Privacy-preserving information sharing within an audit firm

Alexander Kogan, Cheng Yin

Research output: Contribution to journalArticlepeer-review

Abstract

This paper explores the possibility of sharing contemporaneous firm-level information within an audit firm in a privacy-preserving manner. We develop a number of sharing schemes for utilizing contemporaneous accounting information from peer companies without violating clients’ confidentiality and observe significant improvements in both estimation accuracy and error detection performance. To satisfy different levels of privacy protection, we propose different sharing schemes by utilizing auditors’ self-generated expectations, and the results show that the benefits to auditors from only sharing self-generated estimation residuals (errors) are comparable to that from sharing predicted or actual accounting numbers. To satisfy stricter privacy concerns, we also propose a series of schemes based on sharing categorical information derived from prediction errors. Finally, we use Borda counts to analyze how the choice of the best model changes depending on the cost of errors within different experimental settings.

Original languageEnglish (US)
Pages (from-to)243-268
Number of pages26
JournalJournal of Information Systems
Volume35
Issue number2
DOIs
StatePublished - Jun 1 2021

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Software
  • Information Systems
  • Accounting
  • Human-Computer Interaction
  • Information Systems and Management
  • Management of Technology and Innovation

Keywords

  • Audit analytical procedures
  • Data privacy
  • Peer-based approach

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