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Collusion resistant secret sharing scheme for secure data storage and processing over cloud

机译:用于安全数据存储和云处理的串谋秘密共享方案

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Shamir secret sharing (SSS) is considered as a promising method for outsourcing the data securely due to its ability to support privacy preserving data processing while ensuring data availability. Major drawbacks of original SSS scheme are its susceptibility to collusion attack and high storage overhead. Hence in this paper, we first propose a modified SSS scheme (MSSS) which can resist collusion attack and provide adequate security even with two shares. However, the storage overhead of this scheme is high when it is extended to ensure data availability and integrity in cloud storage systems. Therefore, a modified ramp secret sharing (MRSS) with reduced storage overhead compared to MSSS scheme is also proposed in this paper. The proposed schemes can be employed for any privacy preserving data processing application which involve linear operations on the data. In this paper, in order to demonstrate the capability of proposed schemes to support privacy preserving data processing, Haar discrete wavelet transform (DWT) computation on medical images is considered as an example as DWT is widely used in feature extraction for disease diagnosis from pathological images. We present an algorithm for computing Haar DWT from medical image shares. The security of the proposed scheme is evaluated through mathematical cryptanalysis and resistance against various statistical attacks. The performance analysis shows that shared domain DWT offers same accuracy levels as that of plaintext domain.
机译:Shamir秘密共享(SSS)被认为是安全性地安全地将数据外包的方法安全,因为它能够在确保数据可用性的同时支持隐私保留数据处理。原始SSS方案的主要缺点是其对勾结攻击和高存储开销的易感性。因此,在本文中,我们首先提出了一种改进的SSS方案(MSS),即使有两股股票,也可以抵抗勾结攻击并提供足够的安全性。但是,在扩展时,此方案的存储开销很高,以确保云存储系统中的数据可用性和完整性。因此,本文还提出了与MSSS方案相比减少了存储开销的改进的斜坡秘密共享(MRSS)。所提出的方案可以用于任何隐私保留数据处理应用程序,这些应用程序涉及对数据的线性操作。在本文中,为了证明所提出的支持隐私保留数据处理的方案的能力,医学图像上的HAAR离散小波变换(DWT)计算被认为是DWT广泛用于来自病理图像的疾病诊断的特征提取。我们提出了一种从医学图像共享计算Haar DWT的算法。通过数学密码分析和对各种统计攻击的抵抗来评估所提出的方案的安全性。性能分析表明,共享域DWT提供与明文域相同的准确度。

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