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Privacy Preserving Collaborative Sequential Pattern Mining : 

2008-2-6 1 Privacy Preserving Collaborative Sequential Pattern Mining Justin Zhan University of Ottawa

Privacy-Preserving Collaborative Data Mining: 

2008-2-6 2 Privacy-Preserving Collaborative Data Mining Data Mining Data Set A Data Set B Results Data Set C Alice Carol Bob

Problem Definition: 

2008-2-6 3 Problem Definition Goal: Multiple parties jointly conduct sequential pattern mining without revealing their private data to each other. An example: Pattern: ATM < ticket < pop-corn with support of 1/2 c1 c2

Approach: 

2008-2-6 4 Approach Support: a sequential pattern (x < y) has support s% if s% of transactions (records) in a joint data set of n parties contain both x and y with x happening before y. Approach: Construct event vectors (Col: transaction times of an item; Row: customer-ID) from data tables. Compute the support of sequential patterns (or events) based on event vectors via a secure protocol.

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