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An efficient algorithm for Hiding High Utility Sequential Patterns
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DOI：10.1016/j.ijar.2018.01.005.png)
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Abstract
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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. (C) 2018 Elsevier Inc. All rights reserved.
Keywords:
Data mining
Privacy preserving data mining
High-utility sequential pattern mining
High-utility sequential pattern hiding
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