718 research outputs found
BLOOM: BLoom filter based oblivious outsourced matchings
Whole genome sequencing has become fast, accurate, and cheap, paving the way towards the large-scale collection and processing of human genome data. Unfortunately, this dawning genome era does not only promise tremendous advances in biomedical research but also causes unprecedented privacy risks for the many. Handling storage and processing of large genome datasets through cloud services greatly aggravates these concerns. Current research efforts thus investigate the use of strong cryptographic methods and protocols to implement privacy-preserving genomic computations
Secured Data Outsourcing in Cloud Computing
Cloud computing is a popular technology in the IT world. After internet, it is the biggest thing for IT world. Cloud computing uses the Internet for performing the task on the computer and it is the next- generation architecture of IT Industry. It is related to different technologies and the convergence of various technologies has emerged to be called as cloud computing. It places the application software and databases to the huge data centers, where the supervision of the data and services may not be fully trusted. This unique attribute poses many new security challenges which have not been well understood. In this paper, we develop system which allows customer to use cloud server with various profits and strong securities. So when customer stores his sensitive data on cloud server he should not worry about securities, we also protect customer’s account from malicious behaviors by verifying the result. This result verification mechanism is highly efficient for both cloud server and cloud customer. Covering security analysis and experiment results shows the immediate practicability of our mechanism design.
DOI: 10.17762/ijritcc2321-8169.150314
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
EFFICIENT AND SECURE STORAGE OPERATIONS FOR MOBILE CLOUD COMPUTING
This paper presents a holistic security framework for securing data storage in the public cloud, with a focus on lightweight wireless data storage and retrieval devices without exposing the data content to cloud service providers
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