Frequent Itemset Mining of User’s Multi-attribute Under Local Differential Privacy

PrivBasis: Frequent Itemset Mining with Differential Privacy

The discovery of frequent itemsets can serve valuable economic and research purposes. Releasing discovered frequent itemsets, however, presents privacy challenges. In this paper, we study the problem of how to perform frequent itemset mining on transaction databases while satisfying differential privacy. We propose an approach, called PrivBasis, which leverages a novel notion called basis sets....

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,...

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 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