Improved Multiplication Triple Generation over Rings via RLWE-Based AHE

Rathee, Deevashwer; Schneider, Thomas; Shukla, K. K.

doi:10.1007/978-3-030-31578-8_19

Cited by 18 publications

(8 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different from the state-of-the-art solutions proposed in [15,20], where a party (say, Alice) first splits her private input x A to a share vector x A = (x A,1 , • • • , x A,l ) and then invokes an AHE to compute shares s A with Bob whose input is x B and x B = (x B,1 , • • • , x B,l ) and output is s B such that x A • x B =s A + s B , we formalize Beaver triple generators in the context of 2-party shared scalar product protocol to get s A + s B = x A • x B and then Alice (Bob resp.,) dispenses her shares…”

Section: This Workmentioning

confidence: 97%

“…The Beaver triples deployed in the SPDZ framework are originally constructed from somewhat homomorphic encryptions [9]. Since the computation cost of ciphertext multiplications is high if one leverages somewhat homomorphic encryptions, more and more researchers are considering alternative constructions such as oblivious-transfers [14], additively homomorphic encryptions [15,20], multiplicatively homomorphic encryption [27] and multiplicatively homomorphic key management system [28], where a proof of equivalence between constructions leveraging multiplicatively homomorphic encryption and multiplicatively homomorphic key management system has been proposed. In this work, an efficient solution for generating Beaver triples starting from asymmetric oblivious scalar product protocol is proposed and analyzed.…”

Section: This Workmentioning

confidence: 99%

See 1 more Smart Citation

Highly Scalable Beaver Triple Generator from Additive-only Homomorphic Encryption

Zhu¹

2022

Preprint

View full text Add to dashboard Cite

In a convolution neural network, a composition of linear scalar product, non-linear activation function and maximum pooling computations are intensively invoked. As such, to design and implement privacy-preserving, high efficiency machine learning mechanisms, one highly demands a practical crypto tool for secure arithmetic computations. SPDZ, an interesting framework of secure multi-party computations is a promising technique deployed for industry-scale machine learning development if one is able to generate Beaver (multiplication) triple offline efficiently. This paper studies secure yet efficient Beaver triple generators leveraging privacy-preserving scalar product protocols which in turn can be constructed from additive-only homomorphic encryptions(AHEs). Different from the state-of-the-art solutions, where a party first splits her private input into a shared vector and then invokes an AHE to compute scalar product of the shared vectors managed by individual MPC server, we formalize Beaver triple generators in the context of 2-party shared scalar product protocol and then dispense the generated shares to MPC servers. As such, the protocol presented in this paper can be viewed as a dual construction of the state-of-the-art AHE based solutions. Furthermore, instead of applying the Paillier encryption as a basis of our previous constructions or inheriting from somewhat homomorphic encryptions, we propose an alternative construction of AHE from polynomial ring learning with error (RLWE) which results in an efficient implementation of Beaver triple generators.

show abstract

Section: This Workmentioning

confidence: 97%

Section: This Workmentioning

confidence: 99%

Highly Scalable Beaver Triple Generator from Additive-only Homomorphic Encryption

Zhu¹

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Secure addition (respectively, XORing in B) can be executed locally, that is without communication between the parties. Secure multiplication (respectively, AND in B) is done in an interactive protocol among the two parties using so-called multiplication triples [53][54][55]. Using only addition and multiplication (similarly, AND and XOR) arbitrary functions can be calculated.…”

Section: Secure Multi-party Computation (Mpc)mentioning

confidence: 99%

SPIKE: Secure and Private Investigation of the Kidney Exchange problem

Birka¹,

Hamacher²,

Kussel³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Background:The kidney exchange problem (KEP) addresses the matching of patients in need for a replacement organ with compatible living donors. Ideally many medical institutions should participate in a matching program to increase the chance for successful matches. However, to fulfill legal requirements current systems use complicated policy-based data protection mechanisms that effectively exclude smaller medical facilities to participate. Employing secure multi-party computation (MPC) techniques provides a technical way to satisfy data protection requirements for highly sensitive personal health information while simultaneously reducing the regulatory burdens. Results:We have designed, implemented, and benchmarked SPIKE, a secure MPC-based privacy-preserving KEP which computes a solution by finding matching donor-recipient pairs in a graph structure. SPIKE matches 40 pairs in cycles of length 2 in less than 4 minutes and outperforms the previous state-of-the-art protocol by a factor of 400× in runtime while providing medically more robust solutions. Conclusions:We show how to solve the KEP in a robust and privacy-preserving manner achieving practical performance. The usage of MPC techniques fulfills many data protection requirements on a technical level, allowing smaller health care providers to directly participate in a kidney exchange with reduced legal processes.

show abstract

“…Secure addition (respectively, XORing in B ) can be executed locally, that is without communication between the parties. Secure multiplication (respectively, AND in B ) is done in an interactive protocol among the two parties using so-called multiplication triples [61,69,70]. Using only addition and multiplication (similarly, AND and XOR ) arbitrary functions can be calculated.…”

Section: Boolean and Arithmetic Secret Sharingmentioning

confidence: 99%

SPIKE: secure and private investigation of the kidney exchange problem

Birka

Hamacher

Kussel

et al. 2022

BMC Med Inform Decis Mak

Self Cite

View full text Add to dashboard Cite

Background The kidney exchange problem (KEP) addresses the matching of patients in need for a replacement organ with compatible living donors. Ideally many medical institutions should participate in a matching program to increase the chance for successful matches. However, to fulfill legal requirements current systems use complicated policy-based data protection mechanisms that effectively exclude smaller medical facilities to participate. Employing secure multi-party computation (MPC) techniques provides a technical way to satisfy data protection requirements for highly sensitive personal health information while simultaneously reducing the regulatory burdens. Results We have designed, implemented, and benchmarked SPIKE, a secure MPC-based privacy-preserving KEP protocol which computes a locally optimal solution by finding matching donor–recipient pairs in a graph structure. SPIKE matches 40 pairs in cycles of length 2 in less than 4 min and outperforms the previous state-of-the-art protocol by a factor of $$400\times$$ 400 × in runtime while providing medically more robust solutions. Conclusions We show how to solve the KEP in a robust and privacy-preserving manner achieving significantly more practical performance than the current state-of-the-art (Breuer et al., WPES’20 and CODASPY’22). The usage of MPC techniques fulfills many data protection requirements on a technical level, allowing smaller health care providers to directly participate in a kidney exchange with reduced legal processes. As sensitive data are not leaving the institutions’ network boundaries, the patient data underlie a higher level of protection than in the currently employed (centralized) systems. Furthermore, due to reduced legal barriers, the proposed decentralized system might be simpler to implement in a transnational, intereuropean setting with mixed (national) data protecion laws.

show abstract

Improved Multiplication Triple Generation over Rings via RLWE-Based AHE

Cited by 18 publications

References 22 publications

Highly Scalable Beaver Triple Generator from Additive-only Homomorphic Encryption

Highly Scalable Beaver Triple Generator from Additive-only Homomorphic Encryption

SPIKE: Secure and Private Investigation of the Kidney Exchange problem

SPIKE: secure and private investigation of the kidney exchange problem

Contact Info

Product

Resources

About