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CHAPTER I

INTRODUCTION

Knowledge discovery in database is becoming more interesting tool and

necessary for the people to analyze the long range activities. Knowledge

discovery is the nontrivial extraction of implicit, previously unknown, and

potentially useful information from data. Mostly the people who use this

concept are business analysts, medical scientists, researchers, defense analysts,

socialists, medical researchers, economist and so on to make computer based

tactical decisions. The discovery of knowledge can be considered as a process

which consists of several sub processes termed as stages and these sequence

stages are selection, preprocessing, transformation, data mining, and

interpretation/Evaluation. Among these process/ stages, data mining is the most

significant process and it finds hidden patterns from large voluminous of data.

In knowledge discovery system data mining plays a major role in determining

useful and uncovers knowledge from enormous amount of database and it can

also be the young and interdisciplinary field of computer science such as

database systems, artificial intelligence, statistics and machine learning. An

overview of steps in knowledge discovery in database [1] is given in Figure 1.1.

Now a days, people find data mining is a necessary tool to find knowledge in

decision making process to predict the future trends and also helps the

companies to take right decisions. Because of this reason the data mining field

is getting more attention from many researchers to find useful patterns

efficiently and accurately.

Figure 1.1: An Overview of the Steps in KDD Process.

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Before taking the database into analysis, data in database has to be

cleaned by removing noise and inconsistency. The second thing to be taken into

consideration is availability of source of data may be at one source or in many

sources since several branches may exist for providing the same services. If data

are at multiple sources data has to be integrated. Once the database is cleaned,

relevant data is extracted by removing relevant data and then data mining

technique is applied to extract useful patterns based on interesting measures.

There are many applications of data mining exist in the real world such as text

mining, web mining, spatial mining, multimedia mining, object mining,

sequence mining and so on. Data mining allows the companies to exploit

information and use this to obtain competitive advantage, also helps the

business people to identify trends such as why the customers buy particular

products, to build strategies for direct marketing, ideas for shelf placement,

training of employees vs employee retention and employee benefits vs

employee retention.

Data Mining has been able to grasp the attention of many in the field of

scientific research, business, banking sector, intelligence agencies and many

others from the early days of its inception. Largely it is used in several

applications such as understanding consumer research marketing, cross selling,

product analysis, demand and supply analysis, e-commerce, investment trend in

stocks & real estates, telecommunications, medical, health care areas,

e-commerce, Customer Relationship Management (CRM), Fast Moving

Consumer Goods (FMCG) industry.

Data Mining is also used by intelligence agencies like Federal Bureau of

Investigation (FBI) and Central Intelligence Agency (CIA) to identify threats of

terrorism. In banking, it is used to detect the credit card frauds by identifying

patterns involved in fraudulent transactions. The following section presents

various data mining techniques.

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1.1 Data Mining Techniques

Data mining techniques are the result of a long process of research and

product development. This evolution started when business data was first stored

on computers, continued with improvements in data access, and more recently,

generated technologies that allow users to navigate through their data in real

time. Data mining takes this evolutionary process beyond retrospective data

access and navigation to prospective and proactive information delivery. Data

mining is ready for application in the business community because it is

supported by three technologies that are now sufficiently mature:

Massive data collection

Powerful multiprocessor computers

Data mining algorithms

Data Mining is currently considered an enabler for business intelligence

systems. Data mining algorithms are available as software packages. Building a

data mining application usually starts with a heavy emphasis on data

warehousing followed by exploratory data mining. The analysis and application

building is typically conducted by consultants or in-house analytic teams. The

key challenges to the successful completion of a data mining project are the data

warehousing requirements, and the sophisticated analytical requirements. When

data mining tools are implemented on high performance parallel processing

systems, they can analyze massive databases in minutes.

Given databases of sufficient size and quality, data mining technology

can generate new business opportunities by providing these capabilities:

Automated prediction of trends and behaviors.

