DOCSLIB.ORG

Combining Bayesian Deep Learning and Homomorphic

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Combining Bayesian Deep Learning and Homomorphic Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19) BAYHENN: Combining Bayesian Deep Learning and Homomorphic Encryption for Secure DNN Inference Peichen Xie1;2∗ , Bingzhe Wu1;3∗ and Guangyu Sun1;2 1Center for Energy-efficient Computing and Applications, Peking University 2Advanced Institute of Information Technology, Peking University 3Ant Financial Services Group fxpc, wubingzhe, [email protected] Abstract are not protected, using DLaaS will cause potential privacy issues. A curious server may collect sensitive information Recently, deep learning as a service (DLaaS) has contained in the private data (i.e. client’s privacy). emerged as a promising way to facilitate the em- To address this privacy issue, researchers have employed ployment of deep neural networks (DNNs) for var- the homomorphic encryption to perform various DNN oper- ious purposes. However, using DLaaS also causes ators on encrypted client data [Phong et al., 2018; Ma et al., potential privacy leakage from both clients and 2019]. As a result, the cloud server only serves as a compu- cloud servers. This privacy issue has fueled the re- tation platform but cannot access the raw data from clients. search interests on the privacy-preserving inference However, there exist two major obstacles in applying these of DNN models in the cloud service. In this paper, approaches. First, some common non-linear activation func- we present a practical solution named BAYHENN tions, such as ReLU and Sigmoid, are not cryptographically for secure DNN inference. It can protect both the computable. Second, the inference processing efficiency is client’s privacy and server’s privacy at the same seriously degraded by thousands of times. time. The key strategy of our solution is to combine To tackle these problems, a recent work [Zhang et al., homomorphic encryption and Bayesian neural net- 2018] proposes using an interactive paradigm. A DNN in- works. Specifically, we use homomorphic encryp- ference is partitioned into linear and non-linear computa- tion to protect a client’s raw data and use Bayesian tion parts. Then, only the linear computations are per- neural networks to protect the DNN weights in a formed on the cloud server with encrypted data. The non- cloud server. To verify the effectiveness of our so- linear computations are performed by the client with raw lution, we conduct experiments on MNIST and a data. However, in such an interactive paradigm, the inter- real-life clinical dataset. Our solution achieves con- mediate features extracted by the linear computations are di- sistent latency decreases on both tasks. In partic- rectly exposed (sent back) to the client. Thus, a curious ular, our method can outperform the best existing client can leverage these features to reconstruct the weights method (GAZELLE) by about 5×, in terms of end- of the DNN model held by the cloud [Tramer` et al., 2016; to-end latency. Zhang et al., 2018]. This issue is called the leakage of server’s privacy. 1 Introduction In fact, a practical solution for secure DNN inference should protect both client’s privacy and server’s privacy. In In the past years, deep neural networks (DNNs) have achieved addition, it should support DNNs with all types of non-linear remarkable progress in various fields, such as computer vi- activation functions. Unfortunately, there still lacks an effec- sion [He et al., 2016; Ren et al., 2017], natural language pro- tive approach in literature. The limitations of recent works cessing [Vaswani et al., 2017; Devlin et al., 2018], and med- are discussed in Section 2.3. Thus, we propose a new method ical image analysis [Litjens et al., 2017; Havaei et al., 2017]. for secure DNN inference, called BAYHENN. Our key strat- Recently, deep learning as a service (DLaaS) has emerged as egy is to combine Bayesian deep learning and homomorphic a promising to further enable the widespread use of DNNs encryption. To the best of our knowledge, it is the first ap- in industry/daily-life. Google1, Amazon2, and IBM [Bhat- proach that can protect both client’s privacy and server’s pri- tacharjee et al., 2017] have all launched DLaaS platforms in vacy and support all types of non-linear activation functions their cloud services. Using DLaaS, a client sends its private at the same time. data to the cloud server. Then, the server is responsible for performing the DNN inference and sends the prediction re- BAYHENN follows an interactive paradigm so that all sults back to the client. Obviously, if the private client data types of activation functions are supported. On the one hand, it uses the homomorphic encryption to provide protection for ∗Equal contribution. the