PRIVACY PRESERVING ASSOCIATION RULE MINING USING ATTRIBUTE-IDENTITY MAPPING
IBRAHEEM JAFAR
A dissertation submitted in partial fulfillment of the requirements for the award of the degree of
Master of Science (Computer Science)
Faculty of Computing Universiti Teknologi Malaysia
DEDICATION
Dedicated to my beloved parents, Alhaji Jafar Abubakar and Hajiya Rahmatu Ibraheem, for their wonderful support and encouragement.
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ACKNOWLEDGEMENT ACKNOWLEDGEMENT
I first thank my Creator, Cherisher and Sustainer for His countless blessings on me. Among the blessings is enabling me to successfully complete this dissertation and the master programme in general.
My appreciation goes to my supervisor, Dr. Maheyzah Md. Siraj, for her enormous and vital support in various forms, from inception till completion of this dissertation. I thank the coordinator, master of computer science programme, Dr. Mohamad Shukor Bin Talib, for facilitating necessary coordination towards making the dissertation a success. Also, my appreciation goes to all academic and non-academic staff of faculty of computing, whose cumulative efforts transformed into completion of this dissertation and many more.
My profound gratitude goes to my lovely parents, Alhaji Jafar Abubakar and Hajiya Rahmatu Ibrahim for their support, prayer and guidance in all my endeavours. I also thank my beloved wife, Asiya Ahmad (Asibeb); my children, Abdulkadir Ibraheem (Amir) and Rahmatu Ibraheem (Amirah) for their endurance while I was away for this study. I remain thankful to all members of my family for their supports, prayers and understanding.
I especially express my appreciation to Kaduna State Government for supporting the study, through 2014/2015 Overseas Scholarship Programme. Similar appreciation goes to the Management of National Teachers Institute (NTI) Kaduna, for their support.
ABSTRACT ABSTRACT
Association rule mining uncovers hidden yet important patterns in data. Discovery of the patterns helps data owners to make right decision to enhance efficiency, increase profit and reduce loss. However, there is privacy concern especially when the data owner is not the miner or when many parties are involved. This research studied privacy preserving association rule mining (PPARM) of horizontally partitioned and outsourced data. Existing research works in the area concentrated mainly on the privacy issue and paid very little attention to data quality issue. Meanwhile, the more the data quality, the more accurate and reliable will the association rules be. Consequently, this research proposed Attribute-Identity Mapping (AIM) as a PPARM technique to address the data quality issue. Given a dataset, AIM identifies set of attributes, attribute values for each attribute. It then assigns ‘unique’ identity for each of the attributes and their corresponding values. It then generates sanitized dataset by replacing each attribute and its values with their corresponding identities. For privacy preservation purpose, the sanitization process will be carried out by data owners. They then send the sanitized data, which is made up of only identities, to data miner. When any or all the data owners need(s) ARM result from the aggregate data, they send query to the data miner. The query constitutes attributes (in form of identities), minSup and minConf thresholds and then number of rules they are want. Results obtained show that the PPARM technique maintains 100% data quality without compromising privacy, using Census Income dataset.
