On differentially private frequent itemset mining
نویسندگان
چکیده
منابع مشابه
On differentially private frequent itemset mining
We consider differentially private frequent itemset mining. We begin by exploring the theoretical difficulty of simultaneously providing good utility and good privacy in this task. While our analysis proves that in general this is very difficult, it leaves a glimmer of hope in that our proof of difficulty relies on the existence of long transactions (that is, transactions containing many items)...
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Frequent sets play an important role in many Data Mining tasks that try to search interesting patterns from databases, such as association rules, sequences, correlations, episodes, classifiers and clusters. FrequentItemsets Mining (FIM) is the most well-known techniques to extract knowledge from dataset. In this paper differential privacy aims to get means to increase the accuracy of queries fr...
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Frequent itemsets mining finds sets of items that frequently appear together in a database. However, publishing this information might have privacy implications. Accordingly, in this paper we are considering the problem of guaranteeing differential privacy for frequent itemsets mining. We measure the utility of a frequent itemsets mining algorithm by its likelihood to produce a complete and sou...
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Recently there has been a growing interest in designing differentially private data mining algorithms. A variety of algorithms have been proposed for mining frequent itemsets. Frequent itemset mining (FIM) is one of the most fundamental problems in data mining. It has practical importance in a wide range of application areas such as decision support, web usage mining, bioinformatics, etc. In th...
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Frequent itemset mining is an important building block in many data mining applications like market basket analysis, recommendation, web-mining, fraud detection, and gene expression analysis. In many of them, the datasets being mined can easily grow up to hundreds of gigabytes or even terabytes of data. Hence, efficient algorithms are required to process such large amounts of data. In recent ye...
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ژورنال
عنوان ژورنال: Proceedings of the VLDB Endowment
سال: 2012
ISSN: 2150-8097
DOI: 10.14778/2428536.2428539