An Efficiency-Preserving Transformation from Honest-Verifier Statistical Zero-Knowledge to Statistical Zero-Knowledge

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.

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

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-01986-4 ◽

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.

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PipeZK: Accelerating Zero-Knowledge Proof with a Pipelined Architecture

2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA) ◽

10.1109/isca52012.2021.00040 ◽

2021 ◽

Author(s):

Ye Zhang ◽

Shuo Wang ◽

Xian Zhang ◽

Jiangbin Dong ◽

Xingzhong Mao ◽

...

Keyword(s):

Zero Knowledge ◽

Pipelined Architecture ◽

Zero Knowledge Proof

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

Symmetry ◽

10.3390/sym13071116 ◽

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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