This paper explores the possibility of sharing contemporaneous firm-level information within an audit firm in a privacy-preserving manner and demonstrates the benefits of doing this under the assumption that the same audit firm serves multiple clients competing in the same industry. We develop a number of sharing schemes for utilizing contemporaneous accounting information from peer companies without violating clients’ confidentiality. 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.
Research Article| June 30 2020
Privacy-preserving Information Sharing within an Audit Firm
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Alexander Kogan, Cheng Yin; Privacy-preserving Information Sharing within an Audit Firm. Journal of Information Systems doi: https://doi.org/10.2308/ISYS-2020-017
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