کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
395602 665995 2007 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Privacy-preserving algorithms for distributed mining of frequent itemsets
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Privacy-preserving algorithms for distributed mining of frequent itemsets
چکیده انگلیسی

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 partitioned data leaks a significant amount of information, while the existing solution for horizontally partitioned data only works for three parties or more. In this paper, we design algorithms for both vertically and horizontally partitioned data, with cryptographically strong privacy. We give two algorithms for vertically partitioned data; one of them reveals only the support count and the other reveals nothing. Both of them have computational overheads linear in the number of transactions. Our algorithm for horizontally partitioned data works for two parties and above and is more efficient than the existing solution.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 177, Issue 2, 15 January 2007, Pages 490–503
نویسندگان
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