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Mixing arithmetic and boolean circuits to perform privacy-preserving machine learning has become increasingly popular.
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2015
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Y. Ishai, R. Kumaresan, E. Kushilevitz, and A. Paskin-Cherniavsky, “Secure computation with minimal interaction, revisited,” in CRYPTO 2015, Part II , ser. LNCS, R. Gennaro and M. J. B. Robshaw, Eds., vol. 9216. Springer, Heidelberg, Aug. 2015, pp. 359–378
2015
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M. Fredrikson, S. Jha, and T. Ristenpart, “Model inversion attacks that exploit confidence information and basic countermeasures,” in ACM CCS 2015 , I. Ray, N. Li, and C. Kruegel, Eds. ACM Press, Oct. 2015, pp. 1322–1333
2015
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P. Mohassel, M. Rosulek, and Y. Zhang, “Fast and secure three-party computation: The garbled circuit approach,” in ACM CCS 2015 , I. Ray, N. Li, and C. Kruegel, Eds. ACM Press, Oct. 2015, pp. 591–602
2015
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S. Zahur, M. Rosulek, and D. Evans, “Two halves make a whole - reducing data transfer in garbled circuits using half gates,” in EUROCRYPT 2015, Part II , ser. LNCS, E. Oswald and M. Fischlin, Eds., vol. 9057. Springer, Heidelberg, Apr. 2015, pp. 220–250
2015
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P. Pullonen and S. Siim, “Combining secret sharing and garbled circuits for efficient private IEEE 754 floating-point computations,” in FC 2015 Workshops , ser. LNCS, M. Brenner, N. Christin, B. Johnson, and K. Rohloff, Eds., vol. 8976. Springer, Heidelberg, Jan. 2015, pp. 172–183
2015
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T. Araki, J. Furukawa, Y. Lindell, A. Nof, and K. Ohara, “High-throughput semi-honest secure three-party computation with an honest majority,” in ACM CCS 2016 , E. R. Weippl, S. Katzenbeisser, C. Kruegel, A. C. Myers, and S. Halevi, Eds. ACM Press, Oct. 2016, pp. 805–817
2016
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F. Tramèr, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart, “Stealing machine learning models via prediction APIs,” in USENIX Security 2016 , T. Holz and S. Savage, Eds. USENIX Association, Aug. 2016, pp. 601–618
2016
Cited alongside, same era.
Y. Lindell, “How to simulate it - A tutorial on the simulation proof technique,” Cryptology ePrint Archive, Report 2016/046, 2016, https://eprint.iacr.org/2016/046
2016
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P. Mohassel and Y. Zhang, “SecureML: A system for scalable privacy-preserving machine learning,” in 2017 IEEE Symposium on Security and Privacy . IEEE Computer Society Press, May 2017, pp. 19–38
2017
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M. Byali, C. Hazay, A. Patra, and S. Singla, “Fast actively secure five-party computation with security beyond abort,” in ACM CCS 2019 , L. Cavallaro, J. Kinder, X. Wang, and J. Katz, Eds. ACM Press, Nov. 2019, pp. 1573–1590
2019
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D. Rotaru and T. Wood, “MArBled circuits: Mixing arithmetic and Boolean circuits with active security,” in INDOCRYPT 2019 , ser. LNCS, F. Hao, S. Ruj, and S. Sen Gupta, Eds., vol. 11898. Springer, Heidelberg, Dec. 2019, pp. 227–249
2019
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2019
Later among the works it cites.
M. Abspoel, R. Cramer, I. Damgård, D. Escudero, and C. Yuan, “Efficient information-theoretic secure multiparty computation over ℤ / p k ℤ \mathbb{Z}/p^{k}\mathbb{Z} via galois rings,” in TCC 2019, Part I , ser. LNCS, D. Hofheinz and A. Rosen, Eds., vol. 11891. Springer, Heidelberg, Dec. 2019, pp. 471–501
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J. Furukawa, Y. Lindell, A. Nof, and O. Weinstein, “High-throughput secure three-party computation for malicious adversaries and an honest majority,” in EUROCRYPT 2017, Part II , ser. LNCS, J.-S. Coron and J. B. Nielsen, Eds., vol. 10211. Springer, Heidelberg, Apr. / May 2017, pp. 225–255
2017
Cited alongside, same era.
T. Araki, A. Barak, J. Furukawa, T. Lichter, Y. Lindell, A. Nof, K. Ohara, A. Watzman, and O. Weinstein, “Optimized honest-majority MPC for malicious adversaries - breaking the 1 billion-gate per second barrier,” in 2017 IEEE Symposium on Security and Privacy . IEEE Computer Society Press, May 2017, pp. 843–862
2017
Cited alongside, same era.
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models,” in 2017 IEEE Symposium on Security and Privacy . IEEE Computer Society Press, May 2017, pp. 3–18
2017
Cited alongside, same era.
Cryptography and P. E. G. at TU Darmstadt, “ENCRYPTO Utils,” https://github.com/encryptogroup/ENCRYPTO_utils , 2017
2017
Cited alongside, same era.
P. Mohassel and P. Rindal, “ABY 3 : A mixed protocol framework for machine learning,” in ACM CCS 2018 , D. Lie, M. Mannan, M. Backes, and X. Wang, Eds. ACM Press, Oct. 2018, pp. 35–52
2018
Cited alongside, same era.
