将大规模特征值问题秘密外包给公有云
Securely Outsourcing Large Scale Eigen Value Problem to Public Cloud
摘要：
云计算使计算能力有限的客户能够经济地将其大规模计算外包给具有巨大计算能力的公共云。云计算具有大量的存储，计算能力和软件，可供客户端使用，以减少计算开销和存储限制。但在外包的情况下，客户的机密数据的隐私必须得到维护。我们设计了一个协议，用于将大规模特征值问题外包给恶意云，该协议提供了输入/输出数据的安全性，结果的可验证性和客户端的效率。由于直接求特征向量的方法在维数很大时计算量很大，所以我们采用幂迭代法来求矩阵的最大特征值和相应的特征向量。为了保护隐私，对输入矩阵进行变换得到加密矩阵，并将加密矩阵发送到云端，然后对云端返回的结果进行解密，得到特征值问题的正确解。我们还提出了检测鲁棒作弊的结果验证机制，并提供了理论分析和实验结果，描述了所提出的协议的高效性，正确性，安全性和鲁棒的抗作弊性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
