Publicly Accountable Robust Multi-Party Computation

Rivinius, Marc; Reisert, Pascal; Rausch, Daniel; Küsters, Ralf

doi:10.1109/sp46214.2022.9833608

Cited by 8 publications

(11 citation statements)

References 62 publications

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“…Proof. It is easy to obtain this theorem according to our construction [23,24].The security of the Groth16 algorithm relies on the security of bilinear pairs in addition to being based on the discrete logarithm problem. The security of bilinear pairs depends on the difficulty of the Bilinear Diffie-Hellman Problem (BDHP).…”

Section: Privacymentioning

confidence: 99%

A Publicly Verifiable E-Voting System Based on Biometrics

Liu,

Han,

Tan

et al. 2023

Cryptography

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Voters use traditional paper ballots, a method limited by the factors of time and space, to ensure their voting rights are exercised; this method requires a lot of manpower and resources. Duplicate voting problems may also occur, meaning the transparency and reliability of the voting results cannot be guaranteed. With the rapid developments in science and technology, E-voting system technology is being adopted more frequently in election activities. However, E-voting systems still cannot address the verifiability of the election process; the results of a given election and the credibility of the host organization will be questioned if the election’s verifiability cannot be ensured. Elections may also pose a series of problems related to privacy, security, and so on. To address these issues, this paper presents a public, and verifiable E-voting system with hidden statistics; this system is based on commitment, zk-SNARKs, and machine learning. The system can deal with a large number of candidates, complex voting methods, and result functions in counting both hidden and public votes and can satisfy the requirements of verifiability, privacy, security, and intelligence. Our security analysis shows that our scheme achieves privacy, hidden vote counting and verifiability. Our performance evaluation demonstrates that our system has reasonable applications in real scenarios.

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Section: Privacymentioning

confidence: 99%

A Publicly Verifiable E-Voting System Based on Biometrics

Liu,

Han,

Tan

et al. 2023

Cryptography

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“…Firstly, since the SPDZ protocol utilizes a linear secret-sharing scheme, we could contemplate employing a more-efficient one, such as the lattice-based commitment scheme BDLOP mentioned in [33]. It outperforms the Pedersen commitment scheme due to its linear homomorphic property, efficient ZKPs, and support for larger message space.…”

Section: Discussionmentioning

confidence: 99%

Efficient Decision-Making Scheme Using Secure Multiparty Computation with Correctness Validation

Wang,

Liu,

Han

et al. 2023

Electronics

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In the era of big data, it is essential to securely and efficiently combine the large amounts of private data owned by different companies or organizations to make correct decisions. Secure Multiparty Computation (SMPC) works as a general cryptographic primitive, which enables distributed parties to collaboratively compute an arbitrary functionality without revealing their own private inputs. While SMPC may potentially address this task, several issues, such as computation efficiency and correctness validation, have to be overcome for practical realizations. To tackle these issues, we designed a secure and efficient decision-making scheme to enable clients to outsource data and computations to cloud servers while ensuring the integrity and confidentiality of the input and output, in addition to the correctness of the results. Moreover, we implemented our scheme based on an SMPC computation framework named MP-SPDZ. The experimental evaluation results showed that our proposed scheme is feasible and efficient for practical realizations.

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“…There are also some solutions that do not require the use of blockchain to achieve the auditability, such as AP2FL [13] by introducing a trusted auditor to check the calculation and send the check results to the participants, but this is a special assumption. In [14], the author designed a general distributed protocol that supports public accountability, which can be used to achieve the auditability of calculations, but involves complex cryptographic algorithms and requires a large overhead. In terms of privacy, there are a number of privacy-preserving aggregation schemes [15][16][17] available.…”

Section: Introductionmentioning

confidence: 99%

A Federated Learning Framework with Blockchain-Based Auditable Participant Selection

Zeng,

Zhang,

Liu

et al. 2024

CMC

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Federated learning is an important distributed model training technique in Internet of Things (IoT), in which participant selection is a key component that plays a role in improving training efficiency and model accuracy. This module enables a central server to select a subset of participants to perform model training based on data and device information. By doing so, selected participants are rewarded and actively perform model training, while participants that are detrimental to training efficiency and model accuracy are excluded. However, in practice, participants may suspect that the central server may have miscalculated and thus not made the selection honestly. This lack of trustworthiness problem, which can demotivate participants, has received little attention. Another problem that has received little attention is the leakage of participants' private information during the selection process. We will therefore propose a federated learning framework with auditable participant selection. It supports smart contracts in selecting a set of suitable participants based on their training loss without compromising the privacy. Considering the possibility of malicious campaigning and impersonation of participants, the framework employs commitment schemes and zero-knowledge proofs to counteract these malicious behaviors. Finally, we analyze the security of the framework and conduct a series of experiments to demonstrate that the framework can effectively improve the efficiency of federated learning.

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Publicly Accountable Robust Multi-Party Computation

Cited by 8 publications

References 62 publications

A Publicly Verifiable E-Voting System Based on Biometrics

A Publicly Verifiable E-Voting System Based on Biometrics

Efficient Decision-Making Scheme Using Secure Multiparty Computation with Correctness Validation

A Federated Learning Framework with Blockchain-Based Auditable Participant Selection

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