Noninteractive Zero Knowledge for NP from (Plain) Learning with Errors

2019 ◽  
pp. 89-114 ◽  
Author(s):  
Chris Peikert ◽  
Sina Shiehian
Keyword(s):  
Zero Knowledge ◽  
Learning With Errors

scholarly journals Efficient Zero-Knowledge Proofs for Commitments from Learning with Errors over Rings

2015 ◽  
pp. 305-325 ◽  
Author(s):  
Fabrice Benhamouda ◽  
Stephan Krenn ◽  
Vadim Lyubashevsky ◽  
Krzysztof Pietrzak
Keyword(s):  
Zero Knowledge ◽  
Learning With Errors

Efficient protocols for oblivious linear function evaluation from ring-LWE1

2021 ◽  
pp. 1-40
Author(s):  
Carsten Baum ◽  
Daniel Escudero ◽  
Alberto Pedrouzo-Ulloa ◽  
Peter Scholl ◽  
Juan Ramón Troncoso-Pastoriza
Keyword(s):  
Linear Function ◽  
Communication Complexity ◽  
General Purpose ◽  
Secure Computation ◽  
Function Evaluation ◽  
Zero Knowledge ◽  
Evaluation Protocol ◽  
Set Intersection ◽  
Secure Protocols ◽  
Learning With Errors

An oblivious linear function evaluation protocol, or OLE, is a two-party protocol for the function f ( x ) = a x + b, where a sender inputs the field elements a, b, and a receiver inputs x and learns f ( x ). OLE can be used to build secret-shared multiplication, and is an essential component of many secure computation applications including general-purpose multi-party computation, private set intersection and more. In this work, we present several efficient OLE protocols from the ring learning with errors (RLWE) assumption. Technically, we build two new passively secure protocols, which build upon recent advances in homomorphic secret sharing from (R)LWE (Boyle et al. in: EUROCRYPT 2019, Part II (2019) 3–33 Springer), with optimizations tailored to the setting of OLE. We upgrade these to active security using efficient amortized zero-knowledge techniques for lattice relations (Baum et al. in: CRYPTO 2018, Part II (2018) 669–699 Springer), and design new variants of zero-knowledge arguments that are necessary for some of our constructions. Our protocols offer several advantages over existing constructions. Firstly, they have the lowest communication complexity amongst previous, practical protocols from RLWE and other assumptions; secondly, they are conceptually very simple, and have just one round of interaction for the case of OLE where b is randomly chosen. We demonstrate this with an implementation of one of our passively secure protocols, which can perform more than 1 million OLEs per second over the ring Z m , for a 120-bit modulus m, on standard hardware.

Publicly auditable conditional blind signatures

2021 ◽  
Vol 29 (2) ◽  
pp. 229-271
Author(s):  
Panagiotis Grontas ◽  
Aris Pagourtzis ◽  
Alexandros Zacharakis ◽  
Bingsheng Zhang
Keyword(s):  
Proof Of Concept ◽  
Equivalence Test ◽  
Zero Knowledge ◽  
Private Key ◽  
Blind Signatures ◽  
Cryptographic Primitive ◽  
Security Properties ◽  
Designated Verifier

This work formalizes Publicly Auditable Conditional Blind Signatures (PACBS), a new cryptographic primitive that allows the verifiable issuance of blind signatures, the validity of which is contingent upon a predicate and decided by a designated verifier. In particular, when a user requests the signing of a message, blinded to protect her privacy, the signer embeds data in the signature that makes it valid if and only if a condition holds. A verifier, identified by a private key, can check the signature and learn the value of the predicate. Auditability mechanisms in the form of non-interactive zero-knowledge proofs are provided, so that a cheating signer cannot issue arbitrary signatures and a cheating verifier cannot ignore the embedded condition. The security properties of this new primitive are defined using cryptographic games. A proof-of-concept construction, based on the Okamoto–Schnorr blind signatures infused with a plaintext equivalence test is presented and its security is analyzed.

scholarly journals An access control model for the Internet of Things based on zero-knowledge token and blockchain

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Lihua Song ◽  
Xinran Ju ◽  
Zongke Zhu ◽  
Mengchen Li
Keyword(s):  
Internet Of Things ◽  
Access Control ◽  
Single Point ◽  
Control Model ◽  
Third Party ◽  
Security Level ◽  
Zero Knowledge ◽  
Access Control Model ◽  
Test Analysis ◽  
Zero Knowledge Proof

AbstractInformation security has become a hot topic in Internet of Things (IoT), and traditional centralized access control models are faced with threats such as single point failure, internal attack, and central leak. In this paper, we propose a model to improve the access control security of the IoT, which is based on zero-knowledge proof and smart contract technology in the blockchain. Firstly, we deploy attribute information of access control in the blockchain, which relieves the pressure and credibility problem brought by the third-party information concentration. Secondly, encrypted access control token is used to gain the access permission of the resources, which makes the user's identity invisible and effectively avoids attribute ownership exposure problem. Besides, the use of smart contracts solves the problem of low computing efficiency of IoT devices and the waste of blockchain computing power resources. Finally, a prototype of IoT access control system based on blockchain and zero-knowledge proof technology is implemented. The test analysis results show that the model achieves effective attribute privacy protection, compared with the Attribute-Based Access Control model of the same security level, the access efficiency increases linearly with the increase of access scale.

scholarly journals Implementation Framework for a Blockchain-Based Federated Learning Model for Classification Problems

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1116
Author(s):  
Zeba Mahmood ◽  
Vacius Jusas
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Global Perspective ◽  
Learning Technology ◽  
Classification Problems ◽  
Zero Knowledge ◽  
Privacy And Security ◽  
Implementation Framework ◽  
The Past

This paper introduces a blockchain-based federated learning (FL) framework with incentives for participating nodes to enhance the accuracy of classification problems. Machine learning technology has been rapidly developed and changed from a global perspective for the past few years. The FL framework is based on the Ethereum blockchain and creates an autonomous ecosystem, where nodes compete to improve the accuracy of classification problems. With privacy being one of the biggest concerns, FL makes use of the blockchain-based approach to ensure privacy and security. Another important technology that underlies the FL framework is zero-knowledge proofs (ZKPs), which ensure that data uploaded to the network are accurate and private. Basically, ZKPs allow nodes to compete fairly by only submitting accurate models to the parameter server and get rewarded for that. We have conducted an analysis and found that ZKPs can help improve the accuracy of models submitted to the parameter server and facilitate the honest participation of all nodes in FL.

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