O SlideShare utiliza cookies para otimizar a funcionalidade e o desempenho do site, assim como para apresentar publicidade mais relevante aos nossos usuarios. IT professionals are becoming more confident in the security of the public cloud compared to corporate data centres, according to the latest study from IT consulting firm SADA Systems. The poll, which quizzed more than 200 IT managers around their use of public cloud services, found that 84% of respondents were using some form of public cloud infrastructure today.
When Amazon launched Aurora in 2014, it was presented as a clear challenge to giants in the $34 billion database market. Google has announced the acquisition of Orbitera, a cloud commerce platform, to help boost its multi-cloud efforts with one eye on rivals Amazon Web Services (AWS) and Microsoft. In a 2015 Forrester Consulting survey, more than 60% of businesses said issues with transparency were stalling further expansion into the cloud. Cloud storage firm Dropbox has announced a new customer win in the form of sports brand The Adidas Group. Google has launched customer-supplied encryption keys (CESK) for its Compute Engine infrastructure as a service (IaaS), which enables organisations to better protect their cloudy data.
For all those who may deride Gartner’s Magic Quadrants across its various subject areas, it does provide a clear cut snapshot of when a market has sufficiently matured.
Microsoft has announced it has obtained ISO 27017 compliance, a new cloud-based security certification published at the end of last year. The certification from the ISO, the global organisation which has published more than 21,000 international standards across a variety of industries, is newer and subsequently less known than the ISO 27018 standard, which sets out guidelines to protect personally identifiable information (PII). While public cloud implementations are steadily increasing, private clouds in customers’ own data centres continue to be deployed because of the perceived higher levels of security and control they offer.
But the management of a private cloud can be complex and many organisations underestimate the scale of the challenge.
Amazon Web Services (AWS), Microsoft, IBM and Google continue to grow more quickly than their smaller competitors and own between them more than half of the global cloud infrastructure service market. Software defined infrastructure (SDx), along with use of private and public clouds completely transforms the way IT departments manage enterprise data centres and workloads. In the past, when organisations deployed new applications, the application owner needed to collaborate with several teams. This time last year, Fruition Partners, an IT solutions provider, released a report which assessed the state of cloud adoption through the eyes of the CIO. Cloud Tech promotes industry thought leadership content from industry brands, businesses and analysts, partnering with writers and bloggers to deliver insight and advice on cloud IT strategy to our extensive audience of CIOs and IT managers.
The ever-growing computing and networking technologies today are enabling a fundamental paradigm shift in how people deploy and deliver computing services: computing outsourcing.
A fundamental concern to move computational workloads from private resources to the cloud is the protection of the confidential data that the computation consumes and produces. WIth above challenges in mind, our proposed approach is to understand the nature of an application and its security requirements and develop application-specific solutions that are highly customized and achieve desirable trade-offs among privacy protection, performance, and other factors. The research has resulted in publications in IEEE TPDS (to appear), IEEE INFOCOM'11 and ICDCS'11. Cong Wang, Kui Ren, Jia Wang, and Qian Wang, "Harnessing the Cloud for Securely Outsourcing Large-scale Systems of Linear Equations," to appear, IEEE Transactions on Parallel and Distributed Systems (TPDS), 2012. The 30th IEEE Conference on Computer Communications (INFOCOM'11), Shanghai, China, April 10-15, 2011. Cong Wang, Kui Ren, Jia Wang, and Karthik Mahendra Raje Urs, "Harnessing the Cloud for Securely Solving Large Systems of Linear Equations," The 31st International Conference on Distributed Computing Systems (ICDCS'11), Minneapolis, MN, June 20-24, 2011. When data services are increasingly outsourced to cloud for its greater flexibility and cost-efficiency, sensitive data has to be encrypted before outsourcing to combat unsolicited accesses in cloud and beyond. Our proposed research starts from enabling versatile keyword search over encrypted data that is highly usable, including the functionalities like fuzzy tolerance, result ranking, multi-keywords search, similarity search, etc. Cong Wang, Ning Cao, Kui Ren, and Wenjing Lou, "Enabling Secure and Efficient Ranked Keyword Search over Outsourced Cloud Data," IEEE Transactions on Parallel and Distributed Systems (TPDS), vol.