client’s privacy. On the other hand, it adds proper ran- 1https://cloud.google.com/inference/ domness into DNN weights to prevent the leakage of the 2https://docs.aws.amazon.com/machine-learning/index.html server’s privacy. This idea is motivated by the learning with 4831 Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19) error (LWE) problem, in which solving the noisy linear equa- Client’s Server’s Support all tions system has been proven to be NP-hard [Regev, 2009]. Privacy Privacy activation func. However, “adding proper randomness” is a new challenge. Directly injecting well-designed Gaussian Noise into weights CryptoNets Yes Yes — will cause a drastic performance degradation [Chaudhuri and GELU-Net Yes — Yes Monteleoni, 2008; Zhang et al., 2017]. Therefore, we pro- GAZELLE Yes Yes — pose to use a more intrinsic method, the Bayesian neural net- Ours Yes Yes Yes work, to model the weight uncertainty of DNNs. Armed with the weight uncertainty, we can protect the weight information Table 1: Feature comparison among different solutions. by sending obscured features to the client. In summary, the contributions of our paper are as follows: where '(i) denotes the activation function (e.g. ReLU). • We provide a novel insight that using the Bayesian neu- In summary, the classification model M can be reformu- ral network can prevent leakage of a server’s privacy un- lated into: der an interactive paradigm. M : t = f(x; f(W(i); b(i))gn ) (3) • Based on this insight, we build a practical solution for i=1 secure DNN inference. Our solution is more (about 5×) 2.2 Privacy Leakage in DLaaS efficient than the best existing method, and is capable of supporting all types of activation functions. In a common setting, there are two parties involved in a pro- cess of DLaaS. Specifically, at the beginning, a client sends • We validate the proposed solution on a real-life clinical its private data x into a cloud server, which holds the DNN dataset, which is less explored in previous studies. model M for inference. Then, the server can perform the DNN inference and send the prediction results back to the 2 Preliminary client. Considering that both the client and the server are In this part, we first introduce some basic notations in DNN semi-honest, privacy leakages may occur on both sides: inference. Then, the potential privacy leakages in DLaaS and • Client’s Privacy Leakage: From the perspective of a the goal of this paper are presented. At last, we summarize client, its input data x is directly exposed to the cloud the limitations of previous works on secure DNN inference. server in a non-private setting. Thus, the server may col- lect the sensitive information contained in x. 2.1 DNN Inference • Server’s Privacy Leakage: A cloud server also holds We start from the process of a DNN inference. For simplicity, the private data, i.e. the weight parameters and the DNN the following discussion is based on a fully connected neural structure of M. A curious client may attempt to obtain network. It can be easily switched to the context of a convo- these valuable model information. lutional neural network, as shown in our experiments. We take the image classification as an example in this part. In this paper, we focus on a secure DNN inference. Our goal It can be extended to other types of tasks as well (e.g. re- is to prevent the privacy leakage of the input data (client-side) gression). The image classification aims to assign a label and the model parameters (server-side). The protection of a t to the input image x. To this end, a classification model model structure is out of the scope of this paper, yet part of M : t = f (x; θ) is trained on some pre-collected data. In a the DNN model structure (such as the filter and stride size context of deep learning, the f is a highly non-linear function, in the convolution layers and the type of each layer) is also which is formed by alternately stacking several linear (e.g. protected in our solution4. fully connected layer) and non-linear (e.g. activation func- tion) operations3. 2.3 Limitation of Previous Work Formally, for a neural network, we denote W(i) and b(i) In this subsection, we discuss the limitations of previous as weights and biases corresponding to the i-th linear layer. works, which are most related to ours (listed in Table 1). We To calculate outputs (i.e. activations) of the i-th non-linear do not consider [Bourse et al., 2018; Sanyal et al., 2018], layer (denoted as a(i)), a linear transformation is firstly per- since they only work on binary neural networks and do not formed on the inputs from previous layers following: outperform the state-of-the-art. CryptoNets [Gilad-Bachrach et al., 2016] is the first sys- (i) (i) (i−1) (i) z = W a + b (1) tem for homomorphic encryption based neural network in- ference.
