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contributor: Luca Ciprian
contributor: Philipp Jovanovic
contributor: Sebastian Steinhorst
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file-path: ../assets/Zero-Knowledge_Location_Privacy_2404.14983v2_1746716776817_0.pdf
abstract: We introduce Zero-Knowledge Location Privacy (ZKLP), enabling users to prove to third parties that they are within a specified geographical region while not disclosing their exact location. ZKLP supports varying levels of granularity, allowing for customization depending on the use case. To realize ZKLP, we introduce the first set of Zero-Knowledge Proof (ZKP) circuits that are fully compliant to the IEEE 754 standard for floating-point arithmetic. Our results demonstrate that our floating point circuits amortize efficiently, requiring only 64 constraints per operation for 2 15 single-precision floating-point multiplications. We utilize our floating point implementation to realize the ZKLP paradigm. In comparison to a baseline, we find that our optimized implementation has 15.9× less constraints utilizing single precision floating-point values, and 12.2× less constraints when utilizing double precision floating-point values. We demonstrate the practicability of ZKLP by building a protocol for privacy preserving peer-to-peer proximity testing — Alice can test if she is close to Bob by receiving a single message, without either party revealing any other information about their location. In such a setting, Bob can create a proof of (non-)proximity in 0.26 s, whereas Alice can verify her distance to about 470 peers per second.
author: Chengru Zhang
author: Jens Ernstberger
date published: 23-04-2024

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file-path: ../assets/moonmath-v1.1.5_1746894282402_0.pdf
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date published: 11-06-2024
description: A resource for anyone interested in understanding and unlocking the potential of zk-SNARKs, from beginners to experts.

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author: Dan Boneh
author: ZK Hack

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