Recurrence Analysis of Integrally Private Support Vector Machine

Integral privacy, an alternative to k-Anonymity and differential privacy, focuses on creating ambiguity for intruders by considering models generated from diverse datasets as privacy-preserving. Integral privacy calls such models as recurring models. While prior research has primarily explored recurrence in deep learning models which have large parameter space, this paper addresses the recurrence analysis of a typical machine learning model with relatively small parameter space like Support Vector Machine (SVM). Models having small parameter space can have significant impact due to the presence and absence of a datapoint. Due to this reason, one may intuitively consider that their probability to recur is low. We challenge this hypothesis with the recurrence analysis of SVM models trained with mean samplers like stochastic gradient descent. We find that under constrained environment SVM models recurs with high probability. This research enhances our understanding of privacy-preserving models in the context of SVMs, providing valuable insights into their privacy guarantees.