Data mining automates the process of finding predictive information in large

databases. Questions that traditionally required extensive hands on analysis

can now be answered directly from the data.

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Automated discovery of previously unknown patterns

Data mining tools search through databases and identify previously hidden

patterns in one step. When data mining tools are implemented on high

performance parallel processing systems, they can analyze massive

databases in minutes.

Data mining functionalities are used to specify the kind of patterns to be

found in data mining tasks. Descriptive and predictive are the two basic models

of data mining. Data mining tasks can be classified into two models, which are

descriptive and predictive. Descriptive model discovers patterns or relationships

in data and it serves as a way to explore the properties of the examined data.

Predictive model determines patterns on the existing and/or historical data to

make predictions. Association Rules, clustering, sequence discovery are

descriptive models where as classification, regression analysis, time series

analysis, prediction and so on are predictive models of data mining. Among

these, association rule mining technique is the most essential technique which

helps the people to make right decisions in stabilizing and improving the output.

This technique predicts the knowledge and represents the knowledge as a set of

association rules from large database based on statistical measures given by the

user.

Some of the popularly used data mining techniques are association rule

mining, classification, clustering and sequencing and are as follows:

1.1.1 Association Rule Mining

Given a collection of items and a set of records, each of which contain

some number of items from the given collection, an association discovery

function is an operation against this set of records which return affinities that

exist among the collection of items. These affinities can be expressed by rules

such as “72% of all the records that contain items A, B and C also contain items

D and E”. The specific percentage of occurrences (in the case 72) is called the

confidence factor of the association. Also, in the association, A, B and C are

said to be on an opposite side of the association to D and E. Association

discovery can involve any number of items on either side of the association.

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A typical application that can be built using association discovery is

supermarket problem. The problem is to analyze customers’ buying habits by

finding associations between the different items that customers place in their

shopping baskets. The discovery of such association rules can help the retailer

to develop marketing strategies, by gaining insight into matters like “which

items are most frequently purchased by customers”. It also helps in inventory

management, sale promotion strategies, etc.

1.1.2 Classification

Classification finds rules that partition the data into disjoint groups. The

input for the classification is the training dataset, whose class labels are already

known. Classification analyzes the training dataset and constructs a model

based on the class label, and aims to assign a class label to the future unlabelled

records. Since the class field is known, this type of classification is known as

supervised learning. A set of classification rules are generated by such a

classification process, which can be used to classify future data and develop a

better understanding of each class in the database.

There are several classification discovery models such as decision trees,

neural networks, genetic algorithms and the statistical models like

linear/geometric discriminates. The applications include the credit card analysis,

banking, medical applications and the like.

Previously, the domestic flights in India were operated only by Indian

Airlines. Recently, many other private airlines have also started their operations

for domestic travel. Some of the customers of Indian Airlines preferred these

private airlines and, as a result, Indian Airlines lost their customers. If the

Indian Airlines wishes to analyze why they have lost some customers, their goal

is to predict what type of customers it is foregoing to its competitors ultimately.

In other words, their aim to build a model based on the historical data of loyal

customers versus customers who have left. This becomes a classification

problem. It is a supervised learning task as the historical data becomes the

training set which is used to train the model. The decision tree is the most

popular classification technique.

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1.1.3 Clustering

Clustering is the process of grouping the data into clusters, so that

objects with in a cluster have high similarity in comparison to one another but

are very dissimilar to objects in other clusters [2]. Dissimilarities are assessed

based on the attribute values describing the objects. Often, distance measures

are used. Clustering has its roots in many areas, including data mining,

statistics, biology, and machine learning.

