3,185 research outputs found
Cloud-based Quadratic Optimization with Partially Homomorphic Encryption
The development of large-scale distributed control systems has led to the
outsourcing of costly computations to cloud-computing platforms, as well as to
concerns about privacy of the collected sensitive data. This paper develops a
cloud-based protocol for a quadratic optimization problem involving multiple
parties, each holding information it seeks to maintain private. The protocol is
based on the projected gradient ascent on the Lagrange dual problem and
exploits partially homomorphic encryption and secure multi-party computation
techniques. Using formal cryptographic definitions of indistinguishability, the
protocol is shown to achieve computational privacy, i.e., there is no
computationally efficient algorithm that any involved party can employ to
obtain private information beyond what can be inferred from the party's inputs
and outputs only. In order to reduce the communication complexity of the
proposed protocol, we introduced a variant that achieves this objective at the
expense of weaker privacy guarantees. We discuss in detail the computational
and communication complexity properties of both algorithms theoretically and
also through implementations. We conclude the paper with a discussion on
computational privacy and other notions of privacy such as the non-unique
retrieval of the private information from the protocol outputs
Privacy preserving distributed optimization using homomorphic encryption
This paper studies how a system operator and a set of agents securely execute
a distributed projected gradient-based algorithm. In particular, each
participant holds a set of problem coefficients and/or states whose values are
private to the data owner. The concerned problem raises two questions: how to
securely compute given functions; and which functions should be computed in the
first place. For the first question, by using the techniques of homomorphic
encryption, we propose novel algorithms which can achieve secure multiparty
computation with perfect correctness. For the second question, we identify a
class of functions which can be securely computed. The correctness and
computational efficiency of the proposed algorithms are verified by two case
studies of power systems, one on a demand response problem and the other on an
optimal power flow problem.Comment: 24 pages, 5 figures, journa
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
Privacy-Preserving Outsourcing of Large-Scale Nonlinear Programming to the Cloud
The increasing massive data generated by various sources has given birth to
big data analytics. Solving large-scale nonlinear programming problems (NLPs)
is one important big data analytics task that has applications in many domains
such as transport and logistics. However, NLPs are usually too computationally
expensive for resource-constrained users. Fortunately, cloud computing provides
an alternative and economical service for resource-constrained users to
outsource their computation tasks to the cloud. However, one major concern with
outsourcing NLPs is the leakage of user's private information contained in NLP
formulations and results. Although much work has been done on
privacy-preserving outsourcing of computation tasks, little attention has been
paid to NLPs. In this paper, we for the first time investigate secure
outsourcing of general large-scale NLPs with nonlinear constraints. A secure
and efficient transformation scheme at the user side is proposed to protect
user's private information; at the cloud side, generalized reduced gradient
method is applied to effectively solve the transformed large-scale NLPs. The
proposed protocol is implemented on a cloud computing testbed. Experimental
evaluations demonstrate that significant time can be saved for users and the
proposed mechanism has the potential for practical use.Comment: Ang Li and Wei Du equally contributed to this work. This work was
done when Wei Du was at the University of Arkansas. 2018 EAI International
Conference on Security and Privacy in Communication Networks (SecureComm
Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General Framework
As the modern world becomes increasingly digitized and interconnected,
distributed signal processing has proven to be effective in processing its
large volume of data. However, a main challenge limiting the broad use of
distributed signal processing techniques is the issue of privacy in handling
sensitive data. To address this privacy issue, we propose a novel yet general
subspace perturbation method for privacy-preserving distributed optimization,
which allows each node to obtain the desired solution while protecting its
private data. In particular, we show that the dual variables introduced in each
distributed optimizer will not converge in a certain subspace determined by the
graph topology. Additionally, the optimization variable is ensured to converge
to the desired solution, because it is orthogonal to this non-convergent
subspace. We therefore propose to insert noise in the non-convergent subspace
through the dual variable such that the private data are protected, and the
accuracy of the desired solution is completely unaffected. Moreover, the
proposed method is shown to be secure under two widely-used adversary models:
passive and eavesdropping. Furthermore, we consider several distributed
optimizers such as ADMM and PDMM to demonstrate the general applicability of
the proposed method. Finally, we test the performance through a set of
applications. Numerical tests indicate that the proposed method is superior to
existing methods in terms of several parameters like estimated accuracy,
privacy level, communication cost and convergence rate
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