538 research outputs found
Securely Outsourcing Large Scale Eigen Value Problem to Public Cloud
Cloud computing enables clients with limited computational power to
economically outsource their large scale computations to a public cloud with
huge computational power. Cloud has the massive storage, computational power
and software which can be used by clients for reducing their computational
overhead and storage limitation. But in case of outsourcing, privacy of
client's confidential data must be maintained. We have designed a protocol for
outsourcing large scale Eigen value problem to a malicious cloud which provides
input/output data security, result verifiability and client's efficiency. As
the direct computation method to find all eigenvectors is computationally
expensive for large dimensionality, we have used power iterative method for
finding the largest Eigen value and the corresponding Eigen vector of a matrix.
For protecting the privacy, some transformations are applied to the input
matrix to get encrypted matrix which is sent to the cloud and then decrypting
the result that is returned from the cloud for getting the correct solution of
Eigen value problem. We have also proposed result verification mechanism for
detecting robust cheating and provided theoretical analysis and experimental
result that describes high-efficiency, correctness, security and robust
cheating resistance of the proposed protocol
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
Secure Cloud Computing for Solving Large-Scale Linear Systems of Equations
Solving large-scale linear systems of equations (LSEs) is one of the most common and fundamental problems in big data. But such problems are often too expensive to solve for resource-limited users. Cloud computing has been proposed as an efficient and costeffective way of solving such tasks. Nevertheless, one critical concern in cloud computing is data privacy. Many previous works on secure outsourcing of LSEs have high computational complexity and share a common serious problem, i.e., a huge number of external memory I/O operations, which may render those outsourcing schemes impractical. We develop a practical secure outsourcing algorithm for solving large-scale LSEs, which has both low computational complexity and low memory I/O complexity and can protect clients privacy well. We implement our algorithm on a real-world cloud server and a laptop. We find that the proposed algorithm offers significant time savings for the client (up to 65%) compared to previous algorithms
Numerical Problem Encryption for High-Performance Computing Applications
Recent years witnessed the diffusion of cloud-based services. Cloud services have the interesting advantage that they can provide resources (CPU, disk space, etc.) that would be too expensive to deploy and maintain in-house. A major drawback of cloud-based services is the problem of handling private data and—possibly—intellectual property to a third party. With some service (e.g., data storage), cryptography can provide a solution; however, there are some services that are more difficult to protect. An example of such services is the renting of CPU to carry out numerical computation such as differential equation solving. In this chapter, we discuss the problem of encrypting numerical problems so that their solution can be safely outsourced. The idea is to transform (encrypt) a given numerical problem into a different one whose solution can be mapped back to the solution of the original problem if the key used at the encryption stage is known
Privacy-Preserving Cloud-Assisted Data Analytics
Nowadays industries are collecting a massive and exponentially growing amount of data that can be utilized to extract useful insights for improving various aspects of our life. Data analytics (e.g., via the use of machine learning) has been extensively applied to make important decisions in various real world applications. However, it is challenging for resource-limited clients to analyze their data in an efficient way when its scale is large. Additionally, the data resources are increasingly distributed among different owners. Nonetheless, users\u27 data may contain private information that needs to be protected.
Cloud computing has become more and more popular in both academia and industry communities. By pooling infrastructure and servers together, it can offer virtually unlimited resources easily accessible via the Internet. Various services could be provided by cloud platforms including machine learning and data analytics.
The goal of this dissertation is to develop privacy-preserving cloud-assisted data analytics solutions to address the aforementioned challenges, leveraging the powerful and easy-to-access cloud. In particular, we propose the following systems.
To address the problem of limited computation power at user and the need of privacy protection in data analytics, we consider geometric programming (GP) in data analytics, and design a secure, efficient, and verifiable outsourcing protocol for GP. Our protocol consists of a transform scheme that converts GP to DGP, a transform scheme with computationally indistinguishability, and an efficient scheme to solve the transformed DGP at the cloud side with result verification. Evaluation results show that the proposed secure outsourcing protocol can achieve significant time savings for users.
To address the problem of limited data at individual users, we propose two distributed learning systems such that users can collaboratively train machine learning models without losing privacy. The first one is a differentially private framework to train logistic regression models with distributed data sources. We employ the relevance between input data features and the model output to significantly improve the learning accuracy. Moreover, we adopt an evaluation data set at the cloud side to suppress low-quality data sources and propose a differentially private mechanism to protect user\u27s data quality privacy. Experimental results show that the proposed framework can achieve high utility with low quality data, and strong privacy guarantee.
The second one is an efficient privacy-preserving federated learning system that enables multiple edge users to collaboratively train their models without revealing dataset. To reduce the communication overhead, we select well-aligned and large-enough magnitude gradients for uploading which leads to quick convergence. To minimize the noise added and improve model utility, each user only adds a small amount of noise to his selected gradients, encrypts the noise gradients before uploading, and the cloud server will only get the aggregate gradients that contain enough noise to achieve differential privacy. Evaluation results show that the proposed system can achieve high accuracy, low communication overhead, and strong privacy guarantee.
In future work, we plan to design a privacy-preserving data analytics with fair exchange, which ensures the payment fairness. We will also consider designing distributed learning systems with heterogeneous architectures
Outsourced Analysis of Encrypted Graphs in the Cloud with Privacy Protection
Huge diagrams have unique properties for organizations and research, such as
client linkages in informal organizations and customer evaluation lattices in
social channels. They necessitate a lot of financial assets to maintain because
they are large and frequently continue to expand. Owners of large diagrams may
need to use cloud resources due to the extensive arrangement of open cloud
resources to increase capacity and computation flexibility. However, the
cloud's accountability and protection of schematics have become a significant
issue. In this study, we consider calculations for security savings for
essential graph examination practices: schematic extraterrestrial examination
for outsourcing graphs in the cloud server. We create the security-protecting
variants of the two proposed Eigen decay computations. They are using two
cryptographic algorithms: additional substance homomorphic encryption (ASHE)
strategies and some degree homomorphic encryption (SDHE) methods. Inadequate
networks also feature a distinctively confidential info adaptation convention
to allow the trade-off between secrecy and data sparseness. Both dense and
sparse structures are investigated. According to test results, calculations
with sparse encoding can drastically reduce information. SDHE-based strategies
have reduced computing time, while ASHE-based methods have reduced stockpiling
expenses
- …