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ABSTRAK ABSTRAK
Perlombongan peraturan persatuan mendedahkan corak tersembunyi yang penting dalam data. Penemuan corak membantu pemilik data untuk membuat keputusan yang tepat untuk meningkatkan kecekapan, meningkatkan keuntungan dan mengurangkan kerugian. Walau bagaimanapun, terdapat kebimbangan privasi terutama apabila pemilik data tidak melombong atau apabila banyak pihak yang terlibat. Kajian ini mengkaji privasi perlombongan menggunakan persatuan peraturan (PPARM) terhadap data mendatar dan sumber luar. Penyelidikan sedia ada tertumpu pada isu privasi dan memberi perhatian yang sangat sedikit untuk isu kualiti data. Sementara itu, lebih kualiti data, lebih tepat dan boleh dipercayai akan peraturan persatuan berkenaan. Oleh itu, kajian ini mencadangkan Sifat-Identity Pemetaan (AIM) sebagai teknik PPARM untuk menangani isu kualiti data. Diberi dataset, AIM mengenal pasti menetapkan sifat-sifat, nilai atribut bagi setiap atribut. Ia kemudian memberikan identiti 'unik' untuk setiap satu daripada sifat-sifat dan nilai yang berkaitan mereka. Ia kemudian menjana dataset dibersihkan dengan menggantikan setiap atribut dan nilai-nilainya dengan identiti yang sama mereka. Untuk privasi tujuan pemeliharaan, proses sanitasi akan dijalankan oleh pemilik data. Mereka kemudian menghantar data dibersihkan, yang terdiri daripada hanya identiti, untuk pelombong data. Apabila mana-mana atau semua pemilik data perlu keputusan ARM (s) dari data agregat, mereka menghantar pertanyaan kepada pelombong data. Pertanyaan ini merupakan sifat-sifat (dalam bentuk identiti), minSup dan minConf ambang dan kemudian beberapa peraturan yang mereka mahu. Keputusan yang diperolehi menunjukkan bahawa teknik PPARM mengekalkan 100% kualiti data tanpa menjejaskan privasidengan menggurakan data Census Income.
TABLE OF CONTENTS TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENT iv
ABSTRACT v
ABSTRAK vi
TABLE OF CONTENTS vii
LIST OF TABLES xi
LIST OF FIGURES xiii
LIST OF ABBREVIATIONS xv
LIST OF SYMBOLS xvi
LIST OF APPENDICES xvii
1 INTRODUCTION
1.1 Overview 1
1.2 Problem Background 3
1.3 Statement of the Problem 5
1.4 Research Goal 5
1.5 Research Objectives 5
1.6 Research Scope 6
1.7 Significance of the Research 6
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2 LITERATURE REVIEW
2.1 Introduction 8
2.2 PPDM and PPARM Taxonomy 9
2.2.1 PPDM Conceptualization 10
2.2.2 Data 10
2.2.3 Database 11
2.2.4 Data Mining 14
2.2.5 Data Privacy 17
2.3 Overview of Privacy Preserving Techniques 19
2.3.1 Anonymization Technique 19
2.3.2 Perturbation Technique 19
2.3.3 Cryptographic Technique 20
2.4 Privacy Preserving Data Mining (PPDM) 21
2.4.1 Research Issues in PPDM 21 2.5 Design of PPDM Model 23 2.5.1 Centralized PPDM Model 23 2.5.2 Distributed PPDM Model 24 2.6 Existing PPDM Techniques 24 2.6.1 Classification based PPDM 24 2.6.2 Clustering based PPDM 27
2.6.3 Association Rule based PPDM 27
2.7 Research Focus and Direction 27
2.8 Association Rule Mining (ARM) 28
2.8.1 Basic Notations in ARM 29
2.8.2 ARM Process 31
2.8.3 ARM and Various Types of Attribute
Values 32
2.8.3.1 Categorical Attribute Values 32
2.8.3.2 Binary Attribute Values 32
2.8.3.3 Quantitative Attribute Values 33
2.8.4 ARM Algorithms 34
(PPARM) 36
2.9.1 Data Distribution in PPARM 37
2.9.2 Privacy Preservation Approaches in
PPARM 37
2.9.2.1 Non-cryptographic Approaches 37 2.9.2.2 Cryptographic PPARM Approaches 38
2.10 Research Issues in PPARM 39
2.11 Research Gap in PPARM 40
2.12 Summary 41
3 RESEARCH METHODOLOGY
3.1 Introduction 42
3.2 Research Framework 43
3.2.1 Phase I 44
3.2.2 Introduction to Attribute-Identity Mapping as Privacy Preservation Technique for
PPARM 44 3.2.3 Phase II 46 3.2.4 Phase III 46 3.3 Software 47 3.3.1 Microsoft Excel 47 3.3.2 WEKA 48 3.4 Datasets 48 3.4.1 Voting Dataset 48
3.4.2 Census Income Dataset 50
