7,121 research outputs found
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
CryptGraph: Privacy Preserving Graph Analytics on Encrypted Graph
Many graph mining and analysis services have been deployed on the cloud,
which can alleviate users from the burden of implementing and maintaining graph
algorithms. However, putting graph analytics on the cloud can invade users'
privacy. To solve this problem, we propose CryptGraph, which runs graph
analytics on encrypted graph to preserve the privacy of both users' graph data
and the analytic results. In CryptGraph, users encrypt their graphs before
uploading them to the cloud. The cloud runs graph analysis on the encrypted
graphs and obtains results which are also in encrypted form that the cloud
cannot decipher. During the process of computing, the encrypted graphs are
never decrypted on the cloud side. The encrypted results are sent back to users
and users perform the decryption to obtain the plaintext results. In this
process, users' graphs and the analytics results are both encrypted and the
cloud knows neither of them. Thereby, users' privacy can be strongly protected.
Meanwhile, with the help of homomorphic encryption, the results analyzed from
the encrypted graphs are guaranteed to be correct. In this paper, we present
how to encrypt a graph using homomorphic encryption and how to query the
structure of an encrypted graph by computing polynomials. To solve the problem
that certain operations are not executable on encrypted graphs, we propose hard
computation outsourcing to seek help from users. Using two graph algorithms as
examples, we show how to apply our methods to perform analytics on encrypted
graphs. Experiments on two datasets demonstrate the correctness and feasibility
of our methods
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