Cluster analysis is an important human activity. Early in childhood,

human beings learn how to distinguish between cats and dogs, or between

animals and plants, by continuously improving subconscious clustering

schemes. Cluster analysis has been widely used in numerous applications,

including market research, pattern recognition, data analysis, and image

processing. In business, clustering can help marketers discover distinct groups

in their customer bases and characterize customer groups based on purchasing

patterns. In biology, it can be used to derive plant and animal taxonomies,

categorize genes with similar functionality, and gain insight into structures

inherent in populations. Clustering may also help in the identification of areas

of similar land use in the earth observation database and in the identification of

groups of houses in a city according to house type, value, and geographic

location, as well as the identification of groups of automobile insurance policy

holders with a high average claim cost. It can also be used to classify

documents on the Web for information discovery.

Clustering is also called data segmentation in some applications because

clustering partitions large datasets into groups according to their similarity.

Clustering can also be used for outlier detection, where outliers (values that are

“far away” from any cluster) may be more interesting than common cases.

Applications of outlier detection include the detection of credit card fraud and

the monitoring of criminal activities in electronic commerce.

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1.1.4 Sequence Discovery

A sequence database consists of sequences of ordered elements or

events, recorded with or without a concrete notion of time. There are many

applications involving sequence data. Typical examples include customer

shopping sequences, web click streams, biological sequences etc. Sequential

pattern mining is the mining of frequently occurring ordered events or

subsequences as patterns. An example of a sequential pattern is “Customers

who buy a Canon digital camera are also likely to buy an HP color printer

within a month”. For retail data, sequential patterns are useful for shelf

placement and promotions. This industry, as well as telecommunications and

other businesses, may also use sequential patterns for targeted marketing,

customer retention, and many other tasks. Other areas in which sequential

patterns can be applied include weather prediction, production processes, and

network intrusion detection.

The sequential pattern mining problem was first introduced by Agrawal

and Srikant in 1995 [3] based on their study of customer purchase sequences,

according to them, “Given a set of sequences, where each sequence consists of a

list of events (or elements) and each event consists of a set of items, and given a

user-specified minimum support threshold of minsup, sequential pattern mining

finds all frequent subsequences, that is, the subsequences whose occurrence

frequency in the set of sequences is not less than minsup.”

Though the data mining is well known technique which automatically

and intelligently extracts information from large amount of data, it can also

disclose sensitive information about individuals by compromising the

individual’s right to privacy. Moreover data mining techniques can reveal

critical information about business transactions, compromising the free

competitions in business settings.

Keeping in view of the motivation to incorporate the privacy in data

mining techniques to protect the confidential data of the user, there evolved a

new stream in data mining era that is privacy preserving in data mining. There

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exists a key difference between regular data mining algorithms under various

data mining techniques like classification, association, clustering and privacy

preserving data mining algorithms that is the formal algorithms deals with how

to analyze the stored raw data and how to extract useful knowledge discovery

patterns from the database whereas in the later one, it mainly deals with the

sensitive information of the user records where privacy factor is the major

concern and it is considered to be the vital issue. The following section

discusses about privacy preserving data mining.

1.2 Privacy Preserving Data Mining (PPDM)

Data mining has been viewed as a threat to privacy because of the

widespread proliferation of electronic data maintained by corporations. This has

lead to increased concerns about the privacy of the underlying data. In recent

years, many organizations often share their information with legitimate parties

to discover more useful information for decision making purposes and to

enhance their competitive spirit. The issue of privacy plays a vital role when

several legitimate people share their resources in order to obtain mutual benefits

but no one is interested to disclose their private data. In the process of data

mining, how to resolve the problem of privacy preserving has become a hot

research topic in data mining field. Hence privacy preserving data mining

research area is evolved.

At present, privacy preserving data mining methods can be roughly

divided from four aspects.

Based on the basic strategy of original data hiding, they can be divided

into data perturbation, query restriction and mixed strategies.

Based on the data mining techniques, they can be divided into

association rules mining classification and clustering etc.

Based on the privacy preserving techniques, they can be divided into

heuristic technique, encryption and reconfigurable technique.

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Based on the distribution of data resources, they can be divided into

privacy- preserving centralized database mining and privacy-preserving

distributed database mining.

