Private Itemset Support Counting

نویسندگان

  • Sven Laur
  • Helger Lipmaa
  • Taneli Mielikäinen
چکیده

Private itemset support counting (PISC) is a basic building block of various privacy-preserving data mining algorithms. Briefly, in PISC, Client wants to know the support of her itemset in Server’s database with the usual privacy guarantees. First, we show that if the number of attributes is small, then a communication-efficient PISC protocol can be constructed from a communication-efficient oblivious transfer protocol. The converse is also true: any communication-efficient PISC protocol gives rise to a communicationefficient oblivious transfer protocol. Second, for the general case, we propose a computationally efficient PISC protocol with linear communication in the size of the database. Third, we show how to further reduce the communication by using various tradeoffs and random sampling techniques.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

BISC: a Binary Itemset Support Counting Approach towards Efficient Frequent Itemset Mining

the performance of a depth-first Frequent Itemset Miming (FIM) algorithm is closely related to the total number of recursions which can be modeled as O(n), where k is the maximal recursion depth and n is the branching factor. Many existing approaches focus more on improving support counting rather than on decreasing n and k, which may lead to unsatisfactory performance as they grow. In this pap...

متن کامل

TR-2009001: BISC: A Binary Itemset Support Counting Approach towards Efficient Frequent Itemset Mining

the performance of a depth-first Frequent Itemset Miming (FIM) algorithm is closely related to the total number of recursions which can be modeled as O(n), where k is the maximal recursion depth and n is the branching factor. Many existing approaches focus more on improving support counting rather than on decreasing n and k, which may lead to unsatisfactory performance as they grow. In this pap...

متن کامل

Mining Frequent Itemsets over Uncertain Databases

In recent years, due to the wide applications of uncertain data, mining frequent itemsets over uncertain databases has attracted much attention. In uncertain databases, the support of an itemset is a random variable instead of a fixed occurrence counting of this itemset. Thus, unlike the corresponding problem in deterministic databases where the frequent itemset has a unique definition, the fre...

متن کامل

Fast Algorithms for Mining Interesting Frequent Itemsets without Minimum Support

Real world datasets are sparse, dirty and contain hundreds of items. In such situations, discovering interesting rules (results) using traditional frequent itemset mining approach by specifying a user defined input support threshold is not appropriate. Since without any domain knowledge, setting support threshold small or large can output nothing or a large number of redundant uninteresting res...

متن کامل

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)...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2005