An efficient algorithm for Hiding High Utility Sequential Patterns

作者：Bac Le^*; Duy-Tai Dinh; Van-Nam Huynh; Quang-Minh Nguyen; Fournier-Viger, Philippe

来源：International Journal of Approximate Reasoning, 2018, 95: 77-92.

DOI：10.1016/j.ijar.2018.01.005

• Data mining
• Privacy preserving data mining
• High-utility sequential pattern mining
• High-utility sequential pattern hiding

High Utility Sequential Patterns (HUSP) are a type of patterns that can be found in data collected in many domains such as business, marketing and retail. Two critical topics related to HUSP are: HUSP mining (HUSPM) and HUSP Hiding (HUSPH). HUSPM algorithms are designed to discover all sequential patterns that have a utility greater than or equal to a minimum utility threshold in a sequence database. HUSPH algorithms, by contrast, conceal all HUSP so that competitors cannot find them in shared databases. This paper focuses on HUSPH. It proposes an algorithm named HUS-Hiding to efficiently hide all HUSP. An extensive experimental evaluation is conducted on six real-life datasets to evaluate the performance of the proposed algorithm. According to the experimental results, the designed algorithm is more effective than three state-of-the-art algorithms in terms of runtime, memory usage and hiding accuracy.

• 出版日期2018-4
• 单位哈尔滨工业大学