Generating realistic scaled complex networks

Research on generative models is a central project in the emerging field of network science, and it studies how statistical patterns found in real networks could be generated by formal rules. Output from these generative models is then the basis for designing and evaluating computational methods on networks, and for verification and simulation studies. During the last two decades, a variety of models has been proposed with an ultimate goal of achieving comprehensive realism for the generated networks. In this study, we (a) introduce a new generator, termed ReCoN; (b) explore how ReCoN and some existing models can be fitted to an original network to produce a structurally similar replica, (c) use ReCoN to produce networks much larger than the original exemplar, and finally (d) discuss open problems and promising research directions. In a comparative experimental study, we find that ReCoN is often superior to many other state-of-the-art network generation methods. We argue that ReCoN is a scalable and effective tool for modeling a given network while preserving important properties at both micro- and macroscopic scales, and for scaling the exemplar data by orders of magnitude in size.Comment: 26 pages, 13 figures, extended version, a preliminary version of the paper was presented at the 5th International Workshop on Complex Networks and their Application

Exploratory study to explore the role of ICT in the process of knowledge management in an Indian business environment

In the 21st century and the emergence of a digital economy, knowledge and the knowledge base economy are rapidly growing. To effectively be able to understand the processes involved in the creating, managing and sharing of knowledge management in the business environment is critical to the success of an organization. This study builds on the previous research of the authors on the enablers of knowledge management by identifying the relationship between the enablers of knowledge management and the role played by information communication technologies (ICT) and ICT infrastructure in a business setting. This paper provides the findings of a survey collected from the four major Indian cities (Chennai, Coimbatore, Madurai and Villupuram) regarding their views and opinions about the enablers of knowledge management in business setting. A total of 80 organizations participated in the study with 100 participants in each city. The results show that ICT and ICT infrastructure can play a critical role in the creating, managing and sharing of knowledge in an Indian business environment

Privacy Preservation by Disassociation

In this work, we focus on protection against identity disclosure in the publication of sparse multidimensional data. Existing multidimensional anonymization techniquesa) protect the privacy of users either by altering the set of quasi-identifiers of the original data (e.g., by generalization or suppression) or by adding noise (e.g., using differential privacy) and/or (b) assume a clear distinction between sensitive and non-sensitive information and sever the possible linkage. In many real world applications the above techniques are not applicable. For instance, consider web search query logs. Suppressing or generalizing anonymization methods would remove the most valuable information in the dataset: the original query terms. Additionally, web search query logs contain millions of query terms which cannot be categorized as sensitive or non-sensitive since a term may be sensitive for a user and non-sensitive for another. Motivated by this observation, we propose an anonymization technique termed disassociation that preserves the original terms but hides the fact that two or more different terms appear in the same record. We protect the users' privacy by disassociating record terms that participate in identifying combinations. This way the adversary cannot associate with high probability a record with a rare combination of terms. To the best of our knowledge, our proposal is the first to employ such a technique to provide protection against identity disclosure. We propose an anonymization algorithm based on our approach and evaluate its performance on real and synthetic datasets, comparing it against other state-of-the-art methods based on generalization and differential privacy.Comment: VLDB201

Privacy-Preserving Hierarchical Anonymization Framework over Encrypted Data

Smart cities, which can monitor the real world and provide smart services in a variety of fields, have improved people's living standards as urbanization has accelerated. However, there are security and privacy concerns because smart city applications collect large amounts of privacy-sensitive information from people and their social circles. Anonymization, which generalizes data and reduces data uniqueness is an important step in preserving the privacy of sensitive information. However, anonymization methods frequently require large datasets and rely on untrusted third parties to collect and manage data, particularly in a cloud environment. In this case, private data leakage remains a critical issue, discouraging users from sharing their data and impeding the advancement of smart city services. This problem can be solved if the computational entity can perform the anonymization process without obtaining the original plain text. This study proposed a hierarchical k-anonymization framework using homomorphic encryption and secret sharing composed of two types of domains. Different computing methods are selected flexibly, and two domains are connected hierarchically to obtain higher-level anonymization results in an efficient manner. The experimental results show that connecting two domains can accelerate the anonymization process, indicating that the proposed secure hierarchical architecture is practical and efficient.Comment: 8 pages, 12 figures, submitted to IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS and under revie

The enablers and implementation model for mobile KMS in Australian healthcare

In this research project, the enablers in implementing mobile KMS in Australian regional healthcare will be investigated, and a validated framework and guidelines to assist healthcare in implementing mobile KMS will also be proposed with both qualitative and quantitative approaches. The outcomes for this study are expected to improve the understanding the enabling factors in implementing mobile KMS in Australian healthcare, as well as provide better guidelines for this process

Complementing privacy and utility trade-off with self-organising maps

This research received no external funding.Peer reviewedPublisher PD

K−means clustering microaggregation for statistical disclosure control

This paper presents a K-means clustering technique that satisfies the bi-objective function to minimize the information loss and maintain k-anonymity. The proposed technique starts with one cluster and subsequently partitions the dataset into two or more clusters such that the total information loss across all clusters is the least, while satisfying the k-anonymity requirement. The structure of K− means clustering problem is defined and investigated and an algorithm of the proposed problem is developed. The performance of the K− means clustering algorithm is compared against the most recent microaggregation methods. Experimental results show that K− means clustering algorithm incurs less information loss than the latest microaggregation methods for all of the test situations