  Clipped stochastic gradient descent (SGD) algorithms are among the most
popular algorithms for privacy preserving optimization that reduces the leakage
of users' identity in model training. This paper studies the convergence
properties of these algorithms in a performative prediction setting, where the
data distribution may shift due to the deployed prediction model. For example,
the latter is caused by strategical users during the training of loan policy
for banks. Our contributions are two-fold. First, we show that the
straightforward implementation of a projected clipped SGD (PCSGD) algorithm may
converge to a biased solution compared to the performative stable solution. We
quantify the lower and upper bound for the magnitude of the bias and
demonstrate a bias amplification phenomenon where the bias grows with the
sensitivity of the data distribution. Second, we suggest two remedies to the
bias amplification effect. The first one utilizes an optimal step size design
for PCSGD that takes the privacy guarantee into account. The second one uses
the recently proposed DiceSGD algorithm [Zhang et al., 2024]. We show that the
latter can successfully remove the bias and converge to the performative stable
solution. Numerical experiments verify our analysis.
