PSEUDONYMIZATION TECHNIQUES FOR

PRIVACY STUDY WITH CLINICAL DATA

YAHAYA ABD RAHIM

FAC.INFORMATION AND COMMUNICATION TECHNOLOGY

UNIVERSITY TECHNICAL MALAYSIA MALACCA

1

Introduction

Hospital, clinic or pharmacy among the organizations that huge of personal data.

In new trend , Vijay (2002), these organizations are interested to release or publish data for research or public benefit like business or legal reasons.

However most of the data are “SENSITIVE”.

According to Tiangcheng Li & Ninghui Li (2008), many organizations, industries and governments are increasingly publishing and sharing the valuable and sensitive information without to protect of the privacy of entities. Publishing the data may put the respondent’s privacy in risk, Ge Ruan (2007).

Focus on techniques for data privacy on clinical data.

2

Introduction

What is Privacy?

Privacy includes the right of individuals and organizations to determine for themselves when, how and to what extent information about them is communicated to others.

What Impact with Hospital or Clinical?

Challenging with managing large data in hospital or clinical especially with legal and ethical.

3

Literature Review Data Protection Techniques

4

Protection

(Data)

Encrypt

Anonymity

Application

Source : IHSN ( June 2009)

Purpose : Security & Privacy

Pseudonymization

Literature Review Issues: Data Privacy Area

5

Privacy(Data)

Anonymous communicat

ion

Anonymous transactions

Anonymity in Files &

Databases

Purpose : Privacy

Anonymous Credentials

Anonymous Publication & Storage

Literature Review Issues: Data Privacy Medical Application

Elements

6

Privacy(Data)

“Hard” de-identificatio

n

Various Types Anonymization

Data Flow Segmentatio

n

Purpose : Privacy

Controlled Database

Privacy Risk Assessment

Literature Review Why Data Need To Anonymous?

Publish

Anonymous Process

Researcher(Customize)

Pattern / Predict(Customize)

Advertise(Customize)

• Information Loss

• Leak - Privacy

Incur ProblemSecurity (Pure)

Literature ReviewIssues : Anonymity Technique

Most anonymous techniques consist in reducing the level of detail in the information provided. Therefore, typically most the result in a loss of information, IHSN (2009).

Difficulties into the role of anonymous as a

complete solution to the problem of data protection. It must be considered within the context of the analysis to be done on the data, which information needs to be protect.

Anonymous Process must also be considered within its legal context (Burkhart M., Schatzmann D. & Bernhard P., 2010). But should be the lesser extent for generating licensed files / legal context, IHSN (2009).

8

Problem Statement

9

Most anonymous process may cause privacy leakage with the original data from user information.

Chances of loss information in most anonymous process is high.

Scope

The scope of this research are:

Implemented the pseudonymization techniques from anonymous process with medical clinical data.

Using data in offline mode.

10

Pseudonymization Techniques

11

always map a given identifier with the same pseudo-ID

map a given identifier with a different pseudo-ID

Time-dependent

location-dependent

content-dependent

12

Data Privacy(Domain)

Data Reduction

Data Perturbati

onData synthetic

Dataset

Anonymous dataset

Pseudonymization Process

Flow On Research Methodology

Pseudonymization Implementations

13

Privacy Protection

Data Suppliers (sources) Data Collectors (data registers)

Pseudonymization Implementations: Architecture

14

15

Data Public

Pseudonymization Engine

Anonymizer Risk Analyzer

Data Storage

Source : Enhanced Simplifying Anonymizing Proxy, Saikat Guha, 2011.

Pseudonymization Implementations

Result View (RO4)

16

1 2 3 4 5 6 7 8 9 100

10

20

30

40

50

60Result On Pseudonymization Process

Review TechniqueProposed Technique

Level O

f P

rivacy

Security (Pure)

Density Of Information

Source: Statistic IHSN, 2009

Black Marker, Truncation

ConclusionIt is expected that this research shall produce:

A new technique in anonymous process which more comprehensive where this technique be reduce or none information loss with protection of privacy leakage.

17

Future Work

18

Generalization Process In Pseudonymization

Micro data e.g: Medical data Network data

• Online Anonymization Process as Alternative Beside Encryption

end Thank you…..

Q & A?

19