Search engine companies gather the “database of intentions,” the histories of their clients’ search queries. These search logs are a gold mine for researchers. Search engine companies, in any case, are careful about publishing search logs order not to disclose sensitive information. In this project, we analyse the algorithm for publishing frequent keywords, queries, and clicks of a search log. We first show how techniques that accomplish variations of k-anonymity are vulnerable to activity attacks. We at that point show that the stronger guaranteed by ε-differential privacy, unfortunately, does not give any utility to this issue. We at that point propose a calculation ZEALOUS and demonstrate to set its parameters to achieve (ε, δ)- probabilistic privacy. We then propose an algorithm of ZEALOUS with an analysis by Korolova et al. that achieves (ε’,δ’)- indistinguishability. Our project concludes with a large experimental study utilizing real applications where we think about ZEALOUS and previous work that achieves k-anonymity in search log publishing. Our results demonstrate that ZEALOUS yields comparable utility to k-anonymity while at the same time achieving much stronger privacy guarantees.