Classical machine learning modeling demands considerable computing power for internal calculations and training with big data in a reasonable amount of time. In recent years, clouds provide services to facilitate this process, but it introduces new security threats of data breaches. Modern encryption techniques ensure security and are considered as the best option to protect stored data and data in transit from an unauthorized third-party. However, a decryption process is necessary when the data must be processed or analyzed, falling into the initial problem of data vulnerability. Fully Homomorphic Encryption (FHE) is considered the holy grail of cryptography. It allows a non-trustworthy third-party resource to process encrypted information without disclosing confidential data. In this paper, we analyze the fundamental concepts of FHE, practical implementations, state-of-the-art approaches, limitations, advantages, disadvantages, potential applications, and development tools focusing on neural networks. In recent years, FHE development demonstrates remarkable progress. However, current literature in the homomorphic neural networks is almost exclusively addressed by practitioners looking for suitable implementations. It still lacks comprehensive and more thorough reviews. We focus on the privacy-preserving homomorphic encryption cryptosystems targeted at neural networks identifying current solutions, open issues, challenges, opportunities, and potential research directions.

经典的机器学习建模需要相当大的计算能力，以便在合理的时间内进行大数据的内部计算和训练。近年来，云提供了促进该过程的服务，但它带来了数据泄露的新安全威胁。现代加密技术可确保安全性，被认为是保护存储的数据和未经授权的第三方传输的数据的最佳选择。但是，当必须处理或分析数据时，解密过程是必需的，这将成为数据漏洞的最初问题。完全同态加密（FHE）被认为是密码术的圣杯。它允许不可信的第三方资源处理加密的信息，而不会泄露机密数据。在本文中，我们分析了FHE的基本概念，实际的实现，最新的方法，局限性，优势，劣势，潜在的应用程序以及专注于神经网络的开发工具。近年来，FHE的发展显示出了惊人的进步。但是，同态神经网络中的当前文献几乎都是由寻求合适的实现的从业人员解决的。它仍然缺乏全面和更彻底的评论。我们专注于针对神经网络的保护隐私的同态加密密码系统，以确定当前的解决方案，未解决的问题，挑战，机遇和潜在的研究方向。FHE的发展表明了惊人的进步。但是，同态神经网络中的当前文献几乎都是由寻求合适的实现的从业人员解决的。它仍然缺乏全面和更彻底的评论。我们专注于针对神经网络的保护隐私的同态加密密码系统，以确定当前的解决方案，未解决的问题，挑战，机遇和潜在的研究方向。FHE的发展表明了惊人的进步。但是，同态神经网络中的当前文献几乎都是由寻求合适的实现的从业人员解决的。它仍然缺乏全面和更彻底的评论。我们专注于针对神经网络的保护隐私的同态加密密码系统，以确定当前的解决方案，未解决的问题，挑战，机遇和潜在的研究方向。