A Survey on the Applications of Zero-Knowledge Proofs
- URL: http://arxiv.org/abs/2408.00243v1
- Date: Thu, 1 Aug 2024 02:47:30 GMT
- Title: A Survey on the Applications of Zero-Knowledge Proofs
- Authors: Ryan Lavin, Xuekai Liu, Hardhik Mohanty, Logan Norman, Giovanni Zaarour, Bhaskar Krishnamachari,
- Abstract summary: Zero-knowledge computation (ZKPs) represent a revolutionary advance in computational integrity and privacy technology.
ZKPs have unique advantages in terms of universality and minimal security assumptions.
This survey focuses on the subset of ZKPs called zk-SNARKS.
- Score: 4.3871352596331255
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Zero-knowledge proofs (ZKPs) represent a revolutionary advance in computational integrity and privacy technology, enabling the secure and private exchange of information without revealing underlying private data. ZKPs have unique advantages in terms of universality and minimal security assumptions when compared to other privacy-sensitive computational methods for distributed systems, such as homomorphic encryption and secure multiparty computation. Their application spans multiple domains, from enhancing privacy in blockchain to facilitating confidential verification of computational tasks. This survey starts with a high-level overview of the technical workings of ZKPs with a focus on an increasingly relevant subset of ZKPs called zk-SNARKS. While there have been prior surveys on the algorithmic and theoretical aspects of ZKPs, our work is distinguished by providing a broader view of practical aspects and describing many recently-developed use cases of ZKPs across various domains. These application domains span blockchain privacy, scaling, storage, and interoperability, as well as non-blockchain applications like voting, authentication, timelocks, and machine learning. Aimed at both practitioners and researchers, the survey also covers foundational components and infrastructure such as zero-knowledge virtual machines (zkVM), domain-specific languages (DSLs), supporting libraries, frameworks, and protocols. We conclude with a discussion on future directions, positioning ZKPs as pivotal in the advancement of cryptographic practices and digital privacy across many applications.
Related papers
- Differential Privacy Overview and Fundamental Techniques [63.0409690498569]
This chapter is meant to be part of the book "Differential Privacy in Artificial Intelligence: From Theory to Practice"
It starts by illustrating various attempts to protect data privacy, emphasizing where and why they failed.
It then defines the key actors, tasks, and scopes that make up the domain of privacy-preserving data analysis.
arXiv Detail & Related papers (2024-11-07T13:52:11Z) - ZK-DPPS: A Zero-Knowledge Decentralised Data Sharing and Processing Middleware [3.2995127573095484]
We propose ZK-DPPS, a framework that ensures zero-knowledge communications without the need for traditional ZKPs.
Privacy is preserved through a combination of Fully Homomorphic Encryption (FHE) for computations and Secure Multi-Party Computations (SMPC) for key reconstruction.
We demonstrate the efficacy of ZK-DPPS through a simulated supply chain scenario.
arXiv Detail & Related papers (2024-10-21T01:23:37Z) - Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving ML [4.92218040320554]
Federated Learning is a privacy-preserving alternative to conventional methods that allow multiple learning clients to share model knowledge without disclosing private data.
This work applies the Fully Homomorphic Encryption scheme to a Federated Learning Neural Network architecture that integrates both classical and quantum layers.
arXiv Detail & Related papers (2024-09-14T01:23:26Z) - Provable Privacy with Non-Private Pre-Processing [56.770023668379615]
We propose a general framework to evaluate the additional privacy cost incurred by non-private data-dependent pre-processing algorithms.
Our framework establishes upper bounds on the overall privacy guarantees by utilising two new technical notions.
arXiv Detail & Related papers (2024-03-19T17:54:49Z) - Libertas: Privacy-Preserving Computation for Decentralised Personal Data Stores [19.54818218429241]
We propose a modular design for integrating Secure Multi-Party Computation with Solid.
Our architecture, Libertas, requires no protocol level changes in the underlying design of Solid.
We show how this can be combined with existing differential privacy techniques to also ensure output privacy.
arXiv Detail & Related papers (2023-09-28T12:07:40Z) - A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing.
Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data.
Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
arXiv Detail & Related papers (2023-09-27T14:38:16Z) - Deploying ZKP Frameworks with Real-World Data: Challenges and Proposed
Solutions [0.5584060970507506]
We present Fact Fortress, an end-to-end framework for designing and deploying zero-knowledge proofs of general statements.
Our solution leverages proofs of data provenance and auditable data access policies to ensure the trustworthiness of how sensitive data is handled.
arXiv Detail & Related papers (2023-07-12T18:53:42Z) - A Survey of Secure Computation Using Trusted Execution Environments [80.58996305474842]
This article provides a systematic review and comparison of TEE-based secure computation protocols.
We first propose a taxonomy that classifies secure computation protocols into three major categories, namely secure outsourced computation, secure distributed computation and secure multi-party computation.
Based on these criteria, we review, discuss and compare the state-of-the-art TEE-based secure computation protocols for both general-purpose computation functions and special-purpose ones.
arXiv Detail & Related papers (2023-02-23T16:33:56Z) - Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive
Privacy Analysis and Beyond [57.10914865054868]
We consider vertical logistic regression (VLR) trained with mini-batch descent gradient.
We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks.
arXiv Detail & Related papers (2022-07-19T05:47:30Z) - Reinforcement Learning on Encrypted Data [58.39270571778521]
We present a preliminary, experimental study of how a DQN agent trained on encrypted states performs in environments with discrete and continuous state spaces.
Our results highlight that the agent is still capable of learning in small state spaces even in presence of non-deterministic encryption, but performance collapses in more complex environments.
arXiv Detail & Related papers (2021-09-16T21:59:37Z) - Provably-secure symmetric private information retrieval with quantum
cryptography [0.0]
We propose using quantum key distribution (QKD) instead for a practical implementation, which can realise both the secure communication and shared randomness requirements.
We prove that QKD maintains the security of the SPIR protocol and that it is also secure against any external eavesdropper.
arXiv Detail & Related papers (2020-04-29T02:08:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.