Privacy Preserving Collaborative Sequential Pattern Mining

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

• Goal Multiple parties jointly conduct sequential
  pattern mining without revealing their private
  data to each other.
• An example

ticket ticket
1 6PM
1 8PM
pop-corn pop-corn
1 7PM
1 8PM
ATM ATM
1 8AM
1 9PM
c1
c2
Pattern ATM lt ticket lt pop-corn with support of
1/2
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Approach

• Support a sequential pattern (x lt 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.