research-article

Efficient Key-Rotatable and Security-Updatable Homomorphic Encryption

Authors:

Yoshinori Aono,

Takuya Hayashi,

Le Trieu Phong,

Lihua WangAuthors Info & Claims

SCC '17: Proceedings of the Fifth ACM International Workshop on Security in Cloud Computing

Pages 35 - 42

https://doi.org/10.1145/3055259.3055260

Published: 02 April 2017 Publication History

Abstract

In this paper we presents the notion of key-rotatable and security-updatable homomorphic encryption (KR-SU-HE) scheme, which is a class of public-key homomorphic encryption in which the keys and the security of any ciphertext can be rotated and updated while still keeping the underlying plaintext intact and unrevealed. We formalise syntax and security notions for KR-SU-HE schemes and then build a concrete scheme based on the Learning With Errors assumption. We then perform testing implementation to show that our proposed scheme is efficiently practical.

References

[1]

Y. Aono, X. Boyen, L. T. Phong, and L. Wang. Key-private proxy re-encryption under LWE. In G. Paul and S. Vaudenay, editors, INDOCRYPT, volume 8250 of Lecture Notes in Computer Science, pages 1--18. Springer, 2013.

Digital Library

[2]

Y. Aono, T. Hayashi, L. T. Phong, and L. Wang. Fast and secure linear regression and biometric authentication with security update. IACR Cryptology ePrint Archive, 2015:692, 2015.

[3]

Y. Aono, T. Hayashi, L. T. Phong, and L. Wang. Privacy-preserving logistic regression with distributed data sources via homomorphic encryption. IEICE Transactions, 99-D(8):2079--2089, 2016.

[4]

B. Applebaum, D. Cash, C. Peikert, and A. Sahai. Fast cryptographic primitives and circular-secure encryption based on hard learning problems. In S. Halevi, editor, CRYPTO, volume 5677 of Lecture Notes in Computer Science, pages 595--618. Springer, 2009.

Digital Library

[5]

W. Banaszczyk. New bounds in some transference theorems in the geometry of numbers. Mathematische Annalen, 296(1):625--635, 1993.

[6]

W. Banaszczyk. Inequalities for convex bodies and polar reciprocal lattices in $\bbR^n$. Discrete & Computational Geometry, 13(1):217--231, 1995.

Digital Library

[7]

M. Blaze, G. Bleumer, and M. Strauss. Divertible protocols and atomic proxy cryptography. In K. Nyberg, editor, EUROCRYPT, volume 1403 of Lecture Notes in Computer Science, pages 127--144. Springer, 1998.

[8]

Z. Brakerski. Fully homomorphic encryption without modulus switching from classical gapsvp. In R. Safavi-Naini and R. Canetti, editors, CRYPTO, volume 7417 of Lecture Notes in Computer Science, pages 868--886. Springer, 2012.

Digital Library

[9]

Z. Brakerski, C. Gentry, and V. Vaikuntanathan. (Leveled) fully homomorphic encryption without bootstrapping. In S. Goldwasser, editor, ITCS, pages 309--325. ACM, 2012. Available at https://eprint.iacr.org/2011/277.pdf.

Digital Library

[10]

Z. Brakerski, A. Langlois, C. Peikert, O. Regev, and D. Stehlé. Classical hardness of learning with errors. In D. Boneh, T. Roughgarden, and J. Feigenbaum, editors, STOC, pages 575--584. ACM, 2013.

Digital Library

[11]

Z. Brakerski and V. Vaikuntanathan. Efficient fully homomorphic encryption from (standard) LWE. In R. Ostrovsky, editor, FOCS, pages 97--106. IEEE, 2011.

Digital Library

[12]

N. Chandran, M. Chase, F. Liu, R. Nishimaki, and K. Xagawa. Re-encryption, functional re-encryption, and multi-hop re-encryption: A framework for achieving obfuscation-based security and instantiations from lattices. In H. Krawczyk, editor, Public-Key Cryptography - PKC 2014, volume 8383 of Lecture Notes in Computer Science, pages 95--112. Springer, 2014.

Digital Library

[13]

I. Chillotti, N. Gama, M. Georgieva, and M. Izabachène. Faster fully homomorphic encryption: Bootstrapping in less than 0.1 seconds. In Advances in Cryptology - ASIACRYPT 2016 - 22nd International Conference on the Theory and Application of Cryptology and Information Security, Proceedings, Part I, pages 3--33, 2016.

[14]

C. Gentry. A fully homomorphic encryption scheme. PhD thesis, Stanford University, 2009. crypto.stanford.edu/craig.

Digital Library

[15]

D. E. Knuth and A. C. Yao. The complexity of non-uniform random number generation. Algorithms and Complexity, Academic Press, New York, pages 357--428, 1976.

[16]

R. Lindner and C. Peikert. Better key sizes (and attacks) for LWE-based encryption. In A. Kiayias, editor, CT-RSA, volume 6558 of Lecture Notes in Computer Science, pages 319--339. Springer, 2011.

