TY - GEN
T1 - Garbling, Stacked and Staggered
T2 - 27th International Conference on Theory and Application of Cryptology and Information Security, ASIACRYPT 2021
AU - Heath, David
AU - Kolesnikov, Vladimir
AU - Peceny, Stanislav
N1 - Funding Information:
Acknowledgments. This work was supported in part by NSF award #1909769, by a Facebook research award, a Cisco research award, by Georgia Tech’s IISP cybersecurity seed funding (CSF) award. This material is also based upon work supported in part by DARPA under Contract No. HR001120C0087. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of DARPA.
Funding Information:
This work was supported in part by NSF award #1909769, by a Facebook research award, a Cisco research award, by Georgia Tech?s IISP cybersecurity seed funding (CSF) award. This material is also based upon work supported in part by DARPA under Contract No. HR001120C0087. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of DARPA.
Publisher Copyright:
© 2021, International Association for Cryptologic Research.
PY - 2021
Y1 - 2021
N2 - Stacked Garbling (SGC) is a Garbled Circuit (GC) improvement that efficiently and securely evaluates programs with conditional branching. SGC reduces bandwidth consumption such that communication is proportional to the size of the single longest program execution path, rather than to the size of the entire program. Crucially, the parties expend increased computational effort compared to classic GC. Motivated by procuring a subset in a menu of computational services or tasks, we consider GC evaluation of k-out-of-n branches, whose indices are known (or eventually revealed) to the GC evaluator E. Our stack-and-stagger technique amortizes GC computation in this setting. We retain the communication advantage of SGC, while significantly improving computation and wall-clock time. Namely, each GC party garbles (or evaluates) the total of n branches, a significant improvement over the O(n· k) garblings/evaluations needed by standard SGC. We present our construction as a garbling scheme. Our technique brings significant overall performance improvement in various settings, including those typically considered in the literature: e.g. on a 1Gbps LAN we evaluate 16-out-of-128 functions ≈ 7.68 × faster than standard stacked garbling.
AB - Stacked Garbling (SGC) is a Garbled Circuit (GC) improvement that efficiently and securely evaluates programs with conditional branching. SGC reduces bandwidth consumption such that communication is proportional to the size of the single longest program execution path, rather than to the size of the entire program. Crucially, the parties expend increased computational effort compared to classic GC. Motivated by procuring a subset in a menu of computational services or tasks, we consider GC evaluation of k-out-of-n branches, whose indices are known (or eventually revealed) to the GC evaluator E. Our stack-and-stagger technique amortizes GC computation in this setting. We retain the communication advantage of SGC, while significantly improving computation and wall-clock time. Namely, each GC party garbles (or evaluates) the total of n branches, a significant improvement over the O(n· k) garblings/evaluations needed by standard SGC. We present our construction as a garbling scheme. Our technique brings significant overall performance improvement in various settings, including those typically considered in the literature: e.g. on a 1Gbps LAN we evaluate 16-out-of-128 functions ≈ 7.68 × faster than standard stacked garbling.
KW - Conditional branching
KW - Garbled circuit
KW - Stacked garbling
UR - http://www.scopus.com/inward/record.url?scp=85121934206&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85121934206&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-92075-3_9
DO - 10.1007/978-3-030-92075-3_9
M3 - Conference contribution
AN - SCOPUS:85121934206
SN - 9783030920746
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 245
EP - 274
BT - Advances in Cryptology – ASIACRYPT 2021 - 27th International Conference on the Theory and Application of Cryptology and Information Security, Proceedings, Part 2
A2 - Tibouchi, Mehdi
A2 - Wang, Huaxiong
PB - Springer
Y2 - 6 December 2021 through 10 December 2021
ER -