1. A Privacy-Preserving Index for Range Queries
Bijit Hore, Sharad Mehrotra, Gene Tsudik
Keiichi Shimamura

2. Background Rise in use of cloud services
Outsourcing of IT infrastructure
Increasing use of Database As a Service (DAS)

3. Database as a Service Data is stored at service provider
Service provider cannot be trusted
Security perimeter around data owner
Client is secure and trusted
Server (service provider) is not trusted

4. Problem How to maintain security and privacy using DAS?
How to estimate and analyze the effectiveness of the solution?

5. Solution Split the query into two parts
Insecure query that runs on the server
Secure query that runs on the client
Bucketization for range queries

6. Encryption and Bucketization

7. Tradeoff Larger buckets → more privacy
Smaller buckets → more performance
Want: maximum privacy and performance
Reality: tradeoff between privacy and performance

8. Optimizing Buckets for Performance

9. Breaking Bucketization
With knowledge of
Bucketization scheme
Probability distribution in each bucket
the attacker can form statistical estimates of the values of attributes used in bucketization

10. Protecting Against Attacks
Increase variance of values in a bucket
More different values in each bucket weakens statistical estimates
Increasing variance of one bucket lowers the variance of others
Add entropy
More values in each bucket weakens statistical estimates
More rows are returned per bucket, decreasing performance

11. Variance and Entropy

12. Compromise Maximize variance and entropy for most privacy
Specify a maximum performance degradation
Redistribute elements from “optimized buckets” to “composite buckets”

13. Diffusion

14. Precision Results

15. Variance Results

16. Entropy Results

17. Privacy vs. Performance

18. Conclusion Tradeoff between privacy and performance
Provides a solution for range queries that
Maximizes privacy
Limits performance degradation