Verykios et al. [4] analyzed the state-of-the-art in the area of PPDM,

classifies the proposed privacy preservation data mining algorithms in

different aspects and is shown in Figure 1.2. The PPDM algorithms are broadly

classified into three categories namely heuristic based, reconstruction based and

cryptography based.

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Figure 1.2 Taxonomy of Privacy Preserving Data Mining Algorithms

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Heuristic-Based Techniques

Heuristic-based techniques are to resolve how to select the appropriate

data sets for data modification. Since the optimal selective data modification or

sanitization is an NP-Hard problem, heuristics can be used to address the

complexity issues. The methods of heuristic-based modification include

perturbation, which is accomplished by the alteration of an attribute value by a

new value (i.e., changing a 1-value to a 0- value, or adding noise), and blocking,

which is the replacement of an existing attribute value with a “?”.

Reconstruction-Based Technique

A number of recently proposed techniques address the issue of privacy

preservation by perturbing the data and reconstructing the distributions at an

aggregate level in order to perform the association rules mining. That is, these

algorithms are implemented by perturbing the data first and then reconstructing

the distributions. According to the different methods of reconstructing the

distributions and data types (numerical data, binary data and categorical data),

the corresponding algorithm is not the same.

Cryptographic Techniques

Another branch of privacy preserving data mining by using

cryptographic techniques was developed. This branch became hugely popular

for two main reasons: Firstly, cryptography offers a well-defined model for

privacy, which includes methodologies for proving and quantifying it.

Secondly, there exists a vast tool set of cryptographic algorithms and constructs

to implement privacy-preserving data mining algorithms.

1.2.1 Privacy Preserving in Centralized and Distributed Database

Environment

In large applications the whole data may be in a single location called

centralized or multiple sites called distributed database. Methodologies are

proposed by many authors for both centralized as well as distributed database to

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protect private data. In centralized database environment, various approaches

are proposed for privacy preserving data mining which can be categorized into

data hiding and knowledge hiding approaches. In data hiding, the main concept

is how the privacy of data/information can be maintained before hiding process

begins. In this approach, removal of private/sensitive information prior to its

disclosure by adopting the techniques such as perturbation, sampling,

generalization etc. to produce a sanitized database of the original database. In

case of knowledge hiding approaches, instead of protecting the raw data,

sensitive information from data mining results are protected by using distortion

and blocking techniques. In distributed environment, database is a collection of

multiple, logically interrelated databases distributed over a computer network

and are distributed among number of sites. As the database is distributed,

different users can access it without interfering with one another. In distributed

environment, database is partitioned into disjoint fragments and each site

consists of only one fragment. Data can be partitioned in different ways such as

horizontal, vertical and mixed mode. In distributed database environment, the

discovered knowledge can be shared to many legitimate people by preserving

the privacy of individual sites.

In general, privacy preservation occurs in two major dimensions: users'

personal information and information concerning their collective activity. The

former one is referred to as data hiding and the latter as knowledge hiding.

The two real-life examples where PPDM poses different constraints are

discussed below.

Scenario 1: A hospital shares some data for research purposes (e.g., concerning

a group of patients who have a similar disease). The hospital's security

administrator may suppress some identifiers (e.g., name, address, phone

number, etc) from patient records to meet privacy requirements. However, the

released data may not be fully protected. A patient record may contain other

information that can be linked with other datasets to re-identify individuals or

entities. How can we identify groups of patients with a similar disease without

revealing the values of the attributes associated with them?

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Scenario 2: Two or more companies have a very large dataset of records on

their customers' buying activities. These companies decide to cooperatively

conduct association rule mining on their datasets for their mutual benefit since

this collaboration brings them an advantage over other competitors. However,

some of these companies may not want to share some strategic patterns hidden

within their own data (also called restrictive association rules) with the other

parties. They would like to transform their data in such a way that these

restrictive association rules cannot be discovered but others can be. Is it possible

for these companies to benefit from such collaboration by sharing their data

while preserving some restrictive association rules?

