Advances in distributed service-oriented computing and global communications have formed a strong technology push for large scale data integration among organizations and enterprises. However, concerns about data privacy become increasingly important for large scale mission-critical data integration applications. Ideally, given a database query spanning multiple private databases, we wish to compute the answer to the query without revealing any additional information of each individual database apart from the query result. In practice, we may relax this constraint to allow efficient information integration while minimizing the information disclosure. In this paper, we propose an efficient decentralized peer-to-peer protocol for supporting aggregate queries over multiple private databases while respecting the privacy constraints of participants. The paper has three main contributions. First, it formalizes the notion of loss of privacy in terms of information revealed at individual participating databases. Second, it presents a novel probabilistic decentralized protocol for top k selection across multiple private databases that minimizes the loss of privacy. Third, it experimentally evaluates the protocol in terms of its correctness, efficiency and privacy characteristics.