Applying dynamic verification tagging to the k-anonymity model
Ahmad Bennakhi, Mohamed A. Jeragh · 2017
This paper proposes a new way of verifying obscured data (k-anonymity model) when data mining. This is achieved by hashing the unique key along with other values by using a one-way hash function so that the attribute is referenced by the resulting value of this hash operation. The unique key or ID is hashed using multiple values; one from each attribute. When the data miner requests multiple attributes for the purpose of data mining, the new reference/verification key is derived using the attributes of the table requested in order for the data fetched to be verified by a trusted third party. The proposed solution is compatible with all extensions of the k-anonymity method, especially I-diversity.