Privacy-Enhanced Frequent Sequence Mining and Retrieval for Personalized Behavior Prediction

The widespread use of smartphones has yielded a wealth of behavioral sequence data from user interactions. These interactions offer insights into user preferences and patterns for personalized behavior prediction. However, there are some challenges in current privacy-preserving works for analyzing these data. These approaches have suboptimal service quality with smaller but longer datasets and insufficient emphasis on secure pattern storage and retrieval in real-world applications. To handle these challenges on smartphones, we propose a novel Privacy-enhanced Frequent Sequence Mining and Retrieval (PrivFSMR) framework for this scenario. Specifically, we first introduce a dynamic sequence truncation to anonymize the maximum sequence length of datasets. Following this, we design a privacy-enhanced FSM algorithm to uncover patterns, effectively reducing the privacy budget by integrating differential privacy and the Markov assumption. During the secure pattern storage, PrivFSMR employs symmetric encryption for protection and constructs an encrypted index forest for retrieval. Lastly, future behavior retrieval leverages current device information and the index forest to search similar patterns, thereby predicting potential user behaviors in the future. A comprehensive security analysis proves the PrivFSMR framework guarantees differential privacy and maintains storage and retrieval confidentiality in the lifecycle. In addition to using two publicly available datasets, we also collected a real behavior dataset within 2–4 weeks from 30 users for evaluation. Experimental results on three datasets demonstrate that PrivFSMR excels in mining frequent patterns and predicting future behaviors compared to existing approaches.