利用改进的 ECC 和 Paillier 同态加密算法加强大数据静态安全存储

HU Rong, Ping Huang
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摘要

:随着大数据的快速增长,确保其存储安全已变得至关重要。本研究建议通过改进加密算法来加强 Hadoop 中静态大数据的安全存储。针对非结构化数据,采用并行双线程方法升级椭圆曲线加密算法(ECC)。对于结构化数据,增强了 Paillier 同态加密,以支持对密码文本的操作。对高达 4 G 的数据集进行的实验表明,与标准 ECC、AES 和 DES 的 100 - 160 秒相比,改进后的 ECC 方法将加密时间缩短到 60 - 80 秒。它还可以使用比 RSA 更短的密钥长度,但安全级别相当。增强型 Paillier 加密使用大质数来确保密文的有效性。通过将这些改进的加密技术与安全的 Hadoop 框架相结合,本研究展示了解决大数据存储漏洞的有效方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhanced Secure Storage of Big Data at Rest with Improved ECC and Paillier Homomorphic Encryption Algorithms
: With the rapid growth of Big Data, securing its storage has become crucial. This study proposes to enhance the secure storage of big data at rest in Hadoop by improving encryption algorithms. The Elliptic Curve Cryptography Algorithm (ECC) is upgraded by a parallel two-threaded approach for unstructured data. For structured data, enhance Paillier Homomorphic Encryption to support operations on ciphertexts. Experiments on datasets up to 4 G show that the modified ECC method reduces encryption time to 60 - 80 seconds, compared to 100 - 160 seconds for standard ECC, AES, and DES. It can also use shorter key lengths than RSA with comparable levels of security. Enhanced Paillier encryption uses large prime numbers to ensure the validity of the ciphertext. By combining these improved encryption techniques within a secure Hadoop framework, this research demonstrates an effective way to address vulnerabilities in Big Data storage.
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