1,284 research outputs found
State of The Art and Hot Aspects in Cloud Data Storage Security
Along with the evolution of cloud computing and cloud storage towards matu-
rity, researchers have analyzed an increasing range of cloud computing security
aspects, data security being an important topic in this area. In this paper, we
examine the state of the art in cloud storage security through an overview of
selected peer reviewed publications. We address the question of defining cloud
storage security and its different aspects, as well as enumerate the main vec-
tors of attack on cloud storage. The reviewed papers present techniques for key
management and controlled disclosure of encrypted data in cloud storage, while
novel ideas regarding secure operations on encrypted data and methods for pro-
tection of data in fully virtualized environments provide a glimpse of the toolbox
available for securing cloud storage. Finally, new challenges such as emergent
government regulation call for solutions to problems that did not receive enough
attention in earlier stages of cloud computing, such as for example geographical
location of data. The methods presented in the papers selected for this review
represent only a small fraction of the wide research effort within cloud storage
security. Nevertheless, they serve as an indication of the diversity of problems
that are being addressed
Privacy-Preserving and Outsourced Multi-User k-Means Clustering
Many techniques for privacy-preserving data mining (PPDM) have been
investigated over the past decade. Often, the entities involved in the data
mining process are end-users or organizations with limited computing and
storage resources. As a result, such entities may want to refrain from
participating in the PPDM process. To overcome this issue and to take many
other benefits of cloud computing, outsourcing PPDM tasks to the cloud
environment has recently gained special attention. We consider the scenario
where n entities outsource their databases (in encrypted format) to the cloud
and ask the cloud to perform the clustering task on their combined data in a
privacy-preserving manner. We term such a process as privacy-preserving and
outsourced distributed clustering (PPODC). In this paper, we propose a novel
and efficient solution to the PPODC problem based on k-means clustering
algorithm. The main novelty of our solution lies in avoiding the secure
division operations required in computing cluster centers altogether through an
efficient transformation technique. Our solution builds the clusters securely
in an iterative fashion and returns the final cluster centers to all entities
when a pre-determined termination condition holds. The proposed solution
protects data confidentiality of all the participating entities under the
standard semi-honest model. To the best of our knowledge, ours is the first
work to discuss and propose a comprehensive solution to the PPODC problem that
incurs negligible cost on the participating entities. We theoretically estimate
both the computation and communication costs of the proposed protocol and also
demonstrate its practical value through experiments on a real dataset.Comment: 16 pages, 2 figures, 5 table
Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data
User-generated data is crucial to predictive modeling in many applications.
With a web/mobile/wearable interface, a data owner can continuously record data
generated by distributed users and build various predictive models from the
data to improve their operations, services, and revenue. Due to the large size
and evolving nature of users data, data owners may rely on public cloud service
providers (Cloud) for storage and computation scalability. Exposing sensitive
user-generated data and advanced analytic models to Cloud raises privacy
concerns. We present a confidential learning framework, SecureBoost, for data
owners that want to learn predictive models from aggregated user-generated data
but offload the storage and computational burden to Cloud without having to
worry about protecting the sensitive data. SecureBoost allows users to submit
encrypted or randomly masked data to designated Cloud directly. Our framework
utilizes random linear classifiers (RLCs) as the base classifiers in the
boosting framework to dramatically simplify the design of the proposed
confidential boosting protocols, yet still preserve the model quality. A
Cryptographic Service Provider (CSP) is used to assist the Cloud's processing,
reducing the complexity of the protocol constructions. We present two
constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of
homomorphic encryption, garbled circuits, and random masking to achieve both
security and efficiency. For a boosted model, Cloud learns only the RLCs and
the CSP learns only the weights of the RLCs. Finally, the data owner collects
the two parts to get the complete model. We conduct extensive experiments to
understand the quality of the RLC-based boosting and the cost distribution of
the constructions. Our results show that SecureBoost can efficiently learn
high-quality boosting models from protected user-generated data
Chameleon: A Secure Cloud-Enabled and Queryable System with Elastic Properties
There are two dominant themes that have become increasingly more important in our
technological society. First, the recurrent use of cloud-based solutions which provide
infrastructures, computation platforms and storage as services. Secondly, the use of applicational
large logs for analytics and operational monitoring in critical systems. Moreover,
auditing activities, debugging of applications and inspection of events generated by errors
or potential unexpected operations - including those generated as alerts by intrusion
detection systems - are common situations where extensive logs must be analyzed, and
easy access is required. More often than not, a part of the generated logs can be deemed
as sensitive, requiring a privacy-enhancing and queryable solution.
