Fair_FM: An Improved Functional Mechanism to Provide Better Privacy Protection and Fairness Guarantee

Machine learning algorithms are currently used in various fields. The success of these algorithms is following the access of large amounts of sensitive personal data, which raises sociological concerns about privacy and fairness. At present, existing works only focus on providing fairness guarantee for the specific attribute that “can affect fairness” without considering the impact of other attributes on that attribute. Therefore, this paper takes the classical logistic regression model as the research object, uses Decision Tree Analysis and Bayesian Network to complete the analysis, and uses functional mechanism to achieve fair privacy protection. For the functional mechanism, we analyze that the Chebyshev polynomial expansion is a less error approach. Theoretical analysis and experimental results show that proposed algorithms can effectively achieve differential privacy and fairness guarantee while maintaining good utility.