基于混沌算法的嵌入式数据库SQLite加密方法

Junlong Shi
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摘要

随着嵌入式系统的广泛应用,混沌是一种具有确定性和复杂性的非线性系统。它是当前信息安全领域的一个重要课题,是应用于嵌入式系统的有效途径。它在理论和实践上都有很大的实用价值。本研究主要针对SQLite嵌入式数据库的加密技术,提出了一种基于Logistic混沌映射的改进麻雀算法(Logistic Chaos sparrow Search algorithm, LCSSA)。这表明SQLite在web开发中的安全级别比传统的Access要高。采用logistic混沌映射方法对种群进行初始化,提高了初始解的质量,增加了种群的多样性,降低了算法过早成熟的风险。初始值y0决定了非线性函数的加密方式。以整型变量int为例,取值范围为-231~231。由此可见,该密钥空间足以抵御各种常规攻击。当密钥是错误的密钥时,解密将不会产生任何数据。可以发现,加密和解密对密钥非常敏感,这也是混沌加密系统对初始值的敏感性所决定的。基准函数将改进算法的性能与改进前的算法进行比较,并与SSA进行比较。LCSSA具有更好的收敛性能、更高的精度和更好的稳定性。
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SQLite Encryption Method for Embedded Databases Based on Chaos Algorithm
With the widespread use of embedded systems, chaos is a nonlinear system with certainty and complexity. It is an important topic in the field of information security at present, and it is an effective way to apply to embedded systems. It has great practical value in theory and in practice. This research mainly focuses on the encryption technology of SQLite embedded database and proposes an improved sparrow algorithm (Logistic Chaos Sparrow Search Algorithm, LCSSA) based on Logistic Chaos Map. It shows that the security level of SQLite in web development is higher than that of conventional Access. The population is initialized by the logistic chaotic mapping method, which improves the quality of the initial solution, increases the diversity of the population, and reduces the risk of premature maturity of the algorithm. The initial value y 0 determines the encryption method of the nonlinear function. Taking the integer variable (int) as an example, the value range is -231~231. It can be seen that the key space is sufficient to prevent various conventional attacks. When the key is the wrong key, decryption will not yield any data. It can be found that encryption and decryption are very sensitive to the key, which is also determined by the sensitivity of chaotic encryption system to the initial value. The benchmark function compares the performance of the improved algorithm with the algorithm before the improvement and compares it with the SSA. The LCSSA has better convergence performance, higher accuracy, and better stability.
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