Survey Paper: Trustworthy AI Inference Systems: An Industry Research View

As one of the co-authors, I contributed to a survey paper (coordinated and edited by Rosario Cammarota) that was recently published on ArXiV:

Rosario CammarotaMatthias SchunterAnand RajanFabian BoemerÁgnes KissAmos TreiberChristian WeinertThomas SchneiderEmmanuel StapfAhmad-Reza SadeghiDaniel DemmlerHuili ChenSiam Umar HussainSadegh RiaziFarinaz KoushanfarSaransh GuptaTajan Simunic RosingKamalika ChaudhuriHamid NejatollahiNikil DuttMohsen ImaniKim LaineAnuj DubeyAydin AysuFateme Sadat HosseiniChengmo YangEric WallacePamela Norton

Abstract: n this work, we provide an industry research view for approaching the design, deployment, and operation of trustworthy Artificial Intelligence (AI) inference systems. Such systems provide customers with timely, informed, and customized inferences to aid their decision, while at the same time utilizing appropriate security protection mechanisms for AI models. Additionally, such systems should also use Privacy-Enhancing Technologies (PETs) to protect customers’ data at any time.
To approach the subject, we start by introducing trends in AI inference systems. We continue by elaborating on the relationship between Intellectual Property (IP) and private data protection in such systems. Regarding the protection mechanisms, we survey the security and privacy building blocks instrumental in designing, building, deploying, and operating private AI inference systems. For example, we highlight opportunities and challenges in AI systems using trusted execution environments combined with more recent advances in cryptographic techniques to protect data in use. Finally, we outline areas of further development that require the global collective attention of industry, academia, and government researchers to sustain the operation of trustworthy AI inference systems.