zkTax: A pragmatic way to support zero-knowledge tax disclosures

• URL: http://arxiv.org/abs/2311.13008v2
• Date: Sun, 24 Mar 2024 13:54:08 GMT
• Title: zkTax: A pragmatic way to support zero-knowledge tax disclosures
• Authors: Alex Berke, Tobin South, Robert Mahari, Kent Larson, Alex Pentland,
• Abstract summary: We introduce a zero-knowledge tax disclosure system (zkTax) that allows individuals and organizations to make provable claims about select information in their tax returns. We implement a prototype with a user interface, compatible with U.S. tax forms, and demonstrate how this design could be implemented with minimal changes to existing tax infrastructure.
• Score: 7.4235470217082415
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Tax returns contain key financial information of interest to third parties: public officials are asked to share financial data for transparency, companies seek to assess the financial status of business partners, and individuals need to prove their income to landlords or to receive benefits. Tax returns also contain sensitive data such that sharing them in their entirety undermines privacy. We introduce a zero-knowledge tax disclosure system (zkTax) that allows individuals and organizations to make provable claims about select information in their tax returns without revealing additional information, which can be independently verified by third parties. The system consists of three distinct services that can be distributed: a tax authority provides tax documents signed with a public key; a Redact & Prove Service enables users to produce a redacted version of the tax documents with a zero-knowledge proof attesting the provenance of the redacted data; a Verify Service enables anyone to verify the proof. We implement a prototype with a user interface, compatible with U.S. tax forms, and demonstrate how this design could be implemented with minimal changes to existing tax infrastructure. Our system is designed to be extensible to other contexts and jurisdictions. This work provides a practical example of how distributed tools leveraging cryptography can enhance existing government or financial infrastructures, providing immediate transparency alongside privacy without system overhauls.

Related papers

• Blockchain-based Rental Documentation Management with Audit Support [5.128860078407769]
  Document management in the rental market is a critical process to ensure the accuracy of financial transactions. In Portugal, the challenges include non-compliance, lack of transparency, and bureaucratic process inefficiency. A solution based on Hyperledger Fabric, a blockchain platform, is presented for the implementation of a document management system.
  arXiv  Detail & Related papers  (2024-02-09T09:57:49Z)
• On the Potential and Limitations of Few-Shot In-Context Learning to Generate Metamorphic Specifications for Tax Preparation Software [12.071874385139395]
  Nearly 50% of taxpayers filed their individual income taxes using tax software in the U.S. in FY22. This paper formulates the task of generating metamorphic specifications as a translation task between properties extracted from tax documents.
  arXiv  Detail & Related papers  (2023-11-20T18:12:28Z)
• Give Me More Details: Improving Fact-Checking with Latent Retrieval [58.706972228039604]
  Evidence plays a crucial role in automated fact-checking. Existing fact-checking systems either assume the evidence sentences are given or use the search snippets returned by the search engine. We propose to incorporate full text from source documents as evidence and introduce two enriched datasets.
  arXiv  Detail & Related papers  (2023-05-25T15:01:19Z)
• Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest [70.02478301291264]
  Social media platforms curate access to information and opportunities, and so play a critical role in shaping public discourse. Prior studies have used black-box methods to show that these algorithms can lead to biased or discriminatory outcomes. We propose a new method for platform-supported auditing that can meet the goals of the proposed legislation.
  arXiv  Detail & Related papers  (2022-07-18T17:32:35Z)
• Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models [73.24381010980606]
  This study examines issues of algorithmic fairness in the context of systems that inform tax audit selection by the IRS. We show how the use of more flexible machine learning methods for selecting audits may affect vertical equity. Our results have implications for the design of algorithmic tools across the public sector.
  arXiv  Detail & Related papers  (2022-06-20T16:27:06Z)
• Metamorphic Testing and Debugging of Tax Preparation Software [2.185694185279913]
  We focus on an open-source tax preparation software for our case study. We develop a randomized test-case generation strategy to systematically validate the correctness of tax preparation software.
  arXiv  Detail & Related papers  (2022-05-10T16:10:10Z)
• Synthetic Disinformation Attacks on Automated Fact Verification Systems [53.011635547834025]
  We explore the sensitivity of automated fact-checkers to synthetic adversarial evidence in two simulated settings. We show that these systems suffer significant performance drops against these attacks. We discuss the growing threat of modern NLG systems as generators of disinformation.
  arXiv  Detail & Related papers  (2022-02-18T19:01:01Z)
• Trustworthy Transparency by Design [57.67333075002697]
  We propose a transparency framework for software design, incorporating research on user trust and experience. Our framework enables developing software that incorporates transparency in its design.
  arXiv  Detail & Related papers  (2021-03-19T12:34:01Z)
• Explainable Patterns: Going from Findings to Insights to Support Data Analytics Democratization [60.18814584837969]
  We present Explainable Patterns (ExPatt), a new framework to support lay users in exploring and creating data storytellings. ExPatt automatically generates plausible explanations for observed or selected findings using an external (textual) source of information.
  arXiv  Detail & Related papers  (2021-01-19T16:13:44Z)
• Detecting Transaction-based Tax Evasion Activities on Social Media Platforms Using Multi-modal Deep Neural Networks [0.0]
  This paper presents a machine learning based Regtech tool for international tax authorities to detect transaction-based tax evasion activities on social media platforms. The proposed model combines comments, hashtags and image modalities to produce the final output.
  arXiv  Detail & Related papers  (2020-07-27T13:05:39Z)
• DECO: Liberating Web Data Using Decentralized Oracles for TLS [8.586202293273507]
  DECO allows users to prove that a piece of data accessed via TLS came from a particular website. We implement three applications that are hard to achieve without DECO.
  arXiv  Detail & Related papers  (2019-09-03T03:42:46Z)

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.