The above scenarios describe different privacy preservation problems.

Each scenario poses a set of challenges. For instance, Scenario 1 is a typical

example of individual's privacy preservation, while Scenario 2 refers to

collective privacy preservation.

1.2.2 Different Views of Privacy Preservation Data mining

PPDM could be attempted at three levels as shown in Figure-1.3. The

first level is raw data or databases where transactions reside. The second level is

data mining algorithms and techniques that ensure privacy. The third level is the

output of different data mining algorithms and techniques [5].

Figure 1.3: Three Major Levels of PPDM

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At Level 1, researchers have applied different techniques to raw data or

databases for the sake of protecting the privacy of individuals (by preventing

data miners from getting sensitive data or sensitive knowledge), or protecting

privacy of two or more parties who want to perform some analysis on the

combination of their data without disclosing their data to each other.

At Level 2, privacy-preserving techniques are embedded in the data

mining algorithms or techniques and may allow skillful users to enter specific

constraints before or during the mining process.

Finally, at Level 3, researches have applied different techniques to the

output of data mining algorithms or techniques for the same purpose of Level 1.

Most of the research in the PPDM problems has been performed at Level 1.

A Few researchers applied privacy-preserving techniques at Level 2 or Level 3.

1.2.3 PPDM Applications

A number of diverse application domains where privacy-preserving data

mining methods are useful. Government, Corporations, and individuals have

created tremendous opportunities for knowledge and information based decision

making. Driven by mutual benefits, or by regulations, requires data to be shared,

and demands exchange of data/knowledge among various parties. But original

dataset may contain sensitive information. This sensitive data/knowledge may

contain information about individuals, or some confidential data or significant

information to competitor. Revealing this may lead to loss in business or

reduces the confidentiality to individuals.

The problem of privacy preserving data mining has numerous

applications in customer purchase behavior patterns, customer transactional

analysis, security system, medical research fields, and many research fields.

Some of the applications discussed in [5] are as follows:

Application in Medical Field

A classical example of a privacy-preserving data mining problem of the

first type occurs in the field of medical research. Consider the case of a number

of different hospitals that wish to jointly mine their patient data for the purpose

of medical research. Furthermore, privacy policy and law prevents these

hospitals from ever pooling their data or revealing it to each other, due to the

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confidentiality of patient records. In such cases, classical data mining solutions

cannot be used. Rather, it is necessary to find a solution that enables the

hospitals to compute the desired data mining algorithm on the union of their

databases, without ever pooling or revealing their data. Privacy-preserving data

mining solutions have the property that the only information learned by the

different hospitals is the output of the data mining algorithm.

The developed scrub system was designed for de-identification of

clinical notes and letters which typically occurs in the form of textual data,

which contains references to patients, family members, addresses, phone

numbers or providers. It has been shown that such a system is able to remove

more than 99% of the identifying information from the data while discovering

knowledge using data mining techniques.

Application in Bioterrorism

Bioterrorism applications provide more identifiable information in

accordance with public health law using privacy-preserving data mining

techniques with medical data. Often a biological agent such as anthrax produces

symptoms which are similar to other common respiratory diseases such as the

cough, cold and the flu. In the absence of prior knowledge of such an attack,

health care providers may diagnose a patient affected by an anthrax attack of

symptoms from one of the more common respiratory diseases. The key is to

quickly identify a true anthrax attack from a normal outbreak of a common

respiratory disease, in many cases, an unusual number of such cases in a given

locality may indicate a bio-terrorism attack. Therefore, in order to identify such

attacks it is necessary to track incidences of these common diseases as well.

Therefore, the corresponding data would need to be reported to public health

agencies. However, the common respiratory diseases are not reportable diseases

by law.

In Web Camera Surveillance system, publicly available web cams are

used to detect unusual activities but the problem is that it captures person

specific information which is much invasive approach. This approach

incorporates privacy preserving data mining techniques to extract only facial

counts information from the images captured in webcams. Using facial counts,

it may detect unusual activities. This way, it protects sensitive information.

