A processor implemented method comprising: receiving, by a data connector, data from one or more data sources for making the data consumable by one or more data buyers ; analyzing, by an inflow data analyzer, the received data to extract and process metadata of the received data ; identifying, by the inflow data analyzer, a search space comprising at least one sensitive attribute from the processed metadata based on a pre-defined knowledge base associated with the data, wherein the at least one sensitive attribute comprises one or more of binary, categorical, numerical and descriptive texts ; generating, by an adversary model generator, an adversary model by partitioning the search space into sets of buckets, each set corresponding to the at least one sensitive attribute having a privacy data associated thereof , wherein generating the adversary model comprises generating the sets of buckets based on one of (a) pre-defined ranges of values or (b) pre-defined upper and lower bounds, for each of the at least one sensitive attribute based on the pre-defined knowledge base; computing, by the adversary model generator, bucket count for each of the sets of buckets and creating bucket combinations of the buckets from the sets of buckets ; replacing, by the adversary model generator, the privacy data associated with each of the at least one sensitive attribute with a masking bucket from the buckets ; computing, by the adversary model generator, an entity count for each of the bucket combinations based on the masking bucket assigned to the privacy data ; computing, by the adversary model generator, an anonymity index based on the computed entity count and a pre-defined privacy threshold ; and sanitizing, by a data masking module, the privacy data based on the computed anonymity index to generate output data . receiving, by a data connector, data from one or more data sources for making the data consumable by one or more data buyers ; analyzing, by an inflow data analyzer, the received data to extract and process metadata of the received data ; identifying, by the inflow data analyzer, a search space comprising at least one sensitive attribute from the processed metadata based on a pre-defined knowledge base associated with the data, wherein the at least one sensitive attribute comprises one or more of binary, categorical, numerical and descriptive texts ; generating, by an adversary model generator, an adversary model by partitioning the search space into sets of buckets, each set corresponding to the at least one sensitive attribute having a privacy data associated thereof , wherein generating the adversary model comprises generating the sets of buckets based on one of (a) pre-defined ranges of values or (b) pre-defined upper and lower bounds, for each of the at least one sensitive attribute based on the pre-defined knowledge base; computing, by the adversary model generator, bucket count for each of the sets of buckets and creating bucket combinations of the buckets from the sets of buckets ; replacing, by the adversary model generator, the privacy data associated with each of the at least one sensitive attribute with a masking bucket from the buckets ; computing, by the adversary model generator, an entity count for each of the bucket combinations based on the masking bucket assigned to the privacy data ; computing, by the adversary model generator, an anonymity index based on the computed entity count and a pre-defined privacy threshold ; and sanitizing, by a data masking module, the privacy data based on the computed anonymity index to generate output data . <EOS>