  Local differential privacy (LDP) provides a way for an untrusted data
collector to aggregate users' data without violating their privacy. Various
privacy-preserving data analysis tasks have been studied under the protection
of LDP, such as frequency estimation, frequent itemset mining, and machine
learning. Despite its privacy-preserving properties, recent research has
demonstrated the vulnerability of certain LDP protocols to data poisoning
attacks. However, existing data poisoning attacks are focused on basic
statistics under LDP, such as frequency estimation and mean/variance
estimation. As an important data analysis task, the security of LDP frequent
itemset mining has yet to be thoroughly examined. In this paper, we aim to
address this issue by presenting novel and practical data poisoning attacks
against LDP frequent itemset mining protocols. By introducing a unified attack
framework with composable attack operations, our data poisoning attack can
successfully manipulate the state-of-the-art LDP frequent itemset mining
protocols and has the potential to be adapted to other protocols with similar
structures. We conduct extensive experiments on three datasets to compare the
proposed attack with four baseline attacks. The results demonstrate the
severity of the threat and the effectiveness of the proposed attack.