Kui Ren, Cong Wang, and Qian Wang, "Toward Secure and Effective Data Utilization in Public Cloud," to appear, IEEE Network Magazine, 2012. The 31th IEEE Conference on Computer Communications (INFOCOM'12), Orlando, FL, March, 25-30, 2012. Cong Wang, Ning Cao, Jin Li, Kui Ren, and Wenjing Lou, "Secure Ranked Keyword Search over Encrypted Cloud Data," The 30th International Conference on Distributed Computing Systems (ICDCS'10), Genoa, Italy, June 21-25, 2010. Ning Cao, Cong Wang, Ming Li, Kui Ren, and Wenjing Lou, "Privacy-Preserving Multi-keyword Ranked Search over Encrypted Cloud Data", The 30th IEEE Conference on Computer Communications (INFOCOM'11), Shanghai, China, April 10-15, 2011.
Jin Li, Qian Wang, Cong Wang, Ning Cao, Kui Ren, and Wenjing Lou, "Fuzzy Keyword Search over Encrypted Data in Cloud Computing", The 29th IEEE Conference on Computer Communications (INFOCOM'10), mini-conference, San Diego, CA, March 15-19, 2010.
Outsourcing storage into the cloud relieves data users from the burden of direct hardware and software management and operational overhead therein.
In light of the critical need of a unified storage auditing architecture for this nascent cloud economy to become fully established, our research aims to develop a secure cloud storage system supporting privacy-preserving third-party auditing, whereas three indispensable design properties from a system-usability viewpoint are to be realized. A preliminary version of this paper appeared at the 29th IEEE Conference on Computer Communications (INFOCOM'10).
Qian Wang, Cong Wang, Kui Ren, Wenjing Lou, and Jin Li, "Enabling Public Verifiability and Data Dynamics for Storage Security in Cloud Computing", To appear, IEEE Transactions on Parallel and Distributed Systems (TPDS), Vol. Cong Wang, Qian Wang, Kui Ren, and Wenjing Lou, "Towards Secure and Dependable Storage Services in Cloud Computing," IEEE Transactions on Service Computing (TSC), vol. A preliminary version of this paper appeared at the 17th IEEE International Workshop on Quality of Service (IWQoS'09). Cong Wang, Kui Ren, Wenjing Lou, and Jin Li, "Towards Publicly Auditable Secure Cloud Data Storage Services", IEEE Network Magazine, Vol. Qian Wang, Cong Wang, Jin Li, Kui Ren, and Wenjing Lou, "Enabling Public Verifiability and Data Dynamics for Storage Security in Cloud Computing", The 14th European Symposium on Research in Computer Security (ESORICS'09), Saint Malo, France, September 21-23, 2009. Cong Wang, Qian Wang, Kui Ren, and Wenjing Lou, "Privacy-preserving Public Auditing for Data Storage Security in Cloud Computing", The 29th IEEE Conference on Computer Communications (INFOCOM'10), San Diego, CA, March 15-19, 2010. Various sensitive data pooled in the cloud demands the cloud data sharing service to be responsible for secure, efficient and reliable enforcement of data content access among potentially large number of users on behalf of data owners. We have been investigating advanced cryptographic techniques, including attribute-based encryption, proxy re-encryption, lazy re-encryption, to address these challenges.
Shucheng Yu, Cong Wang, Kui Ren, and Wenjing Lou, "Attribute Based Data Sharing with Attribute Revocation", The 5th ACM Symposium on Information, Computer and Communications Security (ASIACCS'10), Beijing, China, April 13-16, 2010. Shucheng Yu, Cong Wang, Kui Ren, and Wenjing Lou, "Achieving Secure, Scalable, and Fine-grained Data Dccess Control in Cloud Computing", The 29th IEEE Conference on Computer Communications (INFOCOM'10), San Diego, CA, March 15-19, 2010.
Disclaimer: The papers here are made available for timely dissemination of scholarly and technical work. The Office for National Statistics (ONS) recently stated that last year saw nearly six million fraud and cyber crimes take place in 2015, in the UK. However, as businesses are beginning to closely examine what having data in the cloud entails, they’re discovering that their relationships with cloud vendors are sometimes, well, cloudy.
Automation is a key component of software defined networking (SDN), bringing together network, server, security management and other IT functions or teams together.
