Big Data Privacy Context: Literature Effects On Secure Informational Assets

This article's objective is the identification of research opportunities in the current big data privacy domain, evaluating literature effects on secure informational assets. Until now, no study has analyzed such relation. Its results can foster science, technologies and businesses. To achieve these objectives, a big data privacy Systematic Literature Review (SLR) is performed on the main scientific peer reviewed journals in Scopus database. Bibliometrics and text mining analysis complement the SLR. This study provides support to big data privacy researchers on: most and least researched themes, research novelty, most cited works and authors, themes evolution through time and many others. In addition, TOPSIS and VIKOR ranks were developed to evaluate literature effects versus informational assets indicators. Secure Internet Servers (SIS) was chosen as decision criteria. Results show that big data privacy literature is strongly focused on computational aspects. However, individuals, societies, organizations and governments face a technological change that has just started to be investigated, with growing concerns on law and regulation aspects. TOPSIS and VIKOR Ranks differed in several positions and the only consistent country between literature and SIS adoption is the United States. Countries in the lowest ranking positions represent future research opportunities.Comment: 21 pages, 9 figure

Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation

With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. On the other hand, a secured query service should still provide efficient query processing and significantly reduce the in-house workload to fully realize the benefits of cloud computing. We propose the RASP data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. It also preserves multidimensional ranges, which allows existing indexing techniques to be applied to speedup range query processing. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries. We have carefully analyzed the attacks on data and queries under a precisely defined threat model and realistic security assumptions. Extensive experiments have been conducted to show the advantages of this approach on efficiency and security.Comment: 18 pages, to appear in IEEE TKDE, accepted in December 201

Secure Dynamic Cloud-based Collaboration with Hierarchical Access

In recent years, the Cloud has emerged as an attractive way of hosting and delivering services over the Internet. This has resulted in a renewed focus on information security in the case where data is stored in the virtual space of the cloud and is not physically accessible to the customer. Through this thesis the boundaries of securing data in a cloud context, while retaining the benefits of the cloud, are explored. The thesis addresses the increasing security concerns of migrating to the cloud andutilising it for data storage.The research of this thesis is divided into three separate areas: securing data in an untrusted cloud environment, ensuring data access control in the cloud, and securing data outside the cloud in the user's environment. Each area is addressed by separate conceptual designs. Together these comprise a secure dynamic cloud-based collaboration environment with hierarchical access. To further validate the conceptual designs, proof of concept prototypes have been constructed.The conceptual designs have been devised by exploring and extending the boundaries of existing secure data-storage schemes, and then combining these with well-known security principles and cutting-edge research within the field of cryptography. The results of this thesis are feasible conceptual designs for a cloud-based dynamic collaboration environment. The conceptual designs address the challenges of secure cloud-based storage and allow the benefits of cloud-based storage to be utilised. Furthermore, this thesis provides a solid foundation for further work within this field

ESPOONERBAC_{{ERBAC}}: Enforcing Security Policies In Outsourced Environments

Data outsourcing is a growing business model offering services to individuals and enterprises for processing and storing a huge amount of data. It is not only economical but also promises higher availability, scalability, and more effective quality of service than in-house solutions. Despite all its benefits, data outsourcing raises serious security concerns for preserving data confidentiality. There are solutions for preserving confidentiality of data while supporting search on the data stored in outsourced environments. However, such solutions do not support access policies to regulate access to a particular subset of the stored data. For complex user management, large enterprises employ Role-Based Access Controls (RBAC) models for making access decisions based on the role in which a user is active in. However, RBAC models cannot be deployed in outsourced environments as they rely on trusted infrastructure in order to regulate access to the data. The deployment of RBAC models may reveal private information about sensitive data they aim to protect. In this paper, we aim at filling this gap by proposing \textbf{$\mathit{ESPOON_{ERBAC}}$} for enforcing RBAC policies in outsourced environments. $\mathit{ESPOON_{ERBAC}}$ enforces RBAC policies in an encrypted manner where a curious service provider may learn a very limited information about RBAC policies. We have implemented $\mathit{ESPOON_{ERBAC}}$ and provided its performance evaluation showing a limited overhead, thus confirming viability of our approach.Comment: The final version of this paper has been accepted for publication in Elsevier Computers & Security 2013. arXiv admin note: text overlap with arXiv:1306.482

A Survey on Design and Implementation of Protected Searchable Data in the Cloud

While cloud computing has exploded in popularity in recent years thanks to the potential efficiency and cost savings of outsourcing the storage and management of data and applications, a number of vulnerabilities that led to multiple attacks have deterred many potential users. As a result, experts in the field argued that new mechanisms are needed in order to create trusted and secure cloud services. Such mechanisms would eradicate the suspicion of users towards cloud computing by providing the necessary security guarantees. Searchable Encryption is among the most promising solutions - one that has the potential to help offer truly secure and privacy-preserving cloud services. We start this paper by surveying the most important searchable encryption schemes and their relevance to cloud computing. In light of this analysis we demonstrate the inefficiencies of the existing schemes and expand our analysis by discussing certain confidentiality and privacy issues. Further, we examine how to integrate such a scheme with a popular cloud platform. Finally, we have chosen - based on the findings of our analysis - an existing scheme and implemented it to review its practical maturity for deployment in real systems. The survey of the field, together with the analysis and with the extensive experimental results provides a comprehensive review of the theoretical and practical aspects of searchable encryption