A Privacy

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 dont.

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 doesnt explain the results.

15

• Thank you