M. S. Riazi, C. Weinert, O. Tkachenko, E. M. Songhori, T. Schneider, and F. Koushanfar, “Chameleon: A hybrid secure computation framework for machine learning applications,” in ASIACCS 18 , J. Kim, G.-J. Ahn, S. Kim, Y. Kim, J. López, and T. Kim, Eds. ACM Press, Apr. 2018, pp. 707–721
2018
Cited alongside, same era.
S. D. Gordon, S. Ranellucci, and X. Wang, “Secure computation with low communication from cross-checking,” in ASIACRYPT 2018, Part III , ser. LNCS, T. Peyrin and S. Galbraith, Eds., vol. 11274. Springer, Heidelberg, Dec. 2018, pp. 59–85
2018
Cited alongside, same era.
I. Damgård, C. Orlandi, and M. Simkin, “Yet another compiler for active security or: Efficient MPC over arbitrary rings,” in CRYPTO 2018, Part II , ser. LNCS, H. Shacham and A. Boldyreva, Eds., vol. 10992. Springer, Heidelberg, Aug. 2018, pp. 799–829
2018
Cited alongside, same era.
2019
Later among the works it cites.
E. Boyle, N. Gilboa, Y. Ishai, and A. Nof, “Practical fully secure three-party computation via sublinear distributed zero-knowledge proofs,” in ACM CCS 2019 , L. Cavallaro, J. Kinder, X. Wang, and J. Katz, Eds. ACM Press, Nov. 2019, pp. 869–886
2019
Later among the works it cites.
D. Boneh, E. Boyle, H. Corrigan-Gibbs, N. Gilboa, and Y. Ishai, “Zero-knowledge proofs on secret-shared data via fully linear PCPs,” in CRYPTO 2019, Part III , ser. LNCS, A. Boldyreva and D. Micciancio, Eds., vol. 11694. Springer, Heidelberg, Aug. 2019, pp. 67–97
2019
Later among the works it cites.
M. Byali, H. Chaudhari, A. Patra, and A. Suresh, “FLASH: Fast and robust framework for privacy-preserving machine learning,” PoPETs , vol. 2020, no. 2, pp. 459–480, Apr. 2020
2020
Later among the works it cites.
H. Chaudhari, R. Rachuri, and A. Suresh, “Trident: Efficient 4PC framework for privacy preserving machine learning,” in NDSS 2020 . The Internet Society, Feb. 2020
2020
Later among the works it cites.
A. Patra and A. Suresh, “BLAZE: Blazing fast privacy-preserving machine learning,” in NDSS 2020 . The Internet Society, Feb. 2020
2020
Later among the works it cites.
N. Koti, M. Pancholi, A. Patra, and A. Suresh, “SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning,” in USENIX Security’21 , 2021, https://eprint.iacr.org/2020/592
2020
Later among the works it cites.
A. Patra, T. Schneider, A. Suresh, and H. Yalame, “ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation,” in USENIX Security’21 , 2021, https://eprint.iacr.org/2020/1225
2020
Later among the works it cites.
D. Escudero, S. Ghosh, M. Keller, R. Rachuri, and P. Scholl, “Improved primitives for MPC over mixed arithmetic-binary circuits,” in CRYPTO 2020, Part II , ser. LNCS, D. Micciancio and T. Ristenpart, Eds., vol. 12171. Springer, Heidelberg, Aug. 2020, pp. 823–852
2020
Later among the works it cites.
S. Mazloom, P. H. Le, S. Ranellucci, and S. D. Gordon, “Secure parallel computation on national scale volumes of data,” in USENIX Security 2020 , S. Capkun and F. Roesner, Eds. USENIX Association, Aug. 2020, pp. 2487–2504
2020
Later among the works it cites.
A. Dalskov, D. Escudero, and M. Keller, “Fantastic Four: Honest-Majority Four-Party Secure Computation With Malicious Security,” in USENIX Security’21 , 2021, https://eprint.iacr.org/2020/1330
2020
Later among the works it cites.
S. Ohata and K. Nuida, “Communication-efficient (client-aided) secure two-party protocols and its application,” in FC 2020 , ser. LNCS, J. Bonneau and N. Heninger, Eds., vol. 12059. Springer, Heidelberg, Feb. 2020, pp. 369–385
2020
Later among the works it cites.
P. Miao, S. Patel, M. Raykova, K. Seth, and M. Yung, “Two-sided malicious security for private intersection-sum with cardinality,” in CRYPTO 2020, Part III , ser. LNCS, D. Micciancio and T. Ristenpart, Eds., vol. 12172. Springer, Heidelberg, Aug. 2020, pp. 3–33
2020
Later among the works it cites.
B. Alon, E. Omri, and A. Paskin-Cherniavsky, “MPC with friends and foes,” in CRYPTO 2020, Part II , ser. LNCS, D. Micciancio and T. Ristenpart, Eds., vol. 12171. Springer, Heidelberg, Aug. 2020, pp. 677–706
2020
Later among the works it cites.
S. Wagh, S. Tople, F. Benhamouda, E. Kushilevitz, P. Mittal, and T. Rabin, “Falcon: Honest-majority maliciously secure framework for private deep learning,” PoPETs , vol. 2021, no. 1, pp. 188–208, Jan. 2021
2021
Closest in time.
D. Cabarcas, H. D. Vanegas, and D. E. Escudero, “Privacy-preserving machine learning for support vector machines,” Privacy-Preserving Machine Learning Workshop (PPML@CRYPTO’21), 2021
2021
Closest in time.