Privacy-Preserving Cloud Computing using Homomorphic Encryption
In today’s data-driven world, a large amount of data is collected by billions of devices (cell phones, autonomous cars, handheld game consoles, etc.), and this data is then processed in the cloud. A common approach to maintain data privacy in the cloud is to keep the data in encrypted form, and we decrypt the data only when we need to process it. However, this approach requires efficient key management techniques, which are susceptible to attacks. There exists a ground-breaking technology called homomorphic encryption (HE), which allows us to operate on encrypted data and in turn maintain data privacy without the need to store and protect the secret keys. However, HE-based computing is multiple orders of magnitude slower than operating on unencrypted data. To make HE-based computing viable and practical, we need custom hardware designs and support for floating point numbers. In this project, we propose to design and prototype (using FPGAs in the Open Cloud Testbed) an efficient hardware solution for implementing the Cheon-Kim-Kim-Song (CKKS) HE scheme. Our design will be parametrized to support different polynomial lengths and coefficient bit widths, and will be optimized to minimize the time for HE-based privacy-preserving computing. We will perform an end-to-end evaluation of our hardware solution for image classification-based healthcare application.
Principal Investigator: Ajay Joshi