating challenges for adoption. For example, some publicly available homomorphic encryption schemes are limited in expressiveness or cannot support deep computation without incurring significant overheads. This diversity warrants a benchmark suite that can adequately assess capability and performance while supporting a variety of privacy-enhanced software architectures. We propose VIP-Bench, a benchmark suite designed with varying operational complexity and computational depth to evaluate competing privacy frameworks fairly and directly. VIP-Bench defines a forward-looking privacy-enhanced computation model and then develops under that model an array of privacy-focused benchmarks. The benchmark set is designed to flexibly cover the whole range of expected computational power and capability, enabling VIP-Bench to evaluate the privacy-enhanced computation capabilities of both today and tomorrow.
UR - http://www.scopus.com/inward/record.url?scp=85123277823&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85123277823&partnerID=8YFLogxK
U2 - 10.1109/SEED51797.2021.00026
DO - 10.1109/SEED51797.2021.00026
M3 - Conference contribution
AN - SCOPUS:85123277823
T3 - Proceedings - 2021 International Symposium on Secure and Private Execution Environment Design, SEED 2021
SP - 139
EP - 149
BT - Proceedings - 2021 International Symposium on Secure and Private Execution Environment Design, SEED 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 September 2021 through 21 September 2021
ER -