An Effective Differential Privacy Transaction Data Publication Strategy

For the past few years, privacy preserving data publishing which can securely publish data for analysis purpose has attracted considerable research interests in database community. However, the sparsity of the transaction data burdens the trade-off between privacy protection and enough utility maintaining. Most existing data publishing methods for transaction data are based on partition-based anonymity models, for example k-anonymity. They depend on background knowledge from the attack, and the published data cannot meet the needs of the analysis tasks. In contrast, differential privacy is a strong privacy model which provides strong privacy guarantees independent of an adversary’s background knowledge and also maintains high utility for the published data. Because most existing methods and privacy models cannot accommodate both utility and privacy security of the data, in this paper, an application-oriented TDPS(transaction data publish strategy) is proposed, which is based on differential privacy and compressive sensing. Firstly, an entire Trie tree is constructed for a transaction database. Secondly, based on compressive sensing, we get a noisy Trie tree by adding the differential privacy noisy to the Trie tree. Finally, the frequent itemset mining task is performed on the noisy Trie tree. Theoretical analysis and experimental results demonstrate that the TDPS can preserve privacy of the sensitive data well, meanwhile maintain better data utility.