Privacy-Preserving and Secure Cloud Computing: A Case of Large-Scale Nonlinear Programming

2016 ◽

pp. 200-215

Author(s):

Wei Zhang ◽

Jie Wu ◽

Yaping Lin

Keyword(s):

Cloud Computing ◽

Large Scale ◽

Keyword Search ◽

Data Encryption ◽

Privacy Preserving ◽

Security And Privacy ◽

Data Utilization ◽

Cloud Data ◽

Efficient Resource ◽

Efficient Data

Cloud computing has attracted a lot of interests from both the academics and the industries, since it provides efficient resource management, economical cost, and fast deployment. However, concerns on security and privacy become the main obstacle for the large scale application of cloud computing. Encryption would be an alternative way to relief the concern. However, data encryption makes efficient data utilization a challenging problem. To address this problem, secure and privacy preserving keyword search over large scale cloud data is proposed and widely developed. In this paper, we make a thorough survey on the secure and privacy preserving keyword search over large scale cloud data. We investigate existing research arts category by category, where the category is classified according to the search functionality. In each category, we first elaborate on the key idea of existing research works, then we conclude some open and interesting problems.

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Privacy-Preserving Outsourcing of Large-Scale Nonlinear Programming to the Cloud

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering - Security and Privacy in Communication Networks ◽

10.1007/978-3-030-01701-9_31 ◽

2018 ◽

pp. 569-587 ◽

Cited By ~ 2

Author(s):

Ang Li ◽

Wei Du ◽

Qinghua Li

Keyword(s):

Nonlinear Programming ◽

Large Scale ◽

Privacy Preserving

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An overview of biomedical platforms for managing research data

Journal of Data, Information and Management ◽

10.1007/s42488-020-00040-0 ◽

2021 ◽

Author(s):

Vivek Navale ◽

Denis von Kaeppler ◽

Matthew McAuliffe

Keyword(s):

Cloud Computing ◽

Large Scale ◽

Data Reuse ◽

Common Data Elements ◽

Web Based ◽

Bioinformatics Software ◽

Share Data ◽

Secure Cloud Computing ◽

Data Elements ◽

Access Services

AbstractBiomedical platforms provide the hardware and software to securely ingest, process, validate, curate, store, and share data. Many large-scale biomedical platforms use secure cloud computing technology for analyzing, integrating, and storing phenotypic, clinical, and genomic data. Several web-based platforms are available for researchers to access services and tools for biomedical research. The use of bio-containers can facilitate the integration of bioinformatics software with various data analysis pipelines. Adoption of Common Data Models, Common Data Elements, and Ontologies can increase the likelihood of data reuse. Managing biomedical Big Data will require the development of strategies that can efficiently leverage public cloud computing resources. The use of the research community developed standards for data collection can foster the development of machine learning methods for data processing and analysis. Increasingly platforms will need to support the integration of data from multiple disease area research.

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