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Privacy Preserving Collaborative Sequential Pattern Mining

Privacy Preserving Collaborative Sequential Pattern Mining. Justin Zhan University of Ottawa. Privacy-Preserving Collaborative Data Mining. Alice Carol Bob. Data Set C. Data Set A. Data Set B. Data Mining. Results.

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Privacy Preserving Collaborative Sequential Pattern Mining

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  1. Privacy Preserving Collaborative Sequential Pattern Mining Justin Zhan University of Ottawa

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

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

  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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