homomorphic

简明释义

英[ˌhəʊməʊˈmɔːfɪk]美[ˌhoməˈmɔrfɪk]

adj. 同态的；同形的

英英释义

单词用法

同义词

反义词

例句

1.Then, with congruence of the semialgebra, we proved the homomorphic theorem of semialgebra and its first and second isomorphic theorems.

利用半代数的同余来讨论半代数的同态定理及其第一，第二同构定理。

2.The method is better than traditional homomorphic filtering and histogram equalization.

该方法明显优于经典的同态增晰法和直方图均衡化。

3.Because the algorithm of cloud threshold often generates boundary effect, this paper proposed an improved algorithm based on wavelet transform and homomorphic filter.

针对基于小波阈值理论提出的云区阈值法存在的容易产生边界效应的问题，提出了一种改进的单幅图像去除薄云的新算法。

4.Regular order homomorphic mapping and regular continuity concepts are introduced by concepts of regular closed sets in LF topological spaces.

利用正则开（闭）集引入LF拓扑空间之间的几种正则序同态和几种正则连续性，并讨论了它们的性质及其相互关系。

5.The method of the fusion with the original image is presented on the basis of homomorphic filter, and realized it.

在同态滤波的基础上提出并完成了融合的去云的方法，并完成了方法实现。

6.A ring Re, (M) iff each nonzero homomorphic image of R either contains a nonzero M-ideal or has an essential ideal.

一个环R∈（M）当且仅当R的每一个非零同态象或者包含一个非零M理想或者有本质理想。

7.While homomorphic processing can also be used to calculate attenuation, amplitude and phase of one mode simultaneously.

同态处理方法还可以同时估计单一波模式的衰减、幅度和初相位；

8.Researchers are exploring homomorphic 同态的 encryption for secure cloud computing.

研究人员正在探索用于安全云计算的同态的加密。

9.The use of homomorphic 同态的 encryption is crucial in maintaining privacy in machine learning algorithms.

在机器学习算法中，使用同态的加密对于维护隐私至关重要。

10.A homomorphic 同态的 encryption system can perform operations like addition and multiplication directly on encrypted data.

一个同态的加密系统可以直接对加密数据执行加法和乘法等操作。

11.The encryption scheme is designed to be homomorphic 同态的, allowing computations on ciphertexts without decrypting them.

这个加密方案被设计为同态的，允许在密文上进行计算而无需解密。

12.With homomorphic 同态的 encryption, sensitive data can remain encrypted while still being processed.

通过同态的加密，敏感数据可以保持加密状态，同时仍然可以进行处理。

作文

In the field of computer science and cryptography, the term homomorphic refers to a specific type of encryption that allows computations to be performed on ciphertexts, generating an encrypted result that, when decrypted, matches the result of operations performed on the plaintext. This remarkable property makes homomorphic encryption highly valuable for privacy-preserving data processing. Imagine a scenario where sensitive information, such as medical records or financial data, needs to be processed without exposing the underlying data itself. With homomorphic encryption, this is not only possible but also efficient. The concept of homomorphic encryption was first introduced in the context of fully homomorphic encryption (FHE), which allows any arbitrary computation to be carried out on encrypted data. This means that users can perform complex operations without ever needing to decrypt the data, thus maintaining confidentiality. For instance, a cloud service provider could run algorithms on encrypted data uploaded by clients, ensuring that even the service provider does not have access to the raw data. This is particularly crucial in sectors like healthcare, where patient privacy is paramount.There are two main types of homomorphic encryption: partially homomorphic encryption (PHE) and fully homomorphic encryption (FHE). PHE allows certain types of operations to be performed on encrypted data, such as addition or multiplication, but not both. On the other hand, FHE supports all types of operations, making it much more versatile but also significantly more complex and computationally intensive. Researchers are actively exploring ways to improve the efficiency of FHE schemes, as they currently face challenges related to speed and resource consumption.One of the most notable applications of homomorphic encryption is in the realm of secure voting systems. By encrypting votes, the system ensures that individual choices remain confidential while still allowing for the tallying of results. This approach not only protects voter privacy but also enhances the integrity of the electoral process, as the votes cannot be tampered with once encrypted.Moreover, homomorphic encryption can play a critical role in machine learning. In scenarios where sensitive data is involved, such as training models on personal data, homomorphic encryption enables researchers to build and train models without accessing the actual data. This opens up new possibilities for collaboration across organizations while respecting privacy regulations like GDPR.Despite its potential, the adoption of homomorphic encryption is still in its infancy. The complexity of implementing homomorphic schemes often deters organizations from adopting this technology. However, as awareness grows and solutions become more accessible, we can expect to see an increase in the practical use of homomorphic encryption in various industries.In conclusion, the concept of homomorphic encryption represents a significant advancement in the field of data security. It empowers users to perform calculations on encrypted data without compromising privacy, making it a game-changer for industries that handle sensitive information. As research continues to evolve, the future of homomorphic encryption looks promising, paving the way for a more secure digital landscape.

在计算机科学和密码学领域，术语同态指的是一种特定类型的加密，它允许对密文进行计算，从而生成一个加密结果，该结果在解密后与在明文上执行的操作的结果相匹配。这一显著特性使得同态加密在隐私保护数据处理中极具价值。想象一下，在处理敏感信息（如医疗记录或财务数据）时，不需要暴露底层数据的场景。在这种情况下，使用同态加密不仅是可能的，而且也是高效的。同态加密的概念最初是在完全同态加密（FHE）的背景下提出的，它允许对加密数据进行任何任意计算。这意味着用户可以在不解密数据的情况下执行复杂的操作，从而保持机密性。例如，云服务提供商可以对客户上传的加密数据运行算法，确保即使服务提供商也无法访问原始数据。这在医疗等行业中尤其重要，因为患者隐私至关重要。同态加密主要有两种类型：部分同态加密（PHE）和完全同态加密（FHE）。PHE允许对加密数据执行某些类型的操作，例如加法或乘法，但不能同时执行两者。另一方面，FHE支持所有类型的操作，使其更具通用性，但同时也显著复杂且计算密集。研究人员正在积极探索提高FHE方案效率的方法，因为它们目前面临速度和资源消耗方面的挑战。同态加密最引人注目的应用之一是在安全投票系统中。通过对投票进行加密，系统确保个体选择保持机密，同时仍然允许对结果进行统计。这种方法不仅保护了选民的隐私，还增强了选举过程的完整性，因为一旦加密，投票就无法被篡改。此外，同态加密在机器学习中也能发挥关键作用。在涉及敏感数据的场景中，例如在个人数据上训练模型，同态加密使研究人员能够在不访问实际数据的情况下构建和训练模型。这为跨组织合作开辟了新的可能性，同时尊重GDPR等隐私法规。尽管具有潜力，但同态加密的采用仍处于起步阶段。实施同态方案的复杂性往往使组织不愿采用这项技术。然而，随着意识的提高和解决方案变得更加可及，我们可以期待在各个行业中看到同态加密的实际应用增加。总之，同态加密的概念代表了数据安全领域的一项重大进展。它使用户能够在不妨碍隐私的情况下对加密数据进行计算，为处理敏感信息的行业带来了变革。随着研究的不断发展，同态加密的未来看起来充满希望，为更安全的数字环境铺平了道路。