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For many recent years, research is going on in the science of DNA

sequencing and forensic analysis with the use of DNA dataset. Great research

work is going on in this field, DNA databases are growing very fast in both

medical and law enforcement communities, but DNA data is considered

sensitive since it consists of unique data/information which may be used to

detect individuals easily. Here, privacy is required to protect uniquely

identifying information about an individual from the DNA database which are

publicly available. The hospitals usually separate out the clinical data from the

genomic and make the genomic data available for research purposes.

1.3 Privacy Preserving Association Rule Mining

In 1993, Agarwal first introduced, association rules for discovering

interesting relationships between products in large scale transaction data

recorded by point-of-sale systems in supermarkets. Each rule has two parts

called antecedent and consequent. The antecedent and consequent are two sets,

consists of items which exist in the transactions. Antecedent is also called rule

head (left hand side of the rule), similarly Consequent is also called rule body

(right hand side of the rule). For example the following association rule is found

in a supermarket shopping transaction with support 50 and confidence 80.

[Tomato, Carrot] → [Orange Juice] .

[Tomato, Carrot] is called antecedent or rule head and [Orange Juice] is called

consequent or rule body. This rule specifies that whenever customer purchases

tomato and carrot, then the same person also purchases orange juice since

interesting relationship between items exist. These interesting purchase

strategies are determined with the help of measures called support and

confidence that is the rule support is greater than or equal to user specified

support and confidence is also greater than or equal to user specified

confidence.

Here support value indicates that the set of items tomato, carrot and

orange juice were purchased by more than or equal to 50% of the customers.

Similarly, the confidence value indicates that in the transactions where the items

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tomato and carrot are found together, the item orange juice is also present in 80%

of the transactions (that is purchased by the customers).This rule gives

information and can be used as the basis for decisions about marketing activities

such as, e.g., promotional pricing or product placements.

There are many algorithms that exist for finding association rules

employing the given support and confidence. The number of association rules

generated by the algorithm can vary and sometimes it may create too many or

very few rules. So according to the given support and confidence values, rules

are generated. This association rule mining is mainly used for medical research

fields where findings of association between attributes play a vital role in

deciding decisions for diagnosing the patients.

1.3.1 Definition of Association Rule

Let I= {i1,i2,… ,im} be a set of items or attributes and D be a database of

transactions, where each transaction T consists of a set of items so that T I. An

association rule is an implication of the form X→Y, where X I, Y I and

X∩Y=Ø. An association rule X→ Y has support s in D if the probability of a

transaction in D contains both X and Y is s. The association rule X →Y holds in

D with confidence c if the probability of a transaction in D which contains X as

well as Y is c. Association rule mining is originated from Market basket

analysis to discover customer’s buying patterns and preferences to optimize the

sales and profit. Association rule mining are used in many other application

domain, mainly used in Medical field, Financial service Industries,

E-commerce, Sports analysis, Web usage mining, Bio-Informatics and so on.

The algorithm proceeds in two stages. In the first stage, it generates

frequent (large) item sets in the given dataset. In the second stage, it generates

strong association rules from frequent item sets identified in first stage.

To generate association rules, frequent item sets along with support

values are to be determined. Many algorithms are exists for generating frequent

item sets in order to generate association rules. Apriori is the best-known classic

algorithm to mine association rules and it is adopted in this paper to generate

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frequent item sets. It is designed to operate on database containing transactions,

where each transaction contains set of items. In order to declare whether the

item sets is frequent or infrequent, its support value is compared with user

specified minimum support threshold value and if the value of support is greater

than or equal to user specified minimum support threshold value. The

following Figure 1.4 illustrates how frequent item sets are determined from

a database which consists of 4 transactions and five attributes and minimum

support threshold value 50%. So minimum number of transactions required to

support an item or item sets to be frequent is computed from total database size

*minimum support threshold value /100.

