Data mining is a process that analyzes voluminous digital data in order to discover hidden but useful patterns from digital data. However, discovery of such hidden patterns has statistical meaning and may often disclose some sensitive information. As a result privacy becomes one of the prime concerns in data mining research community. Since distributed association mining discovers global association rules by combining local models from various distributed sites, breaching data privacy happens more often than it does in centralized environments. In this work we present a methodology that generates global association rules without revealing confidential inputs such as statistical properties of individual sites and yet retains high level of accuracy in resultant rules. One of the important outcomes of the proposed technique is that it reduces the overall communication costs. Performance evaluation of our proposed method shows that it reduces the communication cost significantly when we compare with some well-known distributed association rule mining algorithms. Furthermore, the global rule model generated by the proposed method is based on the exact global support of each itemsets, and hence diminished inconsistency, which indeed occurs when global models are generated from partial support count of an itemset.