3.4.3 Pre-processing/Data Transformation
of the Census Income Dataset 51
3.5 Performance Evaluation of PPARM Techniques 53
3.5.1 Data Quality Metrics 53
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4 DESIGN AND IMPLEMENTATION
4.1 Introduction 55
4.2 Design and Implementation of Attribute-Identity
Mapping 55
4.2.1 Generating Identity for Each Attribute 56 4.2.2 Generating Identity for Each Attribute
Value 58
4.2.3 Sanitization of Dataset 60
4.3 Association Rule Mining of the Sanitized Dataset 61 4.3.1 Pre-Processing the Sanitized Dataset 61 4.3.2 Mining of the Sanitized Dataset 64
4.4 Recovery of the Original Rules 66
4.5 Summary 68
5 RESULTS AND DISCUSSION
5.1 Introduction 69
5.2 Discussion on AIM Sanitization Process 69 5.2.1 Result for Unsanitized Dataset 70 5.2.2 Comparison of Results between
Sanitized and Unsanitized Datasets 72 5.3 Visual Representations of the Results 73
5.4 Quantification of the AIM Technique 75
5.4.1 Accuracy 75
5.4.2 Dissimilarity Measure 75
5.4.3 Completeness 77
5.5 Summary 77
6 CONCLUSION
6.1 Research Problem and Achievements 78
6.2 Recommendations for Future Works 79
LIST OF TABLES LIST OF TABLES
TABLE NO. TITLE PAGE
2.1 Market Basket Summary 29
2.2 Boolean Valued Attributes 33
2.3 Apriori Notations (Agrawal and Srikant, 1994) 34
2.4 k-itemsets and Their Elements 35
2.5 Apriori Algorithm (Agrawal and Srikant, 1994) 36
2.6 Research Gap in PPARM 40
3.1 Summary of Vote Dataset 49
3.2 Vote Dataset: Attributes and their Values 50
3.3 Summary of Census Income Dataset 51
3.4 Pre-processing and Data Transformation of
Age Attribute 52
4.1 Attributes and Their Identities 57
4.2 Attributes, Attributes’ Values and
Their Identities 60
4.3 Sample Sanitized Dataset 61
4.4 Syntax for comment in WEKA 62
4.5 Syntax for declaring dataset/relation
name in WEKA 62
4.6 Syntax for Attributes Declaration in WEKA 63 4.7 Syntax for records specification in WEKA 63
4.8 Best Ten (10) Association Rules Found 65
4.9 List of Look-up Table for Recovery 67
xii 5.2 Comparison of Results Between D and
D’ Datasets 73
LIST OF FIGURES
FIGURE NO. TITLE PAGE
2.1 Literature Review Chapter Flow 8
2.2 PPDM Taxonomy
(Prakash and Singaravel, 2014) 9
2.3 Example of Vertically Partitioned Data 13
2.4 Example of Horizontally Partitioned Data 13
2.5 Data Mining Stages 15
2.6 Centralized PPDM Model (Indumathi, 2012) 23 2.7 Distributed PPDM Model (Indumathi, 2012) 24 2.8 A Market Basket 28 3.1 Research Framework 43 3.2 Phase I 44 3.3 AIM Flowchart 45 3.4 Phase II 46 3.5 Phase III 47
4.1 Flow Chart on Generating Identities for
Attributes 56
4.2 Flow Chart on Generating Identities for
Attributes’ Values 58
4.3 Associator Parameters 64
4.4 Recovery of Original Rules 68
xiv
5.2 Representation of Rule1 74
LIST OF ABBREVIATIONS
AIM - Attribute - Identity Mapping ARM - Association Rule Mining CQA - Certified Quality Auditor
IP - Internet Protocol
LHS - Left Hand Side
minConf - Minimum Confidence
minSup - Minimum Support
PPARM - Privacy Preserving Association Rule Mining PPDM - Privacy Preserving Data Mining
RHS - Right Hand Side
US - United States
xvi
LIST OF SYMBOLS
==> Association
D Unsanitized dataset D’ Sanitized dataset
LIST OF APPENDICES
APPENDIX TITLE PAGE
A Preprocessing of Attributes’ Values 85
B Sample Original/Unsanitized Dataset 93
C Sample Sanitized Dataset 94
D Full List of Attributes, Attributes’ Values and
Their Corresponding Identities 95
CHAPTER 1
INTRODUCTION
1.1 Overview
Information and communication technology, high performance hardware components such as processor and storage capacity, rapid and efficient access to social networks, advanced database technologies, among other factors, have made it possible to generate high volume of data efficiently. Reduction in cost of hardware, computerization of many tasks and access to the technologies also contribute greatly to the efficient production of data.
