论文标题
关于同态学习的简短历史：以隐私为中心的机器学习方法
A brief history on Homomorphic learning: A privacy-focused approach to machine learning
论文作者
论文摘要
加密和数据科学研究随着互联网繁荣的指数增长。传统加密技术迫使用户在可用性，便利性和安全性之间进行权衡。加密使得有价值的数据无法访问，因为每次都需要解密才能执行任何操作。可以节省数十亿美元，数以百万计的人可以从不损害可用性，便利性和安全性之间的加密方法中受益。同态加密是一种范式，允许在加密数据上运行任意操作。它使我们能够运行任何复杂的机器学习算法，而无需访问基础原始数据。因此，同态学习提供了从由于各种政府和组织隐私规则而被忽略的敏感数据中获得见解的能力。 在本文中，我们追溯了罗纳德·L·里维斯特（Ronald L.在他们的1978年论文中。然后，我们逐渐遵循Shafi Goldwasser，Kristin Lauter，Dan Bonch，Tomas Sander，Donald Beaver和Craig Gentry的辉煌思想中发芽的想法。花了30多年的集体努力才最终找到了这个重要问题的答案“是”。
Cryptography and data science research grew exponential with the internet boom. Legacy encryption techniques force users to make a trade-off between usability, convenience, and security. Encryption makes valuable data inaccessible, as it needs to be decrypted each time to perform any operation. Billions of dollars could be saved, and millions of people could benefit from cryptography methods that don't compromise between usability, convenience, and security. Homomorphic encryption is one such paradigm that allows running arbitrary operations on encrypted data. It enables us to run any sophisticated machine learning algorithm without access to the underlying raw data. Thus, homomorphic learning provides the ability to gain insights from sensitive data that has been neglected due to various governmental and organization privacy rules. In this paper, we trace back the ideas of homomorphic learning formally posed by Ronald L. Rivest and Len Alderman as "Can we compute upon encrypted data?" in their 1978 paper. Then we gradually follow the ideas sprouting in the brilliant minds of Shafi Goldwasser, Kristin Lauter, Dan Bonch, Tomas Sander, Donald Beaver, and Craig Gentry to address that vital question. It took more than 30 years of collective effort to finally find the answer "yes" to that important question.
