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

Enabling Privacy-preserving Auctions in Big Data

We study how to enable auctions in the big data context to solve many upcoming data-based decision problems in the near future. We consider the characteristics of the big data including, but not limited to, velocity, volume, variety, and veracity, and we believe any auction mechanism design in the future should take the following factors into consideration: 1) generality (variety); 2) efficiency and scalability (velocity and volume); 3) truthfulness and verifiability (veracity). In this paper, we propose a privacy-preserving construction for auction mechanism design in the big data, which prevents adversaries from learning unnecessary information except those implied in the valid output of the auction. More specifically, we considered one of the most general form of the auction (to deal with the variety), and greatly improved the the efficiency and scalability by approximating the NP-hard problems and avoiding the design based on garbled circuits (to deal with velocity and volume), and finally prevented stakeholders from lying to each other for their own benefit (to deal with the veracity). We achieve these by introducing a novel privacy-preserving winner determination algorithm and a novel payment mechanism. Additionally, we further employ a blind signature scheme as a building block to let bidders verify the authenticity of their payment reported by the auctioneer. The comparison with peer work shows that we improve the asymptotic performance of peer works' overhead from the exponential growth to a linear growth and from linear growth to a logarithmic growth, which greatly improves the scalability

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

ToR K-Anonymity against deep learning watermarking attacks

It is known that totalitarian regimes often perform surveillance and censorship of their communication networks. The Tor anonymity network allows users to browse the Internet anonymously to circumvent censorship filters and possible prosecution. This has made Tor an enticing target for state-level actors and cooperative state-level adversaries, with privileged access to network traffic captured at the level of Autonomous Systems(ASs) or Internet Exchange Points(IXPs). This thesis studied the attack typologies involved, with a particular focus on traffic correlation techniques for de-anonymization of Tor endpoints. Our goal was to design a test-bench environment and tool, based on recently researched deep learning techniques for traffic analysis, to evaluate the effectiveness of countermeasures provided by recent ap- proaches that try to strengthen Tor’s anonymity protection. The targeted solution is based on K-anonymity input covert channels organized as a pre-staged multipath network. The research challenge was to design a test-bench environment and tool, to launch active correlation attacks leveraging traffic flow correlation through the detection of in- duced watermarks in Tor traffic. To de-anonymize Tor connection endpoints, our tool analyses intrinsic time patterns of Tor synthetic egress traffic to detect flows with previ- ously injected time-based watermarks. With the obtained results and conclusions, we contributed to the evaluation of the security guarantees that the targeted K-anonymity solution provides as a countermeasure against de-anonymization attacks.Já foi extensamente observado que em vários países governados por regimes totalitários existe monitorização, e consequente censura, nos vários meios de comunicação utilizados. O Tor permite aos seus utilizadores navegar pela internet com garantias de privacidade e anonimato, de forma a evitar bloqueios, censura e processos legais impostos pela entidade que governa. Estas propriedades tornaram a rede Tor um alvo de ataque para vários governos e ações conjuntas de várias entidades, com acesso privilegiado a extensas zonas da rede e vários pontos de acesso à mesma. Esta tese realiza o estudo de tipologias de ataques que quebram o anonimato da rede Tor, com especial foco em técnicas de correlação de tráfegos. O nosso objetivo é realizar um ambiente de estudo e ferramenta, baseada em técnicas recentes de aprendizagem pro- funda e injeção de marcas de água, para avaliar a eficácia de contramedidas recentemente investigadas, que tentam fortalecer o anonimato da rede Tor. A contramedida que pre- tendemos avaliar é baseada na criação de multi-circuitos encobertos, recorrendo a túneis TLS de entrada, de forma a acoplar o tráfego de um grupo anonimo de K utilizadores. A solução a ser desenvolvida deve lançar um ataque de correlação de tráfegos recorrendo a técnicas ativas de indução de marcas de água. Esta ferramenta deve ser capaz de correla- cionar tráfego sintético de saída de circuitos Tor, realizando a injeção de marcas de água à entrada com o propósito de serem detetadas num segundo ponto de observação. Aplicada a um cenário real, o propósito da ferramenta está enquadrado na quebra do anonimato de serviços secretos fornecidos pela rede Tor, assim como os utilizadores dos mesmos. Os resultados esperados irão contribuir para a avaliação da solução de anonimato de K utilizadores mencionada, que é vista como contramedida para ataques de desanonimi- zação

Fant\^omas: Understanding Face Anonymization Reversibility

Face images are a rich source of information that can be used to identify individuals and infer private information about them. To mitigate this privacy risk, anonymizations employ transformations on clear images to obfuscate sensitive information, all while retaining some utility. Albeit published with impressive claims, they sometimes are not evaluated with convincing methodology. Reversing anonymized images to resemble their real input -- and even be identified by face recognition approaches -- represents the strongest indicator for flawed anonymization. Some recent results indeed indicate that this is possible for some approaches. It is, however, not well understood, which approaches are reversible, and why. In this paper, we provide an exhaustive investigation in the phenomenon of face anonymization reversibility. Among other things, we find that 11 out of 15 tested face anonymizations are at least partially reversible and highlight how both reconstruction and inversion are the underlying processes that make reversal possible

Big privacy: challenges and opportunities of privacy study in the age of big data

One of the biggest concerns of big data is privacy. However, the study on big data privacy is still at a very early stage. We believe the forthcoming solutions and theories of big data privacy root from the in place research output of the privacy discipline. Motivated by these factors, we extensively survey the existing research outputs and achievements of the privacy field in both application and theoretical angles, aiming to pave a solid starting ground for interested readers to address the challenges in the big data case. We first present an overview of the battle ground by defining the roles and operations of privacy systems. Second, we review the milestones of the current two major research categories of privacy: data clustering and privacy frameworks. Third, we discuss the effort of privacy study from the perspectives of different disciplines, respectively. Fourth, the mathematical description, measurement, and modeling on privacy are presented. We summarize the challenges and opportunities of this promising topic at the end of this paper, hoping to shed light on the exciting and almost uncharted land