PAIGE: Towards a hybrid-edge design for privacy-preserving intelligent personal assistants

Yilei Liang, Dan O'Keeffe, Nishanth Sastry

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

6 Citations (Scopus)

Abstract

Intelligent Personal Assistants (IPAs) such as Apple's Siri, Google Now, and Amazon Alexa are becoming an increasingly important class of web application. In contrast to previous keyword-oriented search applications, IPAs support a rich query interface that allows user interaction through images, audio, and natural language queries. However, modern IPAs rely heavily on compute-intensive machine-learning inference. To achieve acceptable performance, ML-driven IPAs increasingly depend on specialized hardware accelerators (e.g. GPUs, FPGAs or TPUs), increasing costs for IPA service providers. For end-users, IPAs also present considerable privacy risks given the sensitive nature of the data they capture. We present PAIGE, a hybrid edge-cloud architecture for privacy-preserving Intelligent Personal Assistants. PAIGE's design is founded on the assumption that recent advances in low-cost hardware for machine-learning inference offer an opportunity to offload compute-intensive IPA ML tasks to the network edge. To allow privacy-preserving access to large IPA databases for less compute-intensive pre-processed queries, PAIGE leverages trusted execution environments at the server side. PAIGE's hybrid design allows privacy-preserving hardware acceleration of compute-intensive tasks, while avoiding the need to move potentially large IPA question-answering databases to the edge. As a step towards realising PAIGE, we present a first systematic performance evaluation of existing edge accelerator hardware platforms for a subset of IPA workloads, and show they offer a competitive alternative to existing data-center alternatives.

Original languageEnglish
Title of host publicationEdgeSys 2020 - Proceedings of the 3rd ACM International Workshop on Edge Systems, Analytics and Networking, Part of EuroSys 2020
PublisherAssociation for Computing Machinery, Inc
Pages55-60
Number of pages6
ISBN (Electronic)9781450371322
DOIs
Publication statusPublished - 27 Apr 2020
Event3rd ACM International Workshop on Edge Systems, Analytics and Networking, in conjunction with ACM EuroSys 2020 - Heraklion, Greece
Duration: 27 Apr 2020 → …

Publication series

NameEdgeSys 2020 - Proceedings of the 3rd ACM International Workshop on Edge Systems, Analytics and Networking, Part of EuroSys 2020

Conference

Conference3rd ACM International Workshop on Edge Systems, Analytics and Networking, in conjunction with ACM EuroSys 2020
Country/TerritoryGreece
CityHeraklion
Period27/04/2020 → …

Keywords

  • Edge computing
  • Intelligent personal assistants
  • Trusted execution environments

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