The use of health care data is constrained by legitimate privacy concerns in multi-center (or distributed data) studies, preventing the creation of a single dataset of covariate information. In this study, we explore the application of specimen pooling as a privacy-preserving tool for estimating hazard ratio (HR) of a covariate for a time to event outcome. By utilizing the equivalence between the Cox proportional hazards (PH) model and conditional logistic model, we estimate the HRs using only the aggregate covariates without revealing individual participant’s data. The approach is demonstrated via extensive simulation studies and with the SMART dataset where pooled estimates of HRs of a Cox PH model are shown to be similar to individual level (unpooled) covariate effects. Hence, the approach preserves privacy while providing valid estimates of relevant HRs. Additionally, effect modifiers can be accommodated and consistently estimated.