Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response

Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response

Frequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data. There are increasing concerns about the privacy problem in the frequent itemset mining. Some works have been proposed to handle this kind of problem. In this paper, we introduce a personalized privacy problem, in which different attributes may need differen...

Privacy Preserving Outsourcing for Frequent Itemset Mining

Cloud computing uses the paradigm of data mining-as-a-service. A company/store lacking in mining expertise can outsource its mining needs to a service provider (server). The item-set of the outsourced database are the private property of the data owner. To protect this corporate privacy, the data owner encrypts the data and sends to the server. Based on the mining queries sent from client side,...

Privacy-Preserving Frequent Itemset Mining for Sparse and Dense Data

Frequent itemset mining is a task that can in turn be used for other purposes such as associative rule mining. One problem is that the data may be sensitive, and its owner may refuse to give it for analysis in plaintext. There exist many privacy-preserving solutions for frequent itemset mining, but in any case enhancing the privacy inevitably spoils the efficiency. Leaking some less sensitive i...

Privacy-preserving Histogram Computation and Frequent Itemset Mining with Sharemind

Privacy Preserving Private Frequent Itemset Mining via Smart Splitting

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...