Figure 1.4: Frequent Item Set Generation

In the same way to declare whether the rule formed by item sets is

declared to be strong or weak, its confidence value is compared with user

specified minimum confidence threshold value. If rule confidence is greater

than or equal to user specified minimum confidence threshold value then

declare the rule as strong otherwise a weak rule.

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1.3.2 Privacy Preserving Association Rule Mining in Centralized

and Distributed Database

Privacy preserving association rule mining can be defined as the process

of finding association rules from the large database without revealing any

private data or information pertaining to the database. The problem of finding

privacy preserving association rule mining can be solved in different ways for

two different cases such as centralized database environment and distributed

database environment. Even though, the problem in the environments is

different from one another, finding efficient solution in both environments is a

challenging task to the researchers.

In the centralized database environment, source of large database is

provided by single person called database owner who has full rights over the

database as well as over the results of the database. In many real life applications,

many people wish to access the database as well as mined results though they are

not actually owner of the database but they are treated as partners or legitimate

people since they may get mutual benefits between themselves. So the database

owner is willing to provide their database to their partners in order to have

goodwill between them. The partners may then allow accessing the database to

find association rules but they should not be allowed to get opportunity to access

confidential data or information. This makes the issue to the researchers how to

design a new database called distorted database based on original database to

preserve private data or information. Many approaches have been developed by

researchers to find distorted database where distorted database is determined by

doing changes in supporting transactions for items or item sets. These changes

makes the database to hide sensitive information but also produces side effects

such as non sensitive information may also be hidden and also spurious

information may be produced in some cases.

In distributed database environment, the main target is finding global

association rules from n (n≥2) number of sites without interfering in any one’s

private data. However every site owner participates in mining process by

providing their partial results in disguised form to protect from one another in

order to find global frequent item sets.

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Data hiding can be done in different ways in different partitioned

methods which means for horizontally partitioned database model the process of

finding global association rules from n sites is entirely different from finding

global association rules for vertically partitioned databases since the data

schema is different from one another. In many real life applications, different

types of mixed partitioned databases, called hybrid are also exist which are

formed by combining basic partitioned models in different ways according to

organizational structure. Privacy preserving association rule mining is also

required in mixed model.

1.4 Scope of the Investigation

In this thesis, main work is focused on problem of finding privacy

preserving association rule mining from large database efficiently in two

different environments such as centralized and distributed database

environments. A brief account of the methods adopted to solve this problem in

two different environments is given hereunder.

The main thrust of the work, as reported in the dissertation, is the

presentation of methodologies for finding association rules without revealing

any one’s secret data to any other one in centralized as well as distributed

environments. The need of efficient methodologies that would work irrespective

of the number of attributes, number of transactions, size of sensitive data,

number of sensitive rules and number of users.

To protect sensitive data or information in the process of mining

association rules without any side effects makes this research problem a

challenging one.

In this work, in centralized database environment, two approaches namely

heuristic and exact approaches are considered for finding association rules by

preserving sensitive information. A new methodology based on heuristic

approach is proposed to hide the sensitive information in the process of

generating association rules. In another experiment, a modification for the

existing inline algorithm is proposed to minimize the side effects in the process

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of hiding sensitive information by applying divide and conquer strategy on

constraints. A partition based hybrid hiding methodology is also proposed to

hide the sensitive information for large database in the process of finding

optimum solution efficiently by using the concepts of divide and conquer and

parallelism.

The performance analysis of the above said three proposed methodologies in

case of centralized database environment is discussed.

In this thesis work, the problem of finding privacy preserving association

rule mining in case of distributed database environment is also considered.

The problem of finding privacy preserving association rule mining in

case of horizontally partitioned databases with Trusted Party is discussed with

proposed methodology by adopting cryptography techniques. As another case

how to find the privacy preserving association rule mining for horizontally

partitioned databases when no site can be considered as trusted party is

discussed with proposed cryptography based methodology.