Data, to be meaningful, need to be processed and analyzed to generate useful information out of it. The process of information generation from data is mostly applicable to small-scale data. Also, in the small-scale data, the kind of information to be generated is known already; for example, finding an average score from a set of scores. However, for huge amount of data, there are interesting patterns and knowledge that can be of high value for decision making. The activity of realizing such patterns and knowledge out of huge amount of data is known as data mining (Xu et al., 2014).
Data mining is a domain that involves many methods from many areas such as statistics, database management, information retrieval, data warehousing, machine learning and pattern recognition (Patel and Donga, 2015). There are three basic data mining techniques, which are classification clustering and association rule.
Classification, as the name implies, involves assigning class value to an instance of database/dataset. Before the actual classification stage, learning stage should take place. At the learning stage, sample dataset, known as training set, with class labels for each instance is provided. Based on class labels of the training set, classifiers or models are generated. Next stage is the actual classification. Given an instance of dataset, whose class value is unknown, a classifier is employed to estimate appropriate class value to the instance. Classification is categorized as supervised learning because class labels are predefined in the training set (Patel and Donga, 2015).
Clustering involves grouping of instances, based on similarities in their attribute values. Each group or cluster is given a description label. Unlike classification, clustering is categorized as unsupervised learning. This is because the clusters’ labels are not predefined (Jain and Srivastava, 2013).
The third technique, association rule, generates rules that describe relationships among instances of dataset. It is also known as frequent patterns/itemset mining, because it tells how frequent patterns/itemsets occur together. The frequency is usually expressed in percentage (Jain and Srivastava, 2013).
As the technology for generating data and mining it improves, threat to data security and privacy also increases, which creates data privacy concern. In the case of data mining, privacy preserving data mining PPDM, emerged as a general research area and measure to preserve privacy in data mining. Specifically, privacy preserving association rule mining PPARM, emerged as PPDM wing for association rule, which is the focus of this research.
3 1.2 Problem Background
PPARM is a research discipline that seeks to facilitate privacy of both data and data mining result in association rule mining with minimum or no compromise to data and data mining result quality. There are two (2) major aspects in PPARM, which are data distribution and privacy preservation approach.
Data distribution is based on number of sources of data. This can be single source or multiple sources. When there is single source of data, the data distribution is called centralized; whereas when data come from multiple sources, the data distribution is called distributed. In both distributions, privacy issues arise especially when there is need to outsource data for mining purpose (Abdel Wahab et al., 2014).
Data outsourcing is a situation where data owners give their data to third party, for mining purpose. This happens majorly because of two (2) reasons. Firstly, the data owner does not have resources and/or expertise needed for mining purpose. This is the case of centralized data. Secondly, multiple data owners/parties need data mining result their data aggregate. This is the case of distributed data (Wadkar and Shelke, 2015). This research is based on distributed data with horizontal partitioning. That is, the various data owners have the same database schema, but different instances.
Recent research works in PPARM include that by Abdel Wahab et al. (2014). The work proposed DiffGen algorithm, which facilitates privacy preservation by generalization. While the research focused on scalability to large dataset, generalization leads to information loss and hence data quality is negatively affected.
Santhoshi and Rao (2015) proposed a cryptographic based PPARM approach which uses 1-1 substitution cipher and encryption of items in the transaction. While the work assured data quality, the dataset used does not cater for multi-party data ownership.
