Towards Fast and Scalable Private Inference

Jianqiao Mo, Karthik Garimella, Negar Neda, Austin Ebel, Brandon Reagen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Privacy and security have rapidly emerged as first order design constraints. Users now demand more protection over who can see their data (confidentiality) as well as how it is used (control). Here, existing cryptographic techniques for security fall short: they secure data when stored or communicated but must decrypt it for computation. Fortunately, a new paradigm of computing exists, which we refer to as privacy-preserving computation (PPC). Emerging PPC technologies can be leveraged for secure outsourced computation or to enable two parties to compute without revealing either users' secret data. Despite their phenomenal potential to revolutionize user protection in the digital age, the realization has been limited due to exorbitant computational, communication, and storage overheads. This paper reviews recent efforts on addressing various PPC overheads using private inference (PI) in neural network as a motivating application. First, the problem and various technologies, including homomorphic encryption (HE), secret sharing (SS), garbled circuits (GCs), and oblivious transfer (OT), are introduced. Next, a characterization of their overheads when used to implement PI is covered. The characterization motivates the need for both GCs and HE accelerators. Then two solutions are presented: HAAC for accelerating GCs and RPU for accelerating HE. To conclude, results and effects are shown with a discussion on what future work is needed to overcome the remaining overheads of PI.

Original languageEnglish (US)
Title of host publicationProceedings of the 20th ACM International Conference on Computing Frontiers 2023, CF 2023
PublisherAssociation for Computing Machinery, Inc
Number of pages7
ISBN (Electronic)9798400701405
StatePublished - May 9 2023
Event20th ACM International Conference on Computing Frontiers, CF 2023 - Bologna, Italy
Duration: May 9 2023May 11 2023

Publication series

NameProceedings of the 20th ACM International Conference on Computing Frontiers 2023, CF 2023


Conference20th ACM International Conference on Computing Frontiers, CF 2023


  • garbled circuits
  • homomorphic encryption
  • privacy preserving computation
  • private inference

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Software
  • Safety, Risk, Reliability and Quality


Dive into the research topics of 'Towards Fast and Scalable Private Inference'. Together they form a unique fingerprint.

Cite this