Data mining aims at extracting hidden information from data. The process of discovering useful patterns and relationships in the large volume of data is called data mining. The goal of the data mining process is to extract information from a data set and transform it into an understandable structure. It involves databases, data management aspects, visualization & online updating. Data mining poses a threat to information privacy. Privacy-preserving data mining hides the sensitive rules and prevents the data from being disclosed to the public. The objective is to propose a novel association rule hiding (ARH) algorithm to hide the sensitive attributes. A function is used to obtain a prior weight for each transaction, by which the order of transactions modified can be efficiently decided. Apriori is used to find the frequent itemset with minimum support and confidence. Sensitive rules are generated based on frequent itemsets and the FHSAR algorithm is used for hiding sensitive association rules. This paper analyses the dataset obtained from SPMF an open source data mining library which is prepared based on real-life data. This paper shows the effectiveness of the algorithm.