Data products are designed to generate information for public or business policy, and research and public information [1]. When data mining and database techniques are advanced, the security of classifying sensitive data in a database becomes a primary issue when providing information to data snoopers. Association rule hiding [2] investigation is a ground-breaking and mainstream apparatus for finding connections covered up in expansive informational indexes. The relationships can be shown as random item-sets or association rules. Rules are categorized as sensitive and non-sensitive depending upon the type of information it can reveal to an adversary. The rule is liable if its probability of disclosure exceeds a given threshold. PPDM is an important tool that can be applied in different fields such as healthcare, e-commerce, product research. [3]The rule is liable if the probability of disclosure exceeds the given threshold. (PPDM)privacy preserving Data Mining is the mainframe that can be applied in different fields, for example, e-healthcare, product research, e-commerce etc. There is a need to hide sensitive rules of the association. The main approach is to decrease the rules’ support or confidence. This is achieved by altering the details of the transaction. This generates side effects such as the generation of new rules and non-sensitive rules are falsely hidden [4]. This article proposes an efficient algorithm to hide association rules using the new support formula. This formula has been tested on the value of range 20 to 500. Whenever the algorithm is applied, the range is checked and the corresponding value is entered [5]. © IJSTR 2020.