3,327 research outputs found
Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation
With the wide deployment of public cloud computing infrastructures, using
clouds to host data query services has become an appealing solution for the
advantages on scalability and cost-saving. However, some data might be
sensitive that the data owner does not want to move to the cloud unless the
data confidentiality and query privacy are guaranteed. On the other hand, a
secured query service should still provide efficient query processing and
significantly reduce the in-house workload to fully realize the benefits of
cloud computing. We propose the RASP data perturbation method to provide secure
and efficient range query and kNN query services for protected data in the
cloud. The RASP data perturbation method combines order preserving encryption,
dimensionality expansion, random noise injection, and random projection, to
provide strong resilience to attacks on the perturbed data and queries. It also
preserves multidimensional ranges, which allows existing indexing techniques to
be applied to speedup range query processing. The kNN-R algorithm is designed
to work with the RASP range query algorithm to process the kNN queries. We have
carefully analyzed the attacks on data and queries under a precisely defined
threat model and realistic security assumptions. Extensive experiments have
been conducted to show the advantages of this approach on efficiency and
security.Comment: 18 pages, to appear in IEEE TKDE, accepted in December 201
A secure data outsourcing scheme based on Asmuth – Bloom secret sharing
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Data outsourcing is an emerging paradigm for data management in which a database is provided as a service by third-party service providers. One of the major benefits of offering database as a service is to provide organisations, which are unable to purchase expensive hardware and software to host their databases, with efficient data storage accessible online at a cheap rate. Despite that, several issues of data confidentiality, integrity, availability and efficient indexing of users’ queries at the server side have to be addressed in the data outsourcing paradigm. Service providers have to guarantee that their clients’ data are secured against internal (insider) and external attacks. This paper briefly analyses the existing indexing schemes in data outsourcing and highlights their advantages and disadvantages. Then, this paper proposes a secure data outsourcing scheme based on Asmuth–Bloom secret sharing which tries to address the issues in data outsourcing such as data confidentiality, availability and order preservation for efficient indexing
Secure k-Nearest Neighbor Query over Encrypted Data in Outsourced Environments
For the past decade, query processing on relational data has been studied
extensively, and many theoretical and practical solutions to query processing
have been proposed under various scenarios. With the recent popularity of cloud
computing, users now have the opportunity to outsource their data as well as
the data management tasks to the cloud. However, due to the rise of various
privacy issues, sensitive data (e.g., medical records) need to be encrypted
before outsourcing to the cloud. In addition, query processing tasks should be
handled by the cloud; otherwise, there would be no point to outsource the data
at the first place. To process queries over encrypted data without the cloud
ever decrypting the data is a very challenging task. In this paper, we focus on
solving the k-nearest neighbor (kNN) query problem over encrypted database
outsourced to a cloud: a user issues an encrypted query record to the cloud,
and the cloud returns the k closest records to the user. We first present a
basic scheme and demonstrate that such a naive solution is not secure. To
provide better security, we propose a secure kNN protocol that protects the
confidentiality of the data, user's input query, and data access patterns.
Also, we empirically analyze the efficiency of our protocols through various
experiments. These results indicate that our secure protocol is very efficient
on the user end, and this lightweight scheme allows a user to use any mobile
device to perform the kNN query.Comment: 23 pages, 8 figures, and 4 table
Exploring Privacy Preservation in Outsourced K-Nearest Neighbors with Multiple Data Owners
The k-nearest neighbors (k-NN) algorithm is a popular and effective
classification algorithm. Due to its large storage and computational
requirements, it is suitable for cloud outsourcing. However, k-NN is often run
on sensitive data such as medical records, user images, or personal
information. It is important to protect the privacy of data in an outsourced
k-NN system.
Prior works have all assumed the data owners (who submit data to the
outsourced k-NN system) are a single trusted party. However, we observe that in
many practical scenarios, there may be multiple mutually distrusting data
owners. In this work, we present the first framing and exploration of privacy
preservation in an outsourced k-NN system with multiple data owners. We
consider the various threat models introduced by this modification. We discover
that under a particularly practical threat model that covers numerous
scenarios, there exists a set of adaptive attacks that breach the data privacy
of any exact k-NN system. The vulnerability is a result of the mathematical
properties of k-NN and its output. Thus, we propose a privacy-preserving
alternative system supporting kernel density estimation using a Gaussian
kernel, a classification algorithm from the same family as k-NN. In many
applications, this similar algorithm serves as a good substitute for k-NN. We
additionally investigate solutions for other threat models, often through
extensions on prior single data owner systems
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