In targeted (or behavioral) advertising, users' behaviors are tracked over time in order to customize served ads to their interests. This creates serious privacy concerns since for the purpose of pro ling, private information is collected and centralized by a limited number of companies. Despite claims that this information is secure, there is a potential for this information to be leaked through the customized services these companies are o ering. In this paper, we show that targeted ads expose users' private data not only to ad providers but also to any entity that has access to users' ads. We propose a methodology to lter targeted ads and infer users' interests from them. We show that an adversary that has access to only a small number of websites containing Google ads can infer users' interests with an accuracy of more than 79% (Precision) and reconstruct as much as 58% of a Google Ads pro le in general (Recall). This paper is, to our knowledge, the first work that identi es and quantifi es information leakage through ads served in targeted advertising.