2. Intelligent Database Systems Lab Presenter : JIAN-REN CHEN Authors : Cihan Kaleli, Huseyin Polat 2012, KBS Privacy-preserving SOM-based recommendations on horizontally distributed data 1

3. Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Privacy analysis Experiments Conclusions Comments 2

4. Intelligent Database Systems Lab Motivation Collaborative Filtering (CF) systems are used to suggest web pages.  limited number of users’ data -> lack of accuracy -> Cold Start Problem Horizontally partitioned among multiple vendors 3

5. Intelligent Database Systems Lab Objectives Those companies holding inadequate number of users’ data might decide to combine their data.  accurate predictions  Performance Privacy-preserving scheme 4

6. Intelligent Database Systems Lab Methodology Privacy-preserving SOM clustering on horizontally distributed data Privacy-preserving k-nn-based predictions on horizontally distributed data Privacy-preserving k-nn-based predictions on horizontally distributed data a. Off-line i. Cluster users’ data distributed among multiple parties using SOM while preserving data owners’ privacy. ii. Compute aggregate data values required for recommendation estimations. b. Online i. Determine a’s cluster. ii. Estimate prediction after receiving required aggregate data from other parties. Return the referral to a. 5

7. Intelligent Database Systems Lab SOM clustering k-nn-based collaborative filtering Methodology Determine values of initial constants: Find the winning Kohonen layer neuron: Update the weight vectors of all neurons: 6

8. Intelligent Database Systems Lab Methodology Pearson correlation coefficient: The prediction for a on q: SOM clustering k-nn-based collaborative filtering 7

9. Intelligent Database Systems Lab Privacy-preserving SOM clustering on horizontally distributed data Privacy-preserving k- nn-based predictions on horizontally distributed data Privacy-preserving k- nn-based predictions on horizontally distributed data Methodology 8 1. number of clusters 2. sequence of active party 1. number of clusters 2. sequence of active party Determine values of initial constants SOM 1. all users it holds are assigned to a cluster 2. updated W j vectors to the second party 1. all users it holds are assigned to a cluster 2. updated W j vectors to the second party 1. the next party repeats step 2 2. sends new updated W j vectors to the next party 1. the next party repeats step 2 2. sends new updated W j vectors to the next party The last party sends the updated W j vectors to the IP The last party sends the updated W j vectors to the IP

10. Intelligent Database Systems Lab Methodology Privacy-preserving SOM clustering on horizontally distributed data Privacy-preserving k- nn-based predictions on horizontally distributed data Privacy-preserving k- nn-based predictions on horizontally distributed data among C parties, P can be written p aq = v a + P, where P is: 9

11. Intelligent Database Systems Lab Attacks and Vulnerabilities: 1)A 1 : Parties can coalesce for capturing a target party’s data 2)A 2 : Paying-off 3)V 1 : Not able to return any result 4)V 2 : Missing values in aggregate values vector Privacy analysis 10

12. Intelligent Database Systems Lab Experiments Data sets 11

13. Intelligent Database Systems Lab Experiments 12

14. Intelligent Database Systems Lab Experiments 13

15. Intelligent Database Systems Lab Experiments 14

16. Intelligent Database Systems Lab Conclusions Integrating split data significantly improves preciseness. Although privacy concerns make accuracy worse, accuracy losses are smaller than the accuracy gains due to collaboration. 15

17. Intelligent Database Systems Lab Comments Advantages – accuracy, performance, and privacy Disadvantage – cost, accuracy Applications – Collaborative Filtering – Privacy-preserving scheme 16