How to Choose Suitable Secure Multiparty Computation Using Generalized SPDZ

Araki, Toshinori; Barak, Assi; Furukawa, Yoshihiro; Keller, Marcel; Ohara, Kazuma; Tsuchida, Hikaru

doi:10.1145/3243734.3278510

Cited by 9 publications

(4 citation statements)

References 6 publications

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“…and needs to produce a regularized superaccumulator as a vector of 𝛼 2𝑤-bit integers, where 𝛼 = ⌈ 2 𝑒 +𝑚 𝑤 ⌉. 4.1.1 The Overall Construction. To perform the conversion, the computation needs to determine the position within the superaccumulator where the mantissa is to be written based on exponent [𝑝], represent the mantissa as 𝛽 superaccumulator blocks, and write the blocks in the right locations without disclosing what locations within the superaccumulator those are.…”

Section: Floating-point To Superaccumulator Conversionmentioning

confidence: 99%

“…When the number of participants is not large, an alternative is to cast each local share in Z 2 as a share in Z 2 𝑘 and have the parties compute XOR of those values over Z 2 𝑘 . This approach is used in Araki et al [4] in the three-party setting with honest majority based on replicated secret sharing (RSS) that costs two consecutive multiplications. The approach of Mohassel and Rindal [40] would also require the same communication in two rounds, but the use of the three-party OT procedure in that work reduces the number of rounds to one.…”

Section: Floating-point To Superaccumulator Conversionmentioning

confidence: 99%

“…The implications are that both protocols can internally use 64-bit arithmetic and the increase in the ring size does not impact communication in bytes. Therefore, communication and the number of rounds of the protocol from [20] are also the same as those numbers for the protocol from [4]. Had we chosen 𝑘 = 32 or 𝑘 = 64, the gap in performance between our protocol and that from [20] would increase due to the need of the later to increase the communication size and use a longer data type for the computation.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Secure and Accurate Summation of Many Floating-Point Numbers

Blanton

Goodrich

Yuan

2023

PoPETs

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Motivated by the importance of floating-point computations, we study the problem of securely and accurately summing many floating-point numbers. Prior work has focused on security absent accuracy or accuracy absent security, whereas our approach achieves both of them. Specifically, we show how to implement floating-point superaccumulators using secure multi-party computation techniques, so that a number of participants holding secret shares of floating-point numbers can accurately compute their sum while keeping the individual values private.

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Section: Floating-point To Superaccumulator Conversionmentioning

confidence: 99%

Section: Floating-point To Superaccumulator Conversionmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

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Secure and Accurate Summation of Many Floating-Point Numbers

Blanton

Goodrich

Yuan

2023

PoPETs

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“…Finally, multiple cryptographic approaches have been proposed for secure aggregation, including (i) general secure multi-party computation techniques [9,23,48,49] that are built on the garbled circuits and oblivious transfer techniques; (ii) secure computation using more recent cryptographic approaches such as homomorphic encryption and its variants [3,5,15,25,31]. However, these two kinds of secure computation solutions have limitations with regards to either the large volumes of ciphertexts that need to be transferred or the inefficiency of computations involved (i.e., unacceptable computation time).…”

Section: Related Workmentioning

confidence: 99%

FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data

Baracaldo

Zhou

et al. 2021

Preprint

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Federated learning (FL) has been proposed to allow collaborative training of machine learning (ML) models among multiple parties where each party can keep its data private. In this paradigm, only model updates, such as model weights or gradients, are shared. Many existing approaches have focused on horizontal FL, where each party has the entire feature set and labels in the training data set. However, many real scenarios follow a vertically-partitioned FL setup, where a complete feature set is formed only when all the datasets from the parties are combined, and the labels are only available to a single party. Privacy-preserving vertical FL is challenging because complete sets of labels and features are not owned by one entity. Existing approaches for vertical FL require multiple peer-to-peer communications among parties, leading to lengthy training times, and are restricted to (approximated) linear models and just two parties. To close this gap, we propose FedV, a framework for secure gradient computation in vertical settings for several widely used ML models such as linear models, logistic regression, and support vector machines. FedV removes the need for peer-to-peer communication among parties by using functional encryption schemes; this allows FedV to achieve faster training times. It also works for larger and changing sets of parties. We empirically demonstrate the applicability for multiple types of ML models and show a reduction of 10%-70% of training time and 80% to 90% in data transfer with respect to the state-of-the-art approaches.

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