In this dissertation, our main goal is to propose a novel approach of storing encrypted
critical data in an elastic and scalable cloud-based storage, focusing on handling JSONbased
ciphered documents. To this end, we make use of Searchable and Homomorphic
Encryption methods to allow operations on the ciphered documents. Additionally, our
solution allows for the user to be near oblivious to our system’s internals, providing
transparency while in use. The achieved end goal is a unified middleware system capable
of providing improved system usability, privacy, and rich querying over the data. This
previously mentioned objective is addressed while maintaining server-side auditable logs,
allowing for searchable capabilities by the log owner or authorized users, with integrity
and authenticity proofs.
Our proposed solution, named Chameleon, provides rich querying facilities on ciphered
data - including conjunctive keyword, ordering correlation and boolean queries
- while supporting field searching and nested aggregations. The aforementioned operations
allow our solution to provide data analytics upon ciphered JSON documents, using
Elasticsearch as our storage and search engine.O uso recorrente de soluções baseadas em nuvem tornaram-se cada vez mais importantes
na nossa sociedade. Tais soluções fornecem infraestruturas, computação e armazenamento
como serviços, para alem do uso de logs volumosos de sistemas e aplicações para
análise e monitoramento operacional em sistemas crĂticos. Atividades de auditoria, debugging
de aplicações ou inspeção de eventos gerados por erros ou possĂveis operações
inesperadas - incluindo alertas por sistemas de detecção de intrusão - são situações comuns
onde logs extensos devem ser analisados com facilidade. Frequentemente, parte dos
logs gerados podem ser considerados confidenciais, exigindo uma solução que permite
manter a confidencialidades dos dados durante procuras.
Nesta dissertação, o principal objetivo é propor uma nova abordagem de armazenar
logs crĂticos num armazenamento elástico e escalável baseado na cloud. A solução proposta
suporta documentos JSON encriptados, fazendo uso de Searchable Encryption e
métodos de criptografia homomórfica com provas de integridade e autenticação. O objetivo
alcançado é um sistema de middleware unificado capaz de fornecer privacidade,
integridade e autenticidade, mantendo registos auditáveis do lado do servidor e permitindo
pesquisas pelo proprietário dos logs ou usuários autorizados. A solução proposta,
Chameleon, visa fornecer recursos de consulta atuando em cima de dados cifrados - incluindo
queries conjuntivas, de ordenação e booleanas - suportando pesquisas de campo
e agregações aninhadas. As operações suportadas permitem à nossa solução suportar data
analytics sobre documentos JSON cifrados, utilizando o Elasticsearch como armazenamento
e motor de busca
Efficient Computation and FPGA implementation of Fully Homomorphic Encryption with Cloud Computing Significance
Homomorphic Encryption provides unique security solution for cloud computing. It ensures not only that data in cloud have confidentiality but also that data processing by cloud server does not compromise data privacy. The Fully Homomorphic Encryption (FHE) scheme proposed by Lopez-Alt, Tromer, and Vaikuntanathan (LTV), also known as NTRU(Nth degree truncated polynomial ring) based method, is considered one of the most important FHE methods suitable for practical implementation. In this thesis, an efficient algorithm and architecture for LTV Fully Homomorphic Encryption is proposed. Conventional linear feedback shift register (LFSR) structure is expanded and modified for performing the truncated polynomial ring multiplication in LTV scheme in parallel. Novel and efficient modular multiplier, modular adder and modular subtractor are proposed to support high speed processing of LFSR operations. In addition, a family of special moduli are selected for high speed computation of modular operations. Though the area keeps the complexity of O(Nn^2) with no advantage in circuit level. The proposed architecture effectively reduces the time complexity from O(N log N) to linear time, O(N), compared to the best existing works. An FPGA implementation of the proposed architecture for LTV FHE is achieved and demonstrated. An elaborate comparison of the existing methods and the proposed work is presented, which shows the proposed work gains significant speed up over existing works
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
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