Download Mining Multiple Private Databases

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
About Me
2

ISYE Senior and CS minor

Graduating December, 2008

Humanitarian Logistics and/or Supply Chain

Originally from Lima, Peru

Travel, paintball and politics
Outline
3

Intro. & Motivation

Problem Definition

Important Concepts & Examples

Private Algorithm

Conclusion
Introduction



↓ of information-sharing restrictions due
to technology
↑ need for distributed data-mining tools
that preserve privacy
Accuracy
Trade-off
Efficiency
4
Privacy
Motivating Scenarios


5
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
Motivating Scenarios (cont'd)

6
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"
Problem & Assumptions

Model: n nodes, horizontal partitioning
...

7
Assume Semi-honesty:

Nodes follow specified protocol

Nodes attempt to learn additional information
about other nodes
Challenges


8
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
Recall Our 3-D Goal
Accuracy
Efficiency
9
Privacy
Private Max


Actual Data sent on
first pass
start
Static Starting Point
Known
30
2
1
30
10
40
30
40
20
4
3
40
10
Multi-Round Max

Randomly perturbed
data passed to
successor during
multiple passes
Start
18
0
32
35
D2
D2


11
No successor can
determine actual data
from it's predecessor
Randomized Starting
Point
30
32
35
10
40
18
20
40
D4
D3
32
35
40
32
35
Evaluation Parameters
Parameter Description
n
# of nodes in the system
k
KNN parameter
Po
Initial randomization probability in neighbor selection
d
Dampening factor in neighbor selection
r
# of rounds in neighbor selection
12

Large k = "avoid information leaks"

Large d = more randomization = more privacy

Small d = more accurate (deterministic)

Large r = "as accurate as ordinary classifier"
Accuracy Results
13
Varying Rounds
14
Privacy Results
15
Conclusion

16
Problems Tackled

Preserving efficiency and accuracy while
introducing provable privacy to the system

Improving a naive protocol

Reducing privacy risk in an efficient manner
Critique



17
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