1. A Privacy – Preserving Index
for Range queries
Paper By: Bijit Hore, Sharad Mehrotra, Gene Tsudik
Presented By: Akshay Phadke

2. What this paper is about
Database as a Service (DAS)
Improving the existing Bucketization Technique
Identification of privacy measures in DAS.
Development of a novel privacy-preserving re- bucketization technique.

3. DAS and its implications
Database-as-a-service in which organizations outsource data management to a service provider.
Privacy because the data is stored at service provider.
One possible solution: Q = Qsec + Qunsec

4. Previous Solutions Bucketization for ranged queries
Attribute domain is partitioned into a set indentified by a set.
Deterministic encryption for join queries.
Drawbacks:
Lacks in-depth privacy scenarios.
Privacy is subjective: no clear specification.

5. Before we proceed Etuple: tuple stored in encrypted form.
crypto-indices: indices created on sensitive attributes.
Bucket_id: Set created is assigned a unique random tag.

6. Example
Allocating a large number of buckets to crypto-indices increases query precision but reduces
privacy. On the other hand, a small number of buckets increases privacy but adversely aects performance.

7. Uniform Query Distribution
Total False Positives:
Average Query Precision:
Goal: Minimize the total number of false positives.

8. Algorithm Basics
Number of false positives depends on the the width of the bucket (i.e. minimum and the maximum values) and the sum of the frequencies.
To solve the problem use Optimal Substructure property: Splitting the problems into two smaller sub problems.

9. Algorithm

10. Variance, ASEE and Entropy
Maximize Var(x)

11. Controlled Diffusion(CDf)
QoS is the maximum allowed performance degradation factor (K).
CDf algorithm increases privacy of buckets.
Diffusion carried out in a controlled manner.
Elements diffused into composite buckets.
d = K..|Bi| / fCB
Composite buckets overlap whereas in case of optimal buckets, they don’t.

12. Experiments
Data Set - Synthetic Data Set - Real Data Set - Benchmark Query Set
Measurements - Decrease in Precision - Privacy Measure - Performance-Privacy Trade Off - Time taken

13. Results Observed decrease in query precision was less than 3
For privacy measure: standard deviation increases by a large factor. Entropy grows more slowly.

14. Critique
Although starts promising, the paper becomes a mathematics paper and seems to loose focus of actual intent.
Examples mentioned just have the first step and the final solution, no intermediate steps.
The paper doesn’t explain the results.

15. Thank you