Similarly, Vemuri and Nutalapati (2015) proposed Rob Frugal algorithm, which is also based on 1-1 substitution cipher and addition of fake transactions. While 1-1 substitution cipher does not reduce data quality, addition of fake transactions does.
Wadkar and Shelke (2015) proposed another PPARM algorithm based on Rob Frugal and k-means clustering. Use of k-means algorithm is applicable only on numeric data, whereas real-life dataset contains both numeric and non-numeric data. Similarly, the k-means clustering serves the function of generalization, which leads to information loss and hence reduces data quality.
Similarly, Mohammed et al. (2016), proposed a PPARM technique called Stochastic Standard Map. While the work ensured data quality, the dataset on which the approach is tested is not a real-life dataset.
It is obvious therefore, most research works paid less attention to data quality. Meanwhile, the more the data quality, the more accurate and reliable will association rules be. Qi and Zong (2012) highlighted issues to consider when developing a privacy preservation model for data mining. Among the issues is data utility, which is another name for data quality. Also, Abdel Wahab et al. (2014) identified three (3) issues in developing a PPARM model. First is ensuring privacy of data, second is ensuring data quality of the generated rules and third is ensuring privacy of the association rules.
5 1.3 Statement of the Problem
Data quality is a key issue in PPARM. This is because privacy preservation techniques achieve their objectives by modifying dataset in order to sanitize it. This however, results to reduction in data quality.
This research proposed Attribute-Identity Mapping (AIM), as privacy preservation technique to address the problem data quality in PPARM.
1.4 Research Goal
The goal of this research is address problem of data quality in PPARM, using AIM privacy preservation technique.
1.5 Research Objectives
Based on the problem statement of the research, below are the objectives of the research:
i. To design and implement Attribute-Identity Mapping (AIM), as privacy preservation technique, to sanitize the original dataset
ii. To apply Association Rule Mining (ARM) technique on the sanitized dataset, to generate identity-based ARM results
iii. To make use of the identity-based ARM results, to facilitate recovery of actual ARM results
1.6 Research Scope
The research has under-listed as its scope:
i. Based on data distribution, the research accommodates horizontally portioned data, where data sources maintain the same schema but different instances
ii. The datasets used for implementation of this research is Census Income dataset iii. The data mining software used is Waikato Environment for Knowledge Analysis
(WEKA), version 3.8
iv. Performance evaluation is based on data quality metrics, which are accuracy, dissimilarity measure and completeness
1.7 Significance of the Research
The research contributes to knowledge in field of privacy preserving association rule mining as follows:
i. The research introduced Attribute-Identity Mapping (AIM), as privacy preservation technique in collaborative association rule mining, by introducing Attribute-Identity Mapping technique
ii. The technique ensures data quality in addition to privacy in PPARM
1.8 Organization of the Research
The research is organized into chapters, comprising of six (6) chapters. Chapter 1 gives an overview of what the research is all about. Topics highlighted in the chapter are research overview, problem background, problem statement, research goal, objectives and scope.
7 Chapter 2 is on literature review. Highlights of the chapter are PPDM conceptualization, Design of PPDM model, existing PPDM techniques, research focus, association rule mining, privacy preserving association rule mining (PPARM). Existing research works in PPARM were highlighted and research gap in the PPARM research area was identified.
Chapter 3 is research methodology. It discussed research framework which is divided in phases. Each phase corresponds to a research objective. Also, discussed in the chapter is research instrumentation. The chapter concludes with details about dataset used in the research.
Chapter 4 discussed design and implementation of the research objectives. These are design and implementation of AIM technique; association rule mining on both sanitized and unsanitized datasets; and recovery of actual association rule mining results from identity-based results.
Chapter 5 is on results and discussion. It highlighted about the experimental results obtained from Chapter 4. The results were discussed and evaluated based data quality metrics.
Chapter 6 highlighted research problem, steps taken to tackle the problem and research achievements. Finally, future works in the research area were highlighted.
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