Load more

Recommended publications
  • ARTIFICIAL INTELLIGENCE and ALGORITHMIC LIABILITY a Technology and Risk Engineering Perspective from Zurich Insurance Group and Microsoft Corp
    White Paper ARTIFICIAL INTELLIGENCE AND ALGORITHMIC LIABILITY A technology and risk engineering perspective from Zurich Insurance Group and Microsoft Corp. July 2021 TABLE OF CONTENTS 1. Executive summary 03 This paper introduces the growing notion of AI algorithmic 2. Introduction 05 risk, explores the drivers and A. What is algorithmic risk and why is it so complex? ‘Because the computer says so!’ 05 B. Microsoft and Zurich: Market-leading Cyber Security and Risk Expertise 06 implications of algorithmic liability, and provides practical 3. Algorithmic risk : Intended or not, AI can foster discrimination 07 guidance as to the successful A. Creating bias through intended negative externalities 07 B. Bias as a result of unintended negative externalities 07 mitigation of such risk to enable the ethical and responsible use of 4. Data and design flaws as key triggers of algorithmic liability 08 AI. A. Model input phase 08 B. Model design and development phase 09 C. Model operation and output phase 10 Authors: D. Potential complications can cloud liability, make remedies difficult 11 Zurich Insurance Group 5. How to determine algorithmic liability? 13 Elisabeth Bechtold A. General legal approaches: Caution in a fast-changing field 13 Rui Manuel Melo Da Silva Ferreira B. Challenges and limitations of existing legal approaches 14 C. AI-specific best practice standards and emerging regulation 15 D. New approaches to tackle algorithmic liability risk? 17 Microsoft Corp. Rachel Azafrani 6. Principles and tools to manage algorithmic liability risk 18 A. Tools and methodologies for responsible AI and data usage 18 Christian Bucher B. Governance and principles for responsible AI and data usage 20 Franziska-Juliette Klebôn C.
    [Show full text]
  • Practical Homomorphic Encryption and Cryptanalysis
    Practical Homomorphic Encryption and Cryptanalysis Dissertation zur Erlangung des Doktorgrades der Naturwissenschaften (Dr. rer. nat.) an der Fakult¨atf¨urMathematik der Ruhr-Universit¨atBochum vorgelegt von Dipl. Ing. Matthias Minihold unter der Betreuung von Prof. Dr. Alexander May Bochum April 2019 First reviewer: Prof. Dr. Alexander May Second reviewer: Prof. Dr. Gregor Leander Date of oral examination (Defense): 3rd May 2019 Author's declaration The work presented in this thesis is the result of original research carried out by the candidate, partly in collaboration with others, whilst enrolled in and carried out in accordance with the requirements of the Department of Mathematics at Ruhr-University Bochum as a candidate for the degree of doctor rerum naturalium (Dr. rer. nat.). Except where indicated by reference in the text, the work is the candidates own work and has not been submitted for any other degree or award in any other university or educational establishment. Views expressed in this dissertation are those of the author. Place, Date Signature Chapter 1 Abstract My thesis on Practical Homomorphic Encryption and Cryptanalysis, is dedicated to efficient homomor- phic constructions, underlying primitives, and their practical security vetted by cryptanalytic methods. The wide-spread RSA cryptosystem serves as an early (partially) homomorphic example of a public- key encryption scheme, whose security reduction leads to problems believed to be have lower solution- complexity on average than nowadays fully homomorphic encryption schemes are based on. The reader goes on a journey towards designing a practical fully homomorphic encryption scheme, and one exemplary application of growing importance: privacy-preserving use of machine learning.
