1. Mining Multiple Private Databases Topk Queries Across Multiple Private Databases (2005) Li Xiong (Emory University) Subramanyam Chitti (GA Tech) Ling Liu (GA Tech) Presented by: Cesar Gutierrez

2. 2 About Me ISYE Senior and CS minor Graduating December, 2008 Humanitarian Logistics and/or Supply Chain Originally from Lima, Peru Travel, paintball and politics

3. 3 Outline Intro. & Motivation Problem Definition Important Concepts & Examples Private Algorithm Conclusion

4. 4 Introduction ↓ of information-sharing restrictions due to technology ↑ need for distributed data-mining tools that preserve privacy Trade-off Accuracy EfficiencyPrivacy

5. 5 Motivating Scenarios CDC needs to study insurance data to detect disease outbreaks  Disease incidents  Disease seriousness  Patient Background Legal/Commercial Problems prevent release of policy holder's information

6. 6 Motivating Scenarios (cont'd) Industrial trade group collaboration  Useful pattern: "manufacturing using chemical supplies from supplier X have high failure rates"  Trade secret: "manufacturing process Y gives low failure rate"

7. 7 Model: n nodes, horizontal partitioning Assume Semi-honesty:  Nodes follow specified protocol  Nodes attempt to learn additional information about other nodes Problem & Assumptions...

8. 8 Challenges Why not use a Trusted Third Party (TTP)?  Difficult to find one that is trusted  Increased danger from single point of compromise Why not use secure multi-party computation techniques?  High communication overhead  Feasible for small inputs only

9. 9 Recall Our 3-D Goal Privacy Accuracy Efficiency

10. 10 Private Max 1 3 2 4 30 20 40 10 30 40 start Actual Data sent on first pass Static Starting Point Known

11. 11 Multi-Round Max Start 183532 4035 D2D2 D3D3 D2D2 D4D4 30 2040 10 183532 4035 0 Randomly perturbed data passed to successor during multiple passes No successor can determine actual data from it's predecessor Randomized Starting Point

12. 12 Evaluation Parameters Large k = "avoid information leaks" Large d = more randomization = more privacy Small d = more accurate (deterministic) Large r = "as accurate as ordinary classifier"

13. 13 Accuracy Results

14. 14 Varying Rounds

15. 15 Privacy Results

16. 16 Conclusion Problems Tackled  Preserving efficiency and accuracy while introducing provable privacy to the system  Improving a naive protocol  Reducing privacy risk in an efficient manner

17. 17 Critique Dependency on other research papers in order to obtain a full understanding Few/No Illustrations A real life example would have created a better understanding of the charts