Social security data mining : an Australian case study

University of Technology, Sydney. Faculty of Engineering and Information Technology.Data mining in business applications has become an increasingly recognized and accepted area of enterprise data mining in recent years. In general, while the general principle and methodologies of data mining and machine learning are applicable for any business applications, it is often essential to develop specific theories, tools and systems for mining data in a particular domain such as social security and social welfare business. This necessity has led to the concept of social security and social welfare data mining, the focus of this thesis work. Social security and social welfare business involves almost every citizen’s life at different life periods. It provides fundamental and crucial government services and support to varied populations of specific need. A typical scenario in Australia is that it not only connects one third of our populations, but also associates with many relevant stakeholders, including banking business, taxation and Medicare. Such business engages complicated infrastructure, networks, mechanisms, policies, activities, and transactions. Data mining of such business is a brand new application area in the data mining community. Mining such social welfare business and data is challenging. The challenges come from the unavailable benchmark and experience in the data mining for this particular domain, the complexities of social welfare business and data, the exploration of possible doable tasks, and the implementation of data mining techniques in relation to the business objectives. In this thesis, which adopts a practice-based innovative attitude and focusses on the marriage of social welfare business with data mining, we believe we have realised our objective of providing a systematic and comprehensive overview of the social security and social welfare data mining. The main contributions consist of the following aspects: • As the first work of its kind, to the best of our knowledge, we present an overall picture of social security and social welfare data mining, as a new domain driven data mining application. • We explore the business nature of social security and social welfare, and the characteristics of social security data. • We propose a concept map of social security data mining, catering for main complexities of social welfare business and data, as well as providing opportunities for exploring new research issues in the community. • Several case studies are discussed, which demonstrate the technical development of social security data mining, and the innovative applications of existing data mining techniques. The nature of social welfare is spreading widely across the world in both developed and developing countries. This thesis work therefore is timely and could be of important business and government value for better understanding our people, our policies, our objectives, and for better services of those people of genuine needs

Data Mining Applications in Banking Sector While Preserving Customer Privacy

In real-life data mining applications, organizations cooperate by using each other’s data on the same data mining task for more accurate results, although they may have different security and privacy concerns. Privacy-preserving data mining (PPDM) practices involve rules and techniques that allow parties to collaborate on data mining applications while keeping their data private. The objective of this paper is to present a number of PPDM protocols and show how PPDM can be used in data mining applications in the banking sector. For this purpose, the paper discusses homomorphic cryptosystems and secure multiparty computing. Supported by experimental analysis, the paper demonstrates that data mining tasks such as clustering and Bayesian networks (association rules) that are commonly used in the banking sector can be efficiently and securely performed. This is the first study that combines PPDM protocols with applications for banking data mining. Doi: 10.28991/ESJ-2022-06-06-014 Full Text: PD

k-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data

Data Mining has wide applications in many areas such as banking, medicine, scientific research and among government agencies. Classification is one of the commonly used tasks in data mining applications. For the past decade, due to the rise of various privacy issues, many theoretical and practical solutions to the classification problem have been proposed under different security models. However, with the recent popularity of cloud computing, users now have the opportunity to outsource their data, in encrypted form, as well as the data mining tasks to the cloud. Since the data on the cloud is in encrypted form, existing privacy preserving classification techniques are not applicable. In this paper, we focus on solving the classification problem over encrypted data. In particular, we propose a secure k-NN classifier over encrypted data in the cloud. The proposed k-NN protocol protects the confidentiality of the data, user's input query, and data access patterns. To the best of our knowledge, our work is the first to develop a secure k-NN classifier over encrypted data under the semi-honest model. Also, we empirically analyze the efficiency of our solution through various experiments.Comment: 29 pages, 2 figures, 3 tables arXiv admin note: substantial text overlap with arXiv:1307.482

Efficient Privacy Preserving Distributed Clustering Based on Secret Sharing

In this paper, we propose a privacy preserving distributed clustering protocol for horizontally partitioned data based on a very efficient homomorphic additive secret sharing scheme. The model we use for the protocol is novel in the sense that it utilizes two non-colluding third parties. We provide a brief security analysis of our protocol from information theoretic point of view, which is a stronger security model. We show communication and computation complexity analysis of our protocol along with another protocol previously proposed for the same problem. We also include experimental results for computation and communication overhead of these two protocols. Our protocol not only outperforms the others in execution time and communication overhead on data holders, but also uses a more efficient model for many data mining applications

FP-tree and COFI Based Approach for Mining of Multiple Level Association Rules in Large Databases

In recent years, discovery of association rules among itemsets in a large database has been described as an important database-mining problem. The problem of discovering association rules has received considerable research attention and several algorithms for mining frequent itemsets have been developed. Many algorithms have been proposed to discover rules at single concept level. However, mining association rules at multiple concept levels may lead to the discovery of more specific and concrete knowledge from data. The discovery of multiple level association rules is very much useful in many applications. In most of the studies for multiple level association rule mining, the database is scanned repeatedly which affects the efficiency of mining process. In this research paper, a new method for discovering multilevel association rules is proposed. It is based on FP-tree structure and uses cooccurrence frequent item tree to find frequent items in multilevel concept hierarchy.Comment: Pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS, Vol. 7 No. 2, February 2010, USA. ISSN 1947 5500, http://sites.google.com/site/ijcsis

A Survey Paper on Implementing Service Oriented Architecture for Data Mining

Web service is working with the web with an object or component to achieve the communication between the distributed applications and between the different platforms through a series of protocols. Web Service provides a set of standard types systems, rules, techniques and internet service-oriented applications for communication between the different platforms, different programming languages and different types of systems to achieve interoperability. This survey paper gives the application of web service for data mining also we build a data mining model based on Web services and going forward it is possible to build a new data mining solution for security according to the prototype of a dynamic web service based data mining process system. DOI: 10.17762/ijritcc2321-8169.15079