research-article

CryptoSPN: Expanding PPML beyond Neural Networks

Authors:

Alejandro Molina,

Christian Weinert,

Thomas Schneider,

Kristian KerstingAuthors Info & Claims

PPMLP'20: Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice

Pages 9 - 14

https://doi.org/10.1145/3411501.3419417

Published: 09 November 2020 Publication History

Abstract

The ubiquitous deployment of machine learning (ML) technologies has certainly improved many applications but also raised challenging privacy concerns, as sensitive client data is usually processed remotely at the discretion of a service provider. Therefore, privacy-preserving machine learning (PPML) aims at providing privacy using techniques such as secure multi-party computation (SMPC).

Recent years have seen a rapid influx of cryptographic frameworks that steadily improve performance as well as usability, pushing PPML towards practice. However, as it is mainly driven by the crypto community, the PPML toolkit so far is mostly restricted to well-known neural networks (NNs). Unfortunately, deep probabilistic models rising in the ML community that can deal with a wide range of probabilistic queries and offer tractability guarantees are severely underrepresented. Due to a lack of interdisciplinary collaboration, PPML is missing such important trends, ultimately hindering the adoption of privacy technology.

In this work, we introduce CryptoSPN, a framework for privacy-preserving inference of sum-product networks (SPNs) to significantly expand the PPML toolkit beyond NNs. SPNs are deep probabilistic models at the sweet-spot between expressivity and tractability, allowing for a range of exact queries in linear time. In an interdisciplinary effort, we combine techniques from both ML and crypto to allow for efficient, privacy-preserving SPN inference via SMPC.

We provide CryptoSPN as open source and seamlessly integrate it into the SPFlow library (Molina et al., arXiv 2019) for practical use by ML experts. Our evaluation on a broad range of SPNs demonstrates that CryptoSPN achieves highly efficient and accurate inference within seconds for medium-sized SPNs.

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Cited By

Gehrke MLiebenow JMohammadi EBraun T(2024)Lifting in Support of Privacy-Preserving Probabilistic InferenceKI - Künstliche Intelligenz10.1007/s13218-024-00851-yOnline publication date: 13-Jun-2024
https://doi.org/10.1007/s13218-024-00851-y
Li BQi PLiu BDi SLiu JPei JYi JZhou B(2023)Trustworthy AI: From Principles to PracticesACM Computing Surveys10.1145/355580355:9(1-46)Online publication date: 16-Jan-2023
https://doi.org/10.1145/3555803
Belle V(2023)Knowledge representation and acquisition for ethical AI: challenges and opportunitiesEthics and Information Technology10.1007/s10676-023-09692-z25:1Online publication date: 11-Mar-2023
https://doi.org/10.1007/s10676-023-09692-z
Show More Cited By

Index Terms

CryptoSPN: Expanding PPML beyond Neural Networks
1. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Privacy protections
    2. Usability in security and privacy
  2. Security services
    1. Privacy-preserving protocols

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cover image ACM Conferences

PPMLP'20: Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice

November 2020

75 pages

ISBN:9781450380881

DOI:10.1145/3411501

Program Chairs:
Benyu Zhang
Ant Group, USA
,
Raluca Ada Popa
UC Berkeley, USA
,
Matei Zaharia
Stanford University, USA
,
Guofei Gu
Texas A&M University, USA
,
Shouling Ji
Zhejiang University, China

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 09 November 2020

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European Research Council
Hessisches Ministerium für Bildung und Kunst
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CCS '20

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CCS '20: 2020 ACM SIGSAC Conference on Computer and Communications Security

November 9, 2020

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Cited By

Gehrke MLiebenow JMohammadi EBraun T(2024)Lifting in Support of Privacy-Preserving Probabilistic InferenceKI - Künstliche Intelligenz10.1007/s13218-024-00851-yOnline publication date: 13-Jun-2024
https://doi.org/10.1007/s13218-024-00851-y
Li BQi PLiu BDi SLiu JPei JYi JZhou B(2023)Trustworthy AI: From Principles to PracticesACM Computing Surveys10.1145/355580355:9(1-46)Online publication date: 16-Jan-2023
https://doi.org/10.1145/3555803
Belle V(2023)Knowledge representation and acquisition for ethical AI: challenges and opportunitiesEthics and Information Technology10.1007/s10676-023-09692-z25:1Online publication date: 11-Mar-2023
https://doi.org/10.1007/s10676-023-09692-z
Tiwari KShukla SGeorge J(2021)A Systematic Review of Challenges and Techniques of Privacy-Preserving Machine LearningData Science and Security10.1007/978-981-16-4486-3_3(19-41)Online publication date: 27-Aug-2021
https://doi.org/10.1007/978-981-16-4486-3_3
Treiber AMolina AWeinert CSchneider TKersting KZhang BAda Popa RZaharia MGu GJi S(2020)CryptoSPNProceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice10.1145/3411501.3419417(9-14)Online publication date: 9-Nov-2020
https://dl.acm.org/doi/10.1145/3411501.3419417
Boemer FCammarota RDemmler DSchneider TYalame HVolkamer MWressnegger C(2020)MP2MLProceedings of the 15th International Conference on Availability, Reliability and Security10.1145/3407023.3407045(1-10)Online publication date: 25-Aug-2020
https://dl.acm.org/doi/10.1145/3407023.3407045

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