Online service platforms (OSPs), such as search engines, news-websites, ad-providers, etc., serve highly pe rsonalized content to the user, based on the profile extracted from his history with the OSP. Although personalization (generally) leads to a better user experience, it also raises privacy concerns for the user---he does not know what is present in his profile and more importantly, what is being used to per sonalize content for him. In this paper, we capture OSP's personalization for an user in a new data structure called the person alization vector ($η$), which is a weighted vector over a set of topics, and present techniques to compute it for users of an OSP. Our approach treats OSPs as black-boxes, and extracts $η$ by mining only their output, specifical ly, the personalized (for an user) and vanilla (without any user information) contents served, and the differences in these content. We formulate a new model called Latent Topic Personalization (LTP) that captures the personalization vector into a learning framework and present efficient inference algorithms for it. We do extensive experiments for search result personalization using both data from real Google users and synthetic datasets. Our results show high accuracy (R-pre = 84%) of LTP in finding personalized topics. For Google data, our qualitative results show how LTP can also identifies evidences---queries for results on a topic with high $η$ value were re-ranked. Finally, we show how our approach can be used to build a new Privacy evaluation framework focused at end-user privacy on commercial OSPs.