    [Show full text]
  • Information Guide
    INFORMATION GUIDE 7 ALL-PRO 7 NFL MVP LAMAR JACKSON 2018 - 1ST ROUND (32ND PICK) RONNIE STANLEY 2016 - 1ST ROUND (6TH PICK) 2020 BALTIMORE DRAFT PICKS FIRST 28TH SECOND 55TH (VIA ATL.) SECOND 60TH THIRD 92ND THIRD 106TH (COMP) FOURTH 129TH (VIA NE) FOURTH 143RD (COMP) 7 ALL-PRO MARLON HUMPHREY FIFTH 170TH (VIA MIN.) SEVENTH 225TH (VIA NYJ) 2017 - 1ST ROUND (16TH PICK) 2020 RAVENS DRAFT GUIDE “[The Draft] is the lifeblood of this Ozzie Newsome organization, and we take it very Executive Vice President seriously. We try to make it a science, 25th Season w/ Ravens we really do. But in the end, it’s probably more of an art than a science. There’s a lot of nuance involved. It’s Joe Hortiz a big-picture thing. It’s a lot of bits and Director of Player Personnel pieces of information. It’s gut instinct. 23rd Season w/ Ravens It’s experience, which I think is really, really important.” Eric DeCosta George Kokinis Executive VP & General Manager Director of Player Personnel 25th Season w/ Ravens, 2nd as EVP/GM 24th Season w/ Ravens Pat Moriarty Brandon Berning Bobby Vega “Q” Attenoukon Sarah Mallepalle Sr. VP of Football Operations MW/SW Area Scout East Area Scout Player Personnel Assistant Player Personnel Analyst Vincent Newsome David Blackburn Kevin Weidl Patrick McDonough Derrick Yam Sr. Player Personnel Exec. West Area Scout SE/SW Area Scout Player Personnel Assistant Quantitative Analyst Nick Matteo Joey Cleary Corey Frazier Chas Stallard Director of Football Admin. Northeast Area Scout Pro Scout Player Personnel Assistant David McDonald Dwaune Jones Patrick Williams Jenn Werner Dir.
    [Show full text]
  • Homomorphic Encryption Library
    Ludwig-Maximilians-Universit¨at Munchen¨ Prof. Dr. D. Kranzlmuller,¨ Dr. N. gentschen Felde Data Science & Ethics { Microsoft SEAL { Exercise 1: Library for Matrix Operations Microsoft's Simple Encrypted Arithmetic Library (SEAL)1 is a publicly available homomorphic encryption library. It can be found and downloaded at http://sealcrypto.codeplex.com/. Implement a library 13b MS-SEAL.h supporting matrix operations using homomorphic encryption on basis of the MS SEAL. due date: 01.07.2018 (EOB) no. of students: 2 deliverables: 1. Implemenatation (including source code(s)) 2. Documentation (max. 10 pages) 3. Presentation (10 { max. 15 minutes) 13b MS-SEAL.h #i n c l u d e <f l o a t . h> #i n c l u d e <s t d b o o l . h> typedef struct f double ∗ e n t r i e s ; unsigned int width; unsigned int height; g matrix ; /∗ Initialize new matrix: − reserve memory only ∗/ matrix initMatrix(unsigned int width, unsigned int height); /∗ Initialize new matrix: − reserve memory − set any value to 0 ∗/ matrix initMatrixZero(unsigned int width, unsigned int height); /∗ Initialize new matrix: − reserve memory − set any value to random number ∗/ matrix initMatrixRand(unsigned int width, unsigned int height); 1https://www.microsoft.com/en-us/research/publication/simple-encrypted-arithmetic-library-seal-v2-0/# 1 /∗ copy a matrix and return its copy ∗/ matrix copyMatrix(matrix toCopy); /∗ destroy matrix − f r e e memory − set any remaining value to NULL ∗/ void freeMatrix(matrix toDestroy); /∗ return entry at position (xPos, yPos), DBL MAX in case of error
    [Show full text]
  • Arxiv:2102.00319V1 [Cs.CR] 30 Jan 2021
    Efficient CNN Building Blocks for Encrypted Data Nayna Jain1,4, Karthik Nandakumar2, Nalini Ratha3, Sharath Pankanti5, Uttam Kumar 1 1 Center for Data Sciences, International Institute of Information Technology, Bangalore 2 Mohamed Bin Zayed University of Artificial Intelligence 3 University at Buffalo, SUNY 4 IBM Systems 5 Microsoft [email protected], [email protected], [email protected]/[email protected], [email protected], [email protected] Abstract Model Owner Model Architecture 푴 Machine learning on encrypted data can address the concerns Homomorphically Encrypted Model Model Parameters 퐸(휽) related to privacy and legality of sharing sensitive data with Encryption untrustworthy service providers, while leveraging their re- Parameters 휽 sources to facilitate extraction of valuable insights from oth- End-User Public Key Homomorphically Encrypted Cloud erwise non-shareable data. Fully Homomorphic Encryption Test Data {퐸(퐱 )}푇 Test Data 푖 푖=1 FHE Computations Service 푇 Encryption (FHE) is a promising technique to enable machine learning {퐱푖}푖=1 퐸 y푖 = 푴(퐸 x푖 , 퐸(휽)) Provider and inferencing while providing strict guarantees against in- Inference 푇 Decryption formation leakage. Since deep convolutional neural networks {푦푖}푖=1 Homomorphically Encrypted Inference Results {퐸(y )}푇 (CNNs) have become the machine learning tool of choice Private Key 푖 푖=1 in several applications, several attempts have been made to harness CNNs to extract insights from encrypted data. How- ever, existing works focus only on ensuring data security Figure 1: In a conventional Machine Learning as a Ser- and ignore security of model parameters. They also report vice (MLaaS) scenario, both the data and model parameters high level implementations without providing rigorous anal- are unencrypted.