In case of vertically partitioned databases, a methodology which adopts

scalar product is proposed to find global association rules without violating

privacy constraints. A mixed model partitioned database model which consists

of a combination of horizontally and vertically partitioned strategies is

considered in this work to find the global association rules by preserving

privacy of individuals.

The performance of the above proposed methodologies in case of

distributed database environment is analyzed.

1.5 Organization of the Thesis

The entire work in thesis is organized into six chapters.

Chapter I is an introductory part of the thesis, which gives detailed

description of data mining techniques, the need of privacy preservation in data

mining and its applications. The goals and approaches for privacy preserving

data mining are also discussed. The significance of association rule mining and

privacy preserving association rule mining in centralized as well as in

distributed database environment are presented in this chapter.

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In Chapter II, a review of literature related to sensitive association rule

hiding in centralized database environment is presented. Earlier work on various

approaches for finding association rules while hiding sensitive rules is also

discussed. This chapter further specifies the earlier work which was done for

preserving individual’s sensitive data/database in the process of finding

association rules in distributed environment. Earlier work related to privacy

preserving association rule mining with various partitioning methods such as

horizontally partitioned distributed databases, vertically partitioned distributed

databases and also mixed partitioned distributed databases is also discussed in

this chapter.

Chapter III deals with approaches for hiding sensitive association rules

in centralized database environment. There are three major approaches exist to

hide sensitive association rules such as heuristic based, border based and exact

approach. In this thesis work heuristic based and exact approaches are adopted.

A new methodology based on heuristic, is proposed in this thesis work to hide

sensitive association rules efficiently with minimum side effects. A modified

inline algorithm which belongs to the category of exact approach is proposed by

adopting divide and conquer strategy for the constraints and Binary Integer

Programming (BIP) method to hide the sensitive association rules. An efficient

hybrid methodology is also proposed in this thesis work by applying

partitioning and parallel processing techniques in case of hiding sensitive item

sets for large database. The proposed methodology efficiently finds the

optimum solution by using BIP without violating any privacy constraints.

The performance analysis of all the proposed methodologies is

discussed. The analysis of the experimental results for these methodologies is

also presented.

Chapter IV covers a methodology for finding privacy preserving

association rule mining, when data is partitioned horizontally among n (>=2)

number of sites. Each site has different transactional database of common

attributes. The main issue is, when all site owners wish to join in mining process

to find global association rules without disclosing their individual database and

findings such as locally large frequent item sets, supports values to any other

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site owners. Two methodologies are proposed in this chapter for finding privacy

preserving association rule mining when data is distributed horizontally among

n number of sites.

The first model deals with a Trusted Party (TP) to find the global

association rules by using the proposed sign based secure sum cryptography

methodology. When no site can be treated as Trusted Party, the problem of

finding global association rules by preserving the privacy of individuals is

considered as second model. A hash based secure sum cryptography

methodology is proposed in the second model.

The efficiency of the two proposed models is discussed in this chapter in

terms of privacy, computations and communications. The experimental results

are analyzed and discussed.

Chapter V discusses about a methodology to find association rules in

vertically partitioned databases in distributed environment for n (>=2) number

of sites. This chapter also discusses a methodology in finding association rules

in mixed partitioned databases in distributed environment. Each new

methodology finds global association rules accurately in distributed

environment without disclosing any site’s private data/information to any other

site owner.

As a first case, n number of vertically partitioned databases with one

special site called data miner is considered to find the global association rules.

A new methodology is proposed which utilizes the cryptography technique

scalar product to efficiently generate the global association rules by preserving

the privacy constraints of individuals. Two types of mixed partitioned databases

with the combination of horizontal and vertical partitioning strategies are

considered in this chapter. The methodologies are proposed for these two mixed

models to find the global association rules. In this chapter the efficiency of the

vertically partitioned model and two mixed partitioned models are discussed

and analyzed on various aspects.

Chapter VI deals with conclusion of the research work carried out in

this thesis. The future work is also suggested.