There have been immense advancements in collaborative data analysis. Hence the desire to keep one's own sensitive data secret is an important challenge of preserving privacy while mining in a distributed setup. There have been indeed numerous attempts to propose privacy preserving data mining algorithms. However, in order to extract the associations with respect to the time segment, the cyclic nature of the association rules must be examined. To the best of our knowledge and explorations, the current privacy preserving algorithms, do not consider the cyclic nature of the association rules. Hence, we here propose techniques with illustrative examples that help to privately decipher cyclic association rules that are common to all the participating parties. The techniques proposed by us for a multi-party scenario are based on homomorphism and Shamir's secret sharing.