1 / 17

Privacy Preserving K -means Clustering on Vertically Partitioned Data

Privacy Preserving K -means Clustering on Vertically Partitioned Data. Presented by: Jaideep Vaidya Joint work: Prof. Chris Clifton. Overview. Global Problem Privacy Preserving Distributed Data Mining Specific Problem Clustering (K-Means) For Vertically Partitioned Data Using

briar-richardson
Download Presentation

Privacy Preserving K -means Clustering on Vertically Partitioned Data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Privacy Preserving K-means Clustering on Vertically Partitioned Data Presented by: Jaideep Vaidya Joint work: Prof. Chris Clifton

  2. Overview • Global Problem • Privacy Preserving Distributed Data Mining • Specific Problem • Clustering (K-Means) • For • Vertically Partitioned Data • Using • Cryptographic Tools

  3. Vertical Partitioning of Data Global Database View Cell Phone Data Medical Records

  4. Is the problem trivial?

  5. Privacy Preserving Data Mining • Perturbation • Agrawal & Srikant, Agrawal & Aggarwal, • Rizvi & Haritsa, Evfimievski et al. • Cryptographic • Lindell & Pinkas, Du & Zhan • Vaidya & Clifton, Kantarcioglu & Clifton

  6. Secure Multiparty Computation (SMC) • Given a function f and n inputs, distributed at n sites, compute the result while revealing nothing to any site except its own input(s) and the result.

  7. Results • Cluster assignment for entities • Not private • Cluster centers • Semi-private

  8. Secure K-means clustering K-means clustering Arbitrarily select k starting points Repeat • Assign to respectively • (re)assign each object to closest cluster based on distance from mean • Re-compute the cluster means Until no change

  9. Assigning objects to closest cluster

  10. Key Idea • Disguise site components with random values • Compare distances while revealing only comparison result • Permute order of clusters to conceal meaning of comparison results

  11. Closest Cluster Computation • 3 special sites, P1, P2 and Pr • P1 generates • r random vectors such that • Permutation π (over 1 .. K)

  12. Permutation ProtocolDu and Atallah ’01 B A Homomorphic encryption: Ek(x)*Ek(y) = Ek(x+y)

  13. Closest Cluster Computation P2 P1 P3 P1 Pr Pr-1 Pr Stage 2 Stage 1

  14. Closest Cluster Computation • Stage 3 • P2 and Pr determine i, the index of the cluster with minimum distance • Stage 4 • P1 computes and broadcasts

  15. When to stop? • Locally compute difference in means • Globally known threshold • Use simple random-adding technique to disguise actual values • First party adds random value to its distance and sends to next party • Each party adds its value to total and sends on • Last party compares with first party’s random +threshold

  16. Communication Cost • r parties, n data elements, m bit distances

  17. Conclusion • Presented a solution for Privacy Preserving K-Means Clustering problem • How to use clusters? • Will parties share required information for the possible benefits? • Improve Efficiency • Working on EM-Clustering, implementations

More Related