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Collusion resistant secret sharing scheme for secure data storage and processing over cloud
Journal of Information Security and Applications ( IF 3.8 ) Pub Date : 2021-05-19 , DOI: 10.1016/j.jisa.2021.102869
Lakshmi V.S. , Deepthi S. , Deepthi P.P.

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.



中文翻译:

防共谋秘密共享方案,用于在云上安全地存储和处理数据

Shamir秘密共享(SSS)被认为是安全地外包数据的一种有前途的方法,因为它具有在确保数据可用性的同时支持隐私保护数据处理的能力。原始SSS方案的主要缺点是易受串通攻击和高存储开销。因此,在本文中,我们首先提出一种改进的SSS方案(MSSS),该方案可以抵抗串通攻击,即使有两个份额也可以提供足够的安全性。但是,扩展该方案以确保云存储系统中的数据可用性和完整性时,该方案的存储开销很高。因此,本文还提出了一种与MSSS方案相比具有减少的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涉及对数据的线性操作的任何隐私保护数据处理应用。在本文中,为了证明所提出的方案支持隐私保护数据处理的能力,以医学图像上的Haar离散小波变换(DWT)计算为例,因为DWT被广泛用于从病理图像中进行疾病诊断的特征提取。我们提出了一种从医学图像份额计算Haar DWT的算法。通过数学密码分析和对各种统计攻击的抵抗力,对所提出方案的安全性进行了评估。性能分析表明,共享域DWT的准确度与纯文本域相同。为了证明所提出的方案支持隐私保护数据处理的能力,将医学图像上的Haar离散小波变换(DWT)计算作为示例,因为DWT广泛用于从病理图像中进行疾病诊断的特征提取。我们提出了一种从医学图像份额计算Haar DWT的算法。通过数学密码分析和对各种统计攻击的抵抗力,对所提出方案的安全性进行了评估。性能分析表明,共享域DWT的准确度与纯文本域相同。为了证明所提出的方案支持隐私保护数据处理的能力,将医学图像上的Haar离散小波变换(DWT)计算作为示例,因为DWT已广泛用于从病理图像中进行疾病诊断的特征提取。我们提出了一种从医学图像份额计算Haar DWT的算法。通过数学密码分析和对各种统计攻击的抵抗力,对所提出方案的安全性进行了评估。性能分析表明,共享域DWT的准确度与纯文本域相同。我们提出了一种从医学图像份额计算Haar DWT的算法。通过数学密码分析和对各种统计攻击的抵抗力,对所提出方案的安全性进行了评估。性能分析表明,共享域DWT的准确度与纯文本域相同。我们提出了一种从医学图像份额计算Haar DWT的算法。通过数学密码分析和对各种统计攻击的抵抗力,对所提出方案的安全性进行了评估。性能分析表明,共享域DWT的准确度与纯文本域相同。

更新日期:2021-05-19
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