[17]

M. Liu and P. Q. Nguyen. Solving BDD by enumeration: An update. In E. Dawson, editor, CT-RSA, volume 7779 of Lecture Notes in Computer Science, pages 293--309. Springer, 2013.

[18]

D. Micciancio and O. Regev. Lattice-based cryptography. In Post-Quantum Cryptography, pages 147--191. Springer, 2009.

[19]

National Institute of Standards and Technology (NIST). Recommendation for Key Management: Part 1: General (Revision 3). http://csrc.nist.gov/publications/nistpubs/800--57/sp800--57_part1_rev3_general.pdf.

[20]

R. Nishimaki and K. Xagawa. Key-private proxy re-encryption from lattices, revisited. IEICE Transactions, 98-A(1):100--116, 2015.

[21]

Open Web Application Security Project. https://www.owasp.org/index.php/Cryptographic_Storage_Cheat_Sheet.

[22]

Payment Card Industry Data Security Standard. https://www.pcisecuritystandards.org/documents/Prioritized_Approach_V2.0.pdf.

[23]

O. Regev. On lattices, learning with errors, random linear codes, and cryptography. In H. N. Gabow and R. Fagin, editors, STOC, pages 84--93. ACM, 2005.

Digital Library

Cited By

Deng THuang YHan GShi ZLin JDou QLiu ZGuo XPhilip Chen CHan C(2024)FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue ClassificationIEEE Transactions on Cybernetics10.1109/TCYB.2024.340392754:12(7851-7864)Online publication date: Dec-2024
https://doi.org/10.1109/TCYB.2024.3403927
Antwi-Boasiako EZhou SLiao YKuada EDanso E(2024)Enhanced privacy-preserving distributed deep learning with application to fog-based IoTInternet of Things10.1016/j.iot.2024.10118326(101183)Online publication date: Jul-2024
https://doi.org/10.1016/j.iot.2024.101183
Ganesh SDheeraj CPadmavathy R(2021)A Deep Learning Framework to Preserve Privacy in Federated (Collaborative) LearningArtificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities10.1007/978-3-030-72236-4_1(1-28)Online publication date: 1-Jun-2021
https://doi.org/10.1007/978-3-030-72236-4_1
Show More Cited By

Index Terms

Efficient Key-Rotatable and Security-Updatable Homomorphic Encryption
1. Security and privacy
  1. Cryptography
    1. Public key (asymmetric) techniques
      1. Public key encryption

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cover image ACM Conferences

SCC '17: Proceedings of the Fifth ACM International Workshop on Security in Cloud Computing

April 2017

100 pages

ISBN:9781450349703

DOI:10.1145/3055259

Program Chairs:
Cong Wang
City University of Hong Kong, China
,
Murat Kantarcioglu
University of Texas at Dallas, USA

Copyright © 2017 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 ACM 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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SIGSAC: ACM Special Interest Group on Security, Audit, and Control

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Association for Computing Machinery

New York, NY, United States

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Published: 02 April 2017

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ASIA CCS '17

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SIGSAC

ASIA CCS '17: ACM Asia Conference on Computer and Communications Security

April 2, 2017

Abu Dhabi, United Arab Emirates

Acceptance Rates

SCC '17 Paper Acceptance Rate 11 of 27 submissions, 41%;

Overall Acceptance Rate 64 of 159 submissions, 40%

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Cited By

Deng THuang YHan GShi ZLin JDou QLiu ZGuo XPhilip Chen CHan C(2024)FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue ClassificationIEEE Transactions on Cybernetics10.1109/TCYB.2024.340392754:12(7851-7864)Online publication date: Dec-2024
https://doi.org/10.1109/TCYB.2024.3403927
Antwi-Boasiako EZhou SLiao YKuada EDanso E(2024)Enhanced privacy-preserving distributed deep learning with application to fog-based IoTInternet of Things10.1016/j.iot.2024.10118326(101183)Online publication date: Jul-2024
https://doi.org/10.1016/j.iot.2024.101183
Ganesh SDheeraj CPadmavathy R(2021)A Deep Learning Framework to Preserve Privacy in Federated (Collaborative) LearningArtificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities10.1007/978-3-030-72236-4_1(1-28)Online publication date: 1-Jun-2021
https://doi.org/10.1007/978-3-030-72236-4_1
Li ZMa CWang D(2020)Achieving Multi-Hop PRE via Branching ProgramIEEE Transactions on Cloud Computing10.1109/TCC.2017.27640828:1(45-58)Online publication date: 1-Jan-2020
https://doi.org/10.1109/TCC.2017.2764082
Emmanuel AZhou SLiao YLiu Q(2020)Privacy-Preservation in Distributed Deep Neural Networks via Encryption of Selected Gradients2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)10.1109/HPCC-SmartCity-DSS50907.2020.00107(816-823)Online publication date: Dec-2020
https://doi.org/10.1109/HPCC-SmartCity-DSS50907.2020.00107
Phong LAono