1,173 research outputs found
A comprehensive meta-analysis of cryptographic security mechanisms for cloud computing
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The concept of cloud computing offers measurable computational or information resources as a service over the Internet. The major motivation behind the cloud setup is economic benefits, because it assures the reduction in expenditure for operational and infrastructural purposes. To transform it into a reality there are some impediments and hurdles which are required to be tackled, most profound of which are security, privacy and reliability issues. As the user data is revealed to the cloud, it departs the protection-sphere of the data owner. However, this brings partly new security and privacy concerns. This work focuses on these issues related to various cloud services and deployment models by spotlighting their major challenges. While the classical cryptography is an ancient discipline, modern cryptography, which has been mostly developed in the last few decades, is the subject of study which needs to be implemented so as to ensure strong security and privacy mechanisms in today’s real-world scenarios. The technological solutions, short and long term research goals of the cloud security will be described and addressed using various classical cryptographic mechanisms as well as modern ones. This work explores the new directions in cloud computing security, while highlighting the correct selection of these fundamental technologies from cryptographic point of view
Security and Privacy Issues of Big Data
This chapter revises the most important aspects in how computing
infrastructures should be configured and intelligently managed to fulfill the
most notably security aspects required by Big Data applications. One of them is
privacy. It is a pertinent aspect to be addressed because users share more and
more personal data and content through their devices and computers to social
networks and public clouds. So, a secure framework to social networks is a very
hot topic research. This last topic is addressed in one of the two sections of
the current chapter with case studies. In addition, the traditional mechanisms
to support security such as firewalls and demilitarized zones are not suitable
to be applied in computing systems to support Big Data. SDN is an emergent
management solution that could become a convenient mechanism to implement
security in Big Data systems, as we show through a second case study at the end
of the chapter. This also discusses current relevant work and identifies open
issues.Comment: In book Handbook of Research on Trends and Future Directions in Big
Data and Web Intelligence, IGI Global, 201
Dark clouds on the horizon:the challenge of cloud forensics
We introduce the challenges to digital forensics introduced by the advent and adoption of technologies, such as encryption, secure networking, secure processors and anonymous routing. All potentially render current approaches to digital forensic investigation unusable. We explain how the Cloud, due to its global distribution and multi-jurisdictional nature, exacerbates these challenges. The latest developments in the computing milieu threaten a complete “evidence blackout” with severe implications for the detection, investigation and prosecution of cybercrime. In this paper, we review the current landscape of cloud-based forensics investigations. We posit a number of potential solutions. Cloud forensic difficulties can only be addressed if we acknowledge its socio-technological nature, and design solutions that address both human and technological dimensions. No firm conclusion is drawn; rather the objective is to present a position paper, which will stimulate debate in the area and move the discipline of digital cloud forensics forward. Thus, the paper concludes with an invitation to further informed debate on this issue
A secure data outsourcing scheme based on Asmuth – Bloom secret sharing
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Data outsourcing is an emerging paradigm for data management in which a database is provided as a service by third-party service providers. One of the major benefits of offering database as a service is to provide organisations, which are unable to purchase expensive hardware and software to host their databases, with efficient data storage accessible online at a cheap rate. Despite that, several issues of data confidentiality, integrity, availability and efficient indexing of users’ queries at the server side have to be addressed in the data outsourcing paradigm. Service providers have to guarantee that their clients’ data are secured against internal (insider) and external attacks. This paper briefly analyses the existing indexing schemes in data outsourcing and highlights their advantages and disadvantages. Then, this paper proposes a secure data outsourcing scheme based on Asmuth–Bloom secret sharing which tries to address the issues in data outsourcing such as data confidentiality, availability and order preservation for efficient indexing
End-to-End Privacy for Open Big Data Markets
The idea of an open data market envisions the creation of a data trading
model to facilitate exchange of data between different parties in the Internet
of Things (IoT) domain. The data collected by IoT products and solutions are
expected to be traded in these markets. Data owners will collect data using IoT
products and solutions. Data consumers who are interested will negotiate with
the data owners to get access to such data. Data captured by IoT products will
allow data consumers to further understand the preferences and behaviours of
data owners and to generate additional business value using different
techniques ranging from waste reduction to personalized service offerings. In
open data markets, data consumers will be able to give back part of the
additional value generated to the data owners. However, privacy becomes a
significant issue when data that can be used to derive extremely personal
information is being traded. This paper discusses why privacy matters in the
IoT domain in general and especially in open data markets and surveys existing
privacy-preserving strategies and design techniques that can be used to
facilitate end to end privacy for open data markets. We also highlight some of
the major research challenges that need to be address in order to make the
vision of open data markets a reality through ensuring the privacy of
stakeholders.Comment: Accepted to be published in IEEE Cloud Computing Magazine: Special
Issue Cloud Computing and the La
Privacy-preserving Cross-domain Routing Optimization -- A Cryptographic Approach
Today's large-scale enterprise networks, data center networks, and wide area
networks can be decomposed into multiple administrative or geographical
domains. Domains may be owned by different administrative units or
organizations. Hence protecting domain information is an important concern.
Existing general-purpose Secure Multi-Party Computation (SMPC) methods that
preserves privacy for domains are extremely slow for cross-domain routing
problems. In this paper we present PYCRO, a cryptographic protocol specifically
designed for privacy-preserving cross-domain routing optimization in Software
Defined Networking (SDN) environments. PYCRO provides two fundamental routing
functions, policy-compliant shortest path computing and bandwidth allocation,
while ensuring strong protection for the private information of domains. We
rigorously prove the privacy guarantee of our protocol. We have implemented a
prototype system that runs PYCRO on servers in a campus network. Experimental
results using real ISP network topologies show that PYCRO is very efficient in
computation and communication costs
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