    [Show full text]
  • MP2ML: a Mixed-Protocol Machine Learning Framework for Private Inference∗ (Full Version)
    MP2ML: A Mixed-Protocol Machine Learning Framework for Private Inference∗ (Full Version) Fabian Boemer Rosario Cammarota Daniel Demmler [email protected] [email protected] [email protected] Intel AI Intel Labs hamburg.de San Diego, California, USA San Diego, California, USA University of Hamburg Hamburg, Germany Thomas Schneider Hossein Yalame [email protected] [email protected] darmstadt.de TU Darmstadt TU Darmstadt Darmstadt, Germany Darmstadt, Germany ABSTRACT 1 INTRODUCTION Privacy-preserving machine learning (PPML) has many applica- Several practical services have emerged that use machine learn- tions, from medical image evaluation and anomaly detection to ing (ML) algorithms to categorize and classify large amounts of financial analysis. nGraph-HE (Boemer et al., Computing Fron- sensitive data ranging from medical diagnosis to financial eval- tiers’19) enables data scientists to perform private inference of deep uation [14, 66]. However, to benefit from these services, current learning (DL) models trained using popular frameworks such as solutions require disclosing private data, such as biometric, financial TensorFlow. nGraph-HE computes linear layers using the CKKS ho- or location information. momorphic encryption (HE) scheme (Cheon et al., ASIACRYPT’17), As a result, there is an inherent contradiction between utility and relies on a client-aided model to compute non-polynomial acti- and privacy: ML requires data to operate, while privacy necessitates vation functions, such as MaxPool and ReLU, where intermediate keeping sensitive information private [70]. Therefore, one of the feature maps are sent to the data owner to compute activation func- most important challenges in using ML services is helping data tions in the clear.
    [Show full text]
  • Smithsonian Institution Fiscal Year 2021 Budget Justification to Congress
    Smithsonian Institution Fiscal Year 2021 Budget Justification to Congress February 2020 SMITHSONIAN INSTITUTION (SI) Fiscal Year 2021 Budget Request to Congress TABLE OF CONTENTS INTRODUCTION Overview .................................................................................................... 1 FY 2021 Budget Request Summary ........................................................... 5 SALARIES AND EXPENSES Summary of FY 2021 Changes and Unit Detail ........................................ 11 Fixed Costs Salary and Related Costs ................................................................... 14 Utilities, Rent, Communications, and Other ........................................ 16 Summary of Program Changes ................................................................ 19 No-Year Funding and Object-Class Breakout .......................................... 23 Federal Resource Summary by Performance/Program Category ............ 24 MUSEUMS AND RESEARCH CENTERS Enhanced Research Initiatives ........................................................... 26 National Air and Space Museum ........................................................ 28 Smithsonian Astrophysical Observatory ............................................ 36 Major Scientific Instrumentation .......................................................... 41 National Museum of Natural History ................................................... 47 National Zoological Park ..................................................................... 55 Smithsonian Environmental
    [Show full text]
  • DELPHI: a Cryptographic Inference Service for Neural Networks Pratyush Mishra Ryan Lehmkuhl Akshayaram Srinivasan Wenting Zheng Raluca Ada Popa