The study bemoaned the rise of shadow IT and gave three best practice tips to control cloud sprawl, by focusing on the user to provide a better service, focusing on business needs, and focusing on IT strategy and how to make better use of IT service management (ITSM). While moving data and computing services to the cloud alike infrastructures promises to provide unprecedented benefits like ubiquitous network access, rapid resource elasticity, minimal management overhead, etc., it simultaneously deprives users of direct control over the systems that manage their data and applications, raising security and privacy the primary obstacles to the public cloud adoption. Thus, secure computation outsourcing services are in great need to not only protect sensitive workload information but validate the integrity of the computation result.
We start from widely applicable engineering computing and optimization problems, which are essential for modern engineering designs, and usually require a substantial amount of computational power and involve confidential data. The study would prepare a solid knowledge base and provide insights for further research on outsourcing more advanced computation problems. However, the encryption makes deployment of traditional data utilization service, such as plaintext keyword search over textual data or query over database, a difficult task.

At the same time, though, cloud storage service is also relinquishing users' physical control of their outsourced data, which inevitably poses new security risks towards the correctness of the data in cloud. Firstly, to allow a third party auditor (TPA) to efficiently audit the cloud storage security, the design must be lightweight and must not introduce any online burden to the users. As cloud server may no longer be in the same trusted domain as the data owners, we have to rethink the problem of access control in this open environment, where cloud server takes full charge of the management of the outsourced data but are not necessarily trusted with respect to the data confidentiality. We aim to provide tools not only extending owners' full control over cloud data access, but enabling effective user revocation while introducing minimum management and online burden on the data owner for the overall access policy enforcement. My current research thus focuses on helping users regain confidence when deploying the most fundamental data and computing services, including data storage, data search, data sharing, and data computation outsourcing, on the commercial public cloud. This is, however, a very difficult task due to a number of challenges that have to be met simultaneously.
Aiming for a practical solution, our proposed methodology is to explicitly decompose computations into public programs running on the cloud and private data owned by the users. Efficient techniques for searching such high dimensional encrypted data have to be developed.
Traditional cryptographic primitives for the purpose of storage correctness protection cannot be utilized, as they usually require a local copy of the data for integrity verification, which isn't viable when storage is outsourced. Secondly, for user data privacy and regulation compliance, it must be guaranteed that no data information should be leaked to TPA during the data auditing process.
What makes the problem more challenging is the enforcement of fine-grained data access, the support of access privilege updates in dynamic scenarios, and the system scalability, while maintaining low level complexity of key management and data encryption. Take insurance fraud for instance; personal premiums may rise as insurance providers seek to recover the money lost. To address these challenges, one needs a firm foundation in big data and system security, cloud computing, large-scale computing, information assurance, access control, and computer and mobile networks; I am interested in all these areas and always feel passionate about designing, measuring, and analyzing broad range of security and privacy mechanisms to address related problems.
Firstly, such a service has to be practically feasible (immediate practicality) in terms of computational complexity. By organizing computation problems at various abstraction levels into a hierarchy, it is possible to leverage the structures of specific computations for achieving desirable trade-offs among security, efficiency, and practicality in a systematic manner.
Besides, aside from eliminating the local storage management, storing data into the cloud serves no purpose unless they can be easily searched and utilized. Our ultimate goal is to enable rich search semantics in a privacy preserving manner and efficiently support for large-scale and distributed nature of cloud data.
Besides, the large amount of cloud data and users' constrained computing capabilities further makes the task of data correctness auditing in a cloud environment expensive and even formidable. Thirdly, given cloud data's dynamic features, the auditing mechanism must support fully dynamic data operations while satisfying the requirements of data integrity protection.
Secondly, it has to provide sound security guarantee without restricted system assumptions.
Critical applications that we are currently investigating include secure outsourcing large scale systems of linear equations, linear programming, and convex optimization in the cloud. This necessitates the need for developing effective searching techniques over encrypted cloud data of massive scale.
Thirdly, it also has to enable substantial computational savings at the end-user's side as compared to the amount of the efforts that otherwise has to be committed to solve the problem locally. Such techniques should enable critical search functionalities that have long been enjoyed in modern search engine over unencrypted data, like Google, Bing, etc.
These challenges practically exclude the applicability of the existing techniques developed in the context of secure multi-party computation and fully homomorphic encryption. The adequacy of such techniques is essential to the long-term success of the cloud services and the ultimate privacy protection of both individuals and organizations.

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