Homomorphically securing ai at the edge

Abstract

Edge devices are becoming increasingly pervasive in everyday life. These devices have become more computationally capable allowing for more complex AI reasoning at the edge. Subsequently, there is a need for protecting data to comply with privacy laws and confidentiality regulations. In this paper, we demonstrate the applicability of homomorphic encryption for protecting the data of AI-enabled cameras at the edge by implementing our solution on a commercial edge device. Our solution comprises a local homomorphic key-value database on an edge device populated by an AI camera; permitting the service of homomorphic search to be performed directly on the edge device. We characterize our implementation demonstrating linear behavior with respect to the database size that the edge device can support. Good enough performance is known to be difficult to achieve when employing homomorphic encryption. Our results are encouraging as we achieved solutions considered to be homomorphically fast, for example, linear performance of 1.28 seconds per database entry at over 256 bits of security. This amounts to a query being processed on a database of 200 entries in ∼ 5 minutes.