    DELPHI: A Cryptographic Inference Service for Neural Networks Pratyush Mishra Ryan Lehmkuhl Akshayaram Srinivasan Wenting Zheng Raluca Ada Popa UC Berkeley Abstract Many companies provide neural network prediction services Cloud to users for a wide range of applications. However, current Client Cryptographic Protocol prediction systems compromise one party’s privacy: either Data NN Model the user has to send sensitive inputs to the service provider for Prediction classification, or the service provider must store its proprietary neural networks on the user’s device. The former harms the personal privacy of the user, while the latter reveals the service Figure 1: Cryptographic neural network inference. The lock indi- cates data provided in encrypted form. provider’s proprietary model. We design, implement, and evaluate DELPHI, a secure pre- tion over (convolutional) neural networks [Gil+16; Moh+17; diction system that allows two parties to execute neural net- Liu+17a; Juv+18] by utilizing specialized secure multi-party work inference without revealing either party’s data. DELPHI computation (MPC) [Yao86; Gol+87]. At a high level, these approaches the problem by simultaneously co-designing cryp- protocols proceed by encrypting the user’s input and the ser- tography and machine learning. We first design a hybrid cryp- vice provider’s neural network, and then tailor techniques for tographic protocol that improves upon the communication computing over encrypted data (like homomorphic encryption and computation costs over prior work. Second, we develop a or secret sharing) to run inference over the user’s input. At the planner that automatically generates neural network architec- end of the protocol execution, the intended party(-ies) learn ture configurations that navigate the performance-accuracy the inference result; neither party learns anything else about trade-offs of our hybrid protocol.
    [Show full text]
  • Getting the Most from Your Schmidt Cassegrain Or Any Catadioptric Telescope
    Patrick Moore’s Practical Astronomy Series For other titles published in the series, go to www.springer.com/series/3192 Choosing and Using a New CAT Getting the Most from Your Schmidt Cassegrain or Any Catadioptric Telescope Rod Mollise Rod Mollise 1207 Selma Street Mobile AL 36604 USA ISBN 978-0-387-09771-8 e-ISBN 978-0-387-09772-5 DOI: 10.1007/978-0-387-09772-5 Library of Congress Control Number: 2008934774 © Springer Science+Business Media, LLC 2009 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by simi- lar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper springer.com About the Author Rod Mollise is an engineer by profession. He is also the author of numerous books and magazine articles on every aspect of amateur astronomy. Known to his fans as “Uncle” Rod Mollise, he is most well known for his books about catadioptric tel- escopes (CATs), which aim to help new CAT owners get past the inexperience and anxiety that often accompanies their entry into this wonderful hobby.
    [Show full text]
  • Advances and Open Problems in Federated Learning
    Advances and Open Problems in Federated Learning Peter Kairouz7* H. Brendan McMahan7∗ Brendan Avent21 Aurelien´ Bellet9 Mehdi Bennis19 Arjun Nitin Bhagoji13 Kallista Bonawitz7 Zachary Charles7 Graham Cormode23 Rachel Cummings6 Rafael G.L. D’Oliveira14 Hubert Eichner7 Salim El Rouayheb14 David Evans22 Josh Gardner24 Zachary Garrett7 Adria` Gascon´ 7 Badih Ghazi7 Phillip B. Gibbons2 Marco Gruteser7;14 Zaid Harchaoui24 Chaoyang He21 Lie He 4 Zhouyuan Huo 20 Ben Hutchinson7 Justin Hsu25 Martin Jaggi4 Tara Javidi17 Gauri Joshi2 Mikhail Khodak2 Jakub Konecnˇ y´7 Aleksandra Korolova21 Farinaz Koushanfar17 Sanmi Koyejo7;18 Tancrede` Lepoint7 Yang Liu12 Prateek Mittal13 Mehryar Mohri7 Richard Nock1 Ayfer Ozg¨ ur¨ 15 Rasmus Pagh7;10 Hang Qi7 Daniel Ramage7 Ramesh Raskar11 Mariana Raykova7 Dawn Song16 Weikang Song7 Sebastian U. Stich4 Ziteng Sun3 Ananda Theertha Suresh7 Florian Tramer` 15 Praneeth Vepakomma11 Jianyu Wang2 Li Xiong5 Zheng Xu7 Qiang Yang8 Felix X. Yu7 Han Yu12 Sen Zhao7 1Australian National University, 2Carnegie Mellon University, 3Cornell University, 4Ecole´ Polytechnique Fed´ erale´ de Lausanne, 5Emory University, 6Georgia Institute of Technology, 7Google Research, 8Hong Kong University of Science and Technology, 9INRIA, 10IT University of Copenhagen, 11Massachusetts Institute of Technology, 12Nanyang Technological University, 13Princeton University, 14Rutgers University, 15Stanford University, 16University of California Berkeley, 17 University of California San Diego, 18University of Illinois Urbana-Champaign, 19University of Oulu, 20University of Pittsburgh, 21University of Southern California, 22University of Virginia, 23University of Warwick, 24University of Washington, 25University of Wisconsin–Madison arXiv:1912.04977v3 [cs.LG] 9 Mar 2021 Abstract Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g.
