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INSPIRE: in-storage private information retrieval via protocol and architecture co-design

Authors:

Ishtiyaque Ahmad,

Yuan XieAuthors Info & Claims

ISCA '22: Proceedings of the 49th Annual International Symposium on Computer Architecture

Pages 102 - 115

https://doi.org/10.1145/3470496.3527433

Published: 11 June 2022 Publication History

Abstract

Private Information Retrieval (PIR) plays a vital role in secure, database-centric applications. However, existing PIR protocols explore a massive working space containing hundreds of GiBs of query and database data. As a consequence, PIR performance is severely bounded by storage communication, making it far from practical for real-world deployment.

In this work, we describe INSPIRE, an accelerator for IN-Storage Private Information REtrieval. INSPIRE follows a protocol and architecture co-design approach. We first design the INSPIRE protocol with a multi-stage filtering mechanism, which achieves a constant PIR query size. For a 1-billion-entry database of size 288GiB, INSPIRE's protocol reduces the query size from 27GiB to 3.6MiB. Further, we propose the INSPIRE hardware, a heterogeneous in-storage architecture, which integrates our protocol across the SSD hierarchy. Together with the INSPIRE protocol, the INSPIRE hardware reduces the query time from 28.4min to 36s, relative to the the state-of-the-art FastPIR scheme.

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Cited By

Zhang JCheng XYang LHu JLiu XChen K(2024)SoK: Fully Homomorphic Encryption AcceleratorsACM Computing Surveys10.1145/367695556:12(1-32)Online publication date: 5-Jul-2024
https://dl.acm.org/doi/10.1145/3676955
Lam MJohnson JXiong WMaeng KGupta ULi YLai LLeontiadis IRhu MLee HReddi VWei GBrooks DSuh ETsafrir DMusuvathi MGupta RAbu-Ghazaleh N(2024)GPU-based Private Information Retrieval for On-Device Machine Learning InferenceProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 110.1145/3617232.3624855(197-214)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3617232.3624855
Pan XAn YLiang SMao BZhang MLi QJung MZhang J(2024)Flagger: Cooperative Acceleration for Large-Scale Cross-Silo Federated Learning Aggregation2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA)10.1109/ISCA59077.2024.00071(915-930)Online publication date: 29-Jun-2024
https://doi.org/10.1109/ISCA59077.2024.00071
Show More Cited By

Index Terms

INSPIRE: in-storage private information retrieval via protocol and architecture co-design
1. Hardware
  1. Emerging technologies
    1. Memory and dense storage
2. Security and privacy
  1. Database and storage security
    1. Data anonymization and sanitization

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cover image ACM Conferences

ISCA '22: Proceedings of the 49th Annual International Symposium on Computer Architecture

June 2022

1097 pages

ISBN:9781450386104

DOI:10.1145/3470496

General Chairs:
Valentina Salapura
Google
,
Mohamed Zahran
New York University
,
Program Chairs:
Fred Chong
The University of Chicago
,
Lingjia Tang
The University of Michigan

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGARCH: ACM Special Interest Group on Computer Architecture

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Association for Computing Machinery

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Published: 11 June 2022

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ISCA '22

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ISCA '22: The 49th Annual International Symposium on Computer Architecture

June 18 - 22, 2022

New York, New York

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ISCA '22 Paper Acceptance Rate 67 of 400 submissions, 17%;

Overall Acceptance Rate 543 of 3,203 submissions, 17%

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ISCA '25

Sponsor:
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The 52nd Annual International Symposium on Computer Architecture

June 21 - 25, 2025

Tokyo , Japan

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Cited By

Zhang JCheng XYang LHu JLiu XChen K(2024)SoK: Fully Homomorphic Encryption AcceleratorsACM Computing Surveys10.1145/367695556:12(1-32)Online publication date: 5-Jul-2024
https://dl.acm.org/doi/10.1145/3676955
Lam MJohnson JXiong WMaeng KGupta ULi YLai LLeontiadis IRhu MLee HReddi VWei GBrooks DSuh ETsafrir DMusuvathi MGupta RAbu-Ghazaleh N(2024)GPU-based Private Information Retrieval for On-Device Machine Learning InferenceProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 110.1145/3617232.3624855(197-214)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3617232.3624855
Pan XAn YLiang SMao BZhang MLi QJung MZhang J(2024)Flagger: Cooperative Acceleration for Large-Scale Cross-Silo Federated Learning Aggregation2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA)10.1109/ISCA59077.2024.00071(915-930)Online publication date: 29-Jun-2024
https://doi.org/10.1109/ISCA59077.2024.00071
Wang YLi SZheng QSong LLi ZChang ALi HChen Y(2024)NDSEARCH: Accelerating Graph-Traversal-Based Approximate Nearest Neighbor Search through Near Data Processing2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA)10.1109/ISCA59077.2024.00035(368-381)Online publication date: 29-Jun-2024
https://doi.org/10.1109/ISCA59077.2024.00035
Lee YKim HRhu M(2024)PreSto: An In-Storage Data Preprocessing System for Training Recommendation Models2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA)10.1109/ISCA59077.2024.00033(340-353)Online publication date: 29-Jun-2024
https://doi.org/10.1109/ISCA59077.2024.00033
Niu FYue JShen JLiao XJin H(2024)FlashGNN: An In-SSD Accelerator for GNN Training2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA57654.2024.00035(361-378)Online publication date: 2-Mar-2024
https://doi.org/10.1109/HPCA57654.2024.00035
Liang LLin JQu ZAhmad ITu FGupta TDing YXie Y(2023)SPG: Structure-Private Graph Database via SqueezePIRProceedings of the VLDB Endowment10.14778/3587136.358713816:7(1615-1628)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.14778/3587136.3587138
Wu LZhou MXu WVenkat ARosing TSkadron K(2023)Abakus: Accelerating k-mer Counting with Storage TechnologyACM Transactions on Architecture and Code Optimization10.1145/363295221:1(1-26)Online publication date: 21-Nov-2023
https://dl.acm.org/doi/10.1145/3632952
Suzuki TYamana H(2023)Designing In-Storage Computing for Low Latency and High Throughput Homomorphic Encrypted Execution2023 IEEE 8th International Conference on Big Data Analytics (ICBDA)10.1109/ICBDA57405.2023.10104970(127-136)Online publication date: 3-Mar-2023
https://doi.org/10.1109/ICBDA57405.2023.10104970

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