DPSGD

DPSGD stands for Differentially Private Stochastic Gradient Descent. It is an algorithm used in machine learning to train models while preserving the privacy of individual data points used in the training set. The core idea behind DPSGD is to add carefully calibrated noise to the gradients computed during the training process. This noise makes it difficult for an adversary to infer information about any single data point from the model's parameters.

Stochastic Gradient Descent (SGD) is a common optimization algorithm used to train machine learning models. It iteratively updates the model's parameters by taking steps in the direction of the negative gradient of the loss function, calculated on a small batch of data. DPSGD modifies this process by introducing a privacy mechanism.

The privacy guarantee in DPSGD is quantified using differential privacy, a mathematical framework that provides strong privacy assurances. Specifically, DPSGD aims to achieve (epsilon, delta)-differential privacy. Epsilon represents the privacy loss, with smaller values indicating stronger privacy. Delta represents the probability that the privacy guarantee is violated.

The implementation of DPSGD typically involves several steps. First, gradients are computed for each individual data point within a mini-batch. Then, these gradients are clipped to a maximum norm to limit the influence of any single data point. Finally, Gaussian noise is added to the average of the clipped gradients before they are used to update the model's parameters. This noise addition is crucial for achieving differential privacy. While DPSGD provides privacy, it often comes at the cost of reduced model accuracy compared to standard SGD due to the introduced noise. Researchers continue to explore ways to mitigate this accuracy degradation.