Efficiently Verifiable Computation on Encrypted Data

Fiore, Dario; Gennaro, Rosario; Pastro, Valerio

doi:10.1145/2660267.2660366

Cited by 171 publications

(120 citation statements)

References 52 publications

(89 reference statements)

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“…Gennaro et al proposed a concrete VC scheme for arbitrary circuit using Yao's [4] two-party computation scheme and Gentry's [5] fully homomorphic encryption (FHE) scheme. After that, different VC schemes using FHE were proposed [6][7][8]. They made use of various techniques to achieve verifiability.…”

Section: Verifiable Computation Verifiable Computation (Vc)mentioning

confidence: 99%

Confidentiality-Preserving Publicly Verifiable Computation Schemes for Polynomial Evaluation and Matrix-Vector Multiplication

Sun

Zhu

Qin

et al. 2018

Security and Communication Networks

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With the development of cloud services, outsourcing computation tasks to a commercial cloud server has drawn attention of various communities, especially in the Big Data era. Public verifiability offers a flexible functionality in real circumstance where the cloud service provider (CSP) may be untrusted or some malicious users may slander the CSP on purpose. However, sometimes the computational result is sensitive and is supposed to remain undisclosed in the public verification phase, while existing works on publicly verifiable computation (PVC) fail to achieve this requirement. In this paper, we highlight the property of result confidentiality in publicly verifiable computation and present confidentiality-preserving public verifiable computation (CP-PVC) schemes for multivariate polynomial evaluation and matrix-vector multiplication, respectively. The proposed schemes work efficiently under the amortized model and, compared with previous PVC schemes for these computations, achieve confidentiality of computational results, while maintaining the property of public verifiability. The proposed schemes proved to be secure, efficient, and result-confidential. In addition, we provide the algorithms and experimental simulation to show the performance of the proposed schemes, which indicates that our proposal is also acceptable in practice.

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Section: Verifiable Computation Verifiable Computation (Vc)mentioning

confidence: 99%

Confidentiality-Preserving Publicly Verifiable Computation Schemes for Polynomial Evaluation and Matrix-Vector Multiplication

Sun

Zhu

Qin

et al. 2018

Security and Communication Networks

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“…However, known constructions in this line of work are unfortunately inpractical due to their use of costly primitives, e.g., fully homomorphic encryption and verifiable computation [LTV12,FGP14]; or functional encryption and garbled circuits [GKP + 13]. Indeed, because such constructions require a single party to compute on encrypted data, even without offering verifiability they are inherently much heavier than our approach.…”

Section: Related Workmentioning

confidence: 99%

Certificate Validation in Secure Computation and Its Use in Verifiable Linear Programming

Hoogh

Schoenmakers

Veeningen

2016

Progress in Cryptology – AFRICACRYPT 2016

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Abstract. For many applications of secure multiparty computation it is natural to demand that the output of the protocol is verifiable. Verifiability should ensure that incorrect outputs are always rejected, even if all parties executing the secure computation collude. Since the inputs to a secure computation are private, and potentially the outputs are private as well, adding verifiability is in general hard and costly. In this paper we focus on privacy-preserving linear programming as a typical and practically relevant case for verifiable secure multiparty computation. We introduce certificate validation as an effective technique for achieving verifiable linear programming. Rather than verifying the computation proper, which involves many iterations of the simplex algorithm, we extend the output of the secure computation with a certificate. The certificate allows for efficient and direct validation of the correctness of the output. The overhead incurred by the computation of the certificate is marginal. For the validation of a certificate we design particularly efficient distributed-prover zero-knowledge proofs, fully exploiting the fact that we can use ElGamal encryption for this purpose, hence avoiding the use of more elaborate cryptosystems such as Paillier encryption. We also formulate appropriate security definitions for our approach, and prove security for our protocols in this model, paying special attention to ensuring properties such as input independence. By means of several experiments performed in a real multi-cloud-provider environment, we show that the overall performance for verifiable linear programming is very competitive, incurring minimal overhead compared to protocols providing no correctness guarantees at all.

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“…Note that the third party could be omitted when using verifiable computing [25,26]. However, this would induce substantial overhead which is critical on a device with limited capabilities, such as smart meters.…”

Section: Matchingmentioning

confidence: 99%

Privacy-preserving load profile matching for tariff decisions in smart grids

Unterweger

Knirsch

Eibl

et al. 2016

EURASIP J. on Info. Security

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In liberalized energy markets, matching consumption patterns to energy tariffs is desirable, but practically limited due to privacy concerns, both on the side of the consumer and on the side of the utilities. We propose a protocol through which a customer can obtain a better tariff with the help of their smart meter and a third party, based on privacy-preserving load profile matching. Our security analysis shows that the protocol preserves consumer privacy, i.e., neither the load profile nor the matching result are disclosed to the utility, unless the consumer later decides to actually purchase the tariff. In addition, the utility's load profiles used for matching remain private, allowing each utility to offer special tariffs without disclosing the associated load profiles to their competitors. Our approach is shown to have a smaller ciphertext size than homomorphic encryption in practically relevant configurations. However, matching is only possible with up to about 98 % accuracy in general and 93.5 % based on real-world load profiles, respectively. Depending on the practical requirements, two protocol parameters provide a tradeoff between matching accuracy and ciphertext size.

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Efficiently Verifiable Computation on Encrypted Data

Cited by 171 publications

References 52 publications

Confidentiality-Preserving Publicly Verifiable Computation Schemes for Polynomial Evaluation and Matrix-Vector Multiplication

Confidentiality-Preserving Publicly Verifiable Computation Schemes for Polynomial Evaluation and Matrix-Vector Multiplication

Certificate Validation in Secure Computation and Its Use in Verifiable Linear Programming

Privacy-preserving load profile matching for tariff decisions in smart grids

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