Differential privacy (DP) offers a theoretical upper bound on the potential privacy leakage of analgorithm, while empirical auditing establishes a practical lower bound. Auditing techniques exist forDP training algorithms. However machine learning can also be made private at inference.
Feb 14, 2024
We propose the first framework for auditing private prediction where we instantiate adversaries with varying poisoning and query capabilities. This enables us ...
The privacy analysis of private prediction can be improved, algorithms which are easier to poison lead to much higher privacy leakage, and the privacy ...
These attacks allow an adversary's to predict the user's input value based on the obfuscated output, enabling LDP-Auditor to directly evaluate the privacy ...
Oct 31, 2022 · We present a framework to statistically audit the privacy guarantee conferred by a differentially private machine learner in practice.
Using Machine Learning Techniques in Predicting Auditor Opinion
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The aim of this paper is to provide a new audit opinion prediction model for financial statements. To this end, a sample of a group of listed Egyptian companies ...
The goal of the research is to help the auditors by building a classification model that can predict the fraudulent firm on the basis the present and historical ...
Feb 20, 2024 · Predictive analytics allow auditors to catch a glimpse into probable outcomes for the company, enabling both the client and auditor to work ...
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Oct 22, 2024 · The models for predicting audit opinion analyze the variables that affect the probability of obtaining a qualified opinion.
Jun 19, 2019 · Machine learning provides the potential for significant improvements in audit speed and quality, but also entails certain risks.