Fair Differential Privacy Can Mitigate the Disparate Impact on Model Accuracy
The techniques based on the theory of differential privacy (DP) has become a standard building block in the machine learning community. DP training mechanisms offer strong guarantees that an adversary cannot determine with high confidence about the training data based on analyzing the released model, let alone any details of the instances. However, DP may disproportionately affect the underrepresented and relatively complicated classes. That is, the reduction in utility is unequal for each class. This paper proposes a fair differential privacy algorithm (FairDP) to mitigate the disparate impact on model accuracy for each class. We cast the learning procedure as a two-stage optimization problem, which integrates differential privacy with fairness. FairDP establishes a self-adaptive DP mechanism and dynamically adjusts instance influence in each class depending on the theoretical bias-variance bound. Our experimental evaluation shows the effectiveness of FairDP in mitigating the disparate impact on model accuracy among the classes on several benchmark datasets and scenarios ranging from text to vision.
Code (0)
등록된 구현이 없습니다.
Tasks
FairnessSimilar Papers 제목 키워드 기반
Private and Fair Machine Learning: Revisiting the Disparate Impact of Differentially Private SGD
Differential privacy (DP) is a prominent method for protecting information about individuals during data analysis. Training neural networks with differentially private stochastic gradient descent (DPSGD) influences the m…
DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?
Recent advances in differentially private deep learning have demonstrated that application of differential privacy, specifically the DP-SGD algorithm, has a disparate impact on different sub-groups in the population, whi…
Deep LearningFairnessRemoving Disparate Impact of Differentially Private Stochastic Gradient Descent on Model Accuracy
When we enforce differential privacy in machine learning, the utility-privacy trade-off is different w.r.t. each group. Gradient clipping and random noise addition disproportionately affect underrepresented and complex c…
Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy
Deployment of deep learning in different fields and industries is growing day by day due to its performance, which relies on the availability of data and compute. Data is often crowd-sourced and contains sensitive inform…
FairnessDisparate Impact in Differential Privacy from Gradient Misalignment
As machine learning becomes more widespread throughout society, aspects including data privacy and fairness must be carefully considered, and are crucial for deployment in highly regulated industries. Unfortunately, the …
Fairness