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Data mining techniques have been widely applied in numerous areas and represent an important field of research. In Chapter 1, the research motivation, objectives and contributions are introduced. Chapter 2 introduces background work on data mining, share mining, utility mining, and privacy-preserving data mining. Chapter 3 describes the proposed NFP-growth method for discovering frequent itemsets. Chapters 4 through 6 explain several novel fast algorithms for share mining --- including FSM, EFSM, SuFSM, ShFSM, and DCG --- to efficiently generate all share- frequent itemsets. Furthermore, Chapter 7 presents the Isolated Items Discarding Strategy (IIDS), which can be applied to any existing level-wise share mining or utility mining method to reduce candidates and to improve its performance. Next, Chapter 8 introduces the proposed Maximum Item Conflict First (MICF) algorithm, which has a low sanitization rate and achieves a low misses cost, for hiding all restrictive itemsets. At the end of Chapters 3 through 8, the experimental results and evaluates the performance of the proposed algorithms are provided. Finally, Chapter 9 draws a summary of the dissertation.