21,219 research outputs found
Stochastic Differentially Private and Fair Learning
Machine learning models are increasingly used in high-stakes decision-making
systems. In such applications, a major concern is that these models sometimes
discriminate against certain demographic groups such as individuals with
certain race, gender, or age. Another major concern in these applications is
the violation of the privacy of users. While fair learning algorithms have been
developed to mitigate discrimination issues, these algorithms can still leak
sensitive information, such as individuals' health or financial records.
Utilizing the notion of differential privacy (DP), prior works aimed at
developing learning algorithms that are both private and fair. However,
existing algorithms for DP fair learning are either not guaranteed to converge
or require full batch of data in each iteration of the algorithm to converge.
In this paper, we provide the first stochastic differentially private algorithm
for fair learning that is guaranteed to converge. Here, the term "stochastic"
refers to the fact that our proposed algorithm converges even when minibatches
of data are used at each iteration (i.e. stochastic optimization). Our
framework is flexible enough to permit different fairness notions, including
demographic parity and equalized odds. In addition, our algorithm can be
applied to non-binary classification tasks with multiple (non-binary) sensitive
attributes. As a byproduct of our convergence analysis, we provide the first
utility guarantee for a DP algorithm for solving nonconvex-strongly concave
min-max problems. Our numerical experiments show that the proposed algorithm
consistently offers significant performance gains over the state-of-the-art
baselines, and can be applied to larger scale problems with non-binary
target/sensitive attributes.Comment: ICLR 202
Fair Differentially Private Federated Learning Framework
Federated learning (FL) is a distributed machine learning strategy that
enables participants to collaborate and train a shared model without sharing
their individual datasets. Privacy and fairness are crucial considerations in
FL. While FL promotes privacy by minimizing the amount of user data stored on
central servers, it still poses privacy risks that need to be addressed.
Industry standards such as differential privacy, secure multi-party
computation, homomorphic encryption, and secure aggregation protocols are
followed to ensure privacy in FL. Fairness is also a critical issue in FL, as
models can inherit biases present in local datasets, leading to unfair
predictions. Balancing privacy and fairness in FL is a challenge, as privacy
requires protecting user data while fairness requires representative training
data. This paper presents a "Fair Differentially Private Federated Learning
Framework" that addresses the challenges of generating a fair global model
without validation data and creating a globally private differential model. The
framework employs clipping techniques for biased model updates and Gaussian
mechanisms for differential privacy. The paper also reviews related works on
privacy and fairness in FL, highlighting recent advancements and approaches to
mitigate bias and ensure privacy. Achieving privacy and fairness in FL requires
careful consideration of specific contexts and requirements, taking into
account the latest developments in industry standards and techniques.Comment: Paper report for WASP module
Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach
A critical concern in data-driven decision making is to build models whose
outcomes do not discriminate against some demographic groups, including gender,
ethnicity, or age. To ensure non-discrimination in learning tasks, knowledge of
the sensitive attributes is essential, while, in practice, these attributes may
not be available due to legal and ethical requirements. To address this
challenge, this paper studies a model that protects the privacy of the
individuals sensitive information while also allowing it to learn
non-discriminatory predictors. The method relies on the notion of differential
privacy and the use of Lagrangian duality to design neural networks that can
accommodate fairness constraints while guaranteeing the privacy of sensitive
attributes. The paper analyses the tension between accuracy, privacy, and
fairness and the experimental evaluation illustrates the benefits of the
proposed model on several prediction tasks
Investigating Trade-offs in Utility, Fairness and Differential Privacy in Neural Networks
To enable an ethical and legal use of machine learning algorithms, they must
both be fair and protect the privacy of those whose data are being used.
However, implementing privacy and fairness constraints might come at the cost
of utility (Jayaraman & Evans, 2019; Gong et al., 2020). This paper
investigates the privacy-utility-fairness trade-off in neural networks by
comparing a Simple (S-NN), a Fair (F-NN), a Differentially Private (DP-NN), and
a Differentially Private and Fair Neural Network (DPF-NN) to evaluate
differences in performance on metrics for privacy (epsilon, delta), fairness
(risk difference), and utility (accuracy). In the scenario with the highest
considered privacy guarantees (epsilon = 0.1, delta = 0.00001), the DPF-NN was
found to achieve better risk difference than all the other neural networks with
only a marginally lower accuracy than the S-NN and DP-NN. This model is
considered fair as it achieved a risk difference below the strict (0.05) and
lenient (0.1) thresholds. However, while the accuracy of the proposed model
improved on previous work from Xu, Yuan and Wu (2019), the risk difference was
found to be worse
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