    [Show full text]
  • Friday, November 6, 2020 Home-Delivered $1.90, Retail $2.20
    TE NUPEPA O TE TAIRAWHITI FRIDAY, NOVEMBER 6, 2020 HOME-DELIVERED $1.90, RETAIL $2.20 COSTLY APARTMENT REPAIRS PAGE 3 HISTORIAN TO HISTORIAN PAGE 5 PAGE 13 SUPPORT LOCAL MUSOS: Gisborne musicians Adrian Athy (left), Darryl Monteith, Pete Stewart, Joe Williams, Ali Sochacki JUDGES DISMISS (front), Lazy Fifty manager Kerry Taggart and Ryan Raggett are right behind an initiative to encourage local cafes, bars, eateries and retailers to play the Spotify playlist Gisborne Musicians to promote original music from this region. “The concept will introduce Tairawhiti musicians to a new audience,” said Taggart. Story on page 4 Picture by Rebecca Grunwell TRUMP CLAIMS FRAUDSTER JAILED Two years, one month after failure to repay $147,000 A WOMAN who stole more than willing to undertake community work as months) of a possible 25 percent for But as matters stood, the company was $147,000 from her employer First Fresh well, Mr Clarke said. guilty pleas, which were belated due to still out of pocket, its loss not mitigated. NZ Limited has been jailed for 25 months He submitted a sentence starting point Brown’s initial response to the charges He referred to the company’s and ordered to pay the company $7000 of 36 months imprisonment. being to plead not guilty. victim impact statement written by for emotional harm. Police cited other tariff cases, including There was discount of one month for its managing director, who was in Katrina Louise Brown, 49, previously one with almost identical but slightly remorse and participation in restorative court regularly for Brown’s previous pleaded guilty to 14 charges of accessing more serious justice.
    [Show full text]
  • Partially Encrypted Machine Learning Using Functional Encryption
    Partially Encrypted Machine Learning using Functional Encryption Théo Ryffel1, 2, Edouard Dufour-Sans1, Romain Gay1,3, Francis Bach2, 1 and David Pointcheval1, 2 1Département d’informatique de l’ENS, ENS, CNRS, PSL University, Paris, France 2INRIA, Paris, France 3University of California, Berkeley {theo.ryffel,edufoursans,romain.gay,francis.bach,david.pointcheval}@ens.fr Abstract Machine learning on encrypted data has received a lot of attention thanks to recent breakthroughs in homomorphic encryption and secure multi-party computation. It allows outsourcing computation to untrusted servers without sacrificing privacy of sensitive data. We propose a practical framework to perform partially encrypted and privacy-preserving predictions which combines adversarial training and functional encryption. We first present a new functional encryption scheme to efficiently compute quadratic functions so that the data owner controls what can be computed but is not involved in the calculation: it provides a decryption key which allows one to learn a specific function evaluation of some encrypted data. We then show how to use it in machine learning to partially encrypt neural networks with quadratic activation functions at evaluation time, and we provide a thorough analysis of the information leaks based on indistinguishability of data items of the same label. Last, since most encryption schemes cannot deal with the last thresholding operation used for classification, we propose a training method to prevent selected sensitive features from leaking, which adversarially optimizes the network against an adversary trying to identify these features. This is interesting for several existing works using partially encrypted machine learning as it comes with little reduction on the model’s accuracy and significantly improves data privacy.
    [Show full text]