Distortion Based Algorithms for Privacy Preserving Frequent Item Set Mining
نویسندگان
چکیده
منابع مشابه
Distortion Based Algorithms For Privacy Preserving Frequent Item Set Mining
Data mining services require accurate input data for their results to be meaningful, but privacy concerns may influence users to provide spurious information. In order to preserve the privacy of the client in data mining process, a variety of techniques based on random perturbation of data records have been proposed recently. We focus on an improved distortion process that tries to enhance the ...
متن کاملSimple Algorithms for Frequent Item Set Mining
In this paper I introduce SaM, a split and merge algorithm for frequent item set mining. Its core advantages are its extremely simple data structure and processing scheme, which not only make it quite easy to implement, but also very convenient to execute on external storage, thus rendering it a highly useful method if the transaction database to mine cannot be loaded into main memory. Furtherm...
متن کاملComparison of Frequent Item Set Mining Algorithms
Frequent item sets mining plays an important role in association rules mining. Over the years, a variety of algorithms for finding frequent item sets in very large transaction databases have been developed. The main focus of this paper is to analyze the implementations of the Frequent item set Mining algorithms such as SMine and Apriori Algorithms. General Terms-Data Mining, Frequent Item sets,...
متن کاملFrequent item set mining
Frequent item set mining is one of the best known and most popular data mining methods. Originally developed for market basket analysis, it is used nowadays for almost any task that requires discovering regularities between (nominal) variables. This paper provides an overview of the foundations of frequent item set mining, starting from a definition of the basic notions and the core task. It co...
متن کاملPrivacy-preserving algorithms for distributed mining of frequent itemsets
Standard algorithms for association rule mining are based on identification of frequent itemsets. In this paper, we study how to maintain privacy in distributed mining of frequent itemsets. That is, we study how two (or more) parties can find frequent itemsets in a distributed database without revealing each party’s portion of the data to the other. The existing solution for vertically partitio...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Data Mining & Knowledge Management Process
سال: 2011
ISSN: 2231-007X
DOI: 10.5121/ijdkp.2011.1402