tailieunhanh - An Audit Environment for Outsourcing of Frequent Itemset Mining

The primary goal of the ILO is to promote opportunities for women and men to obtain decent and productive work in conditions of freedom, equity, security and human dignity. The ILO considers gender equality as a key element in its vision of Decent Work for All Women and Men for social and institutional change to bring about equity and growth. Reporting directly to the Director-General, the ILO Bureau for Gender Equality acts as a catalyst and adviser for ILO Constituents and staff to be more effective in increasing gender equality in the world of work, thereby advancing decent work as. | An Audit Environment for Outsourcing of Frequent Itemset Mining W. K. Wong The University of Hong Kong wkwong2@ David W. Cheung The University of Hong Kong dcheung@ Edward Hung The Hong Kong Polytechnic University csehung@ Ben Kao The University of Hong Kong kao@ Nikos Mamoulis The University of Hong Kong nikos@ ABSTRACT Finding frequent itemsets is the most costly task in association rule mining. Outsourcing this task to a service provider brings several benefits to the data owner such as cost relief and a less commitment to storage and computational resources. Mining results however can be corrupted if the service provider i is honest but makes mistakes in the mining process or ii is lazy and reduces costly computation returning incomplete results or iii is malicious and contaminates the mining results. We address the integrity issue in the outsourcing process . how the data owner verifies the correctness of the mining results. For this purpose we propose and develop an audit environment which consists of a database transformation method and a result verification method. The main component of our audit environment is an artificial itemset planting AIP technique. We provide a theoretical foundation on our technique by proving its appropriateness and showing probabilistic guarantees about the correctness of the verification process. Through analytical and experimental studies we show that our technique is both effective and efficient. 1. INTRODUCTION Association rule mining discovers correlated itemsets that occur frequently in a transactional database. A variety of efficient algorithms for mining association rules have been proposed 1 2 4 . The problem can be divided into two subproblems i computing the set of frequent itemsets and ii computing the set of association rules based on the mined frequent itemsets. While the latter problem rule generation is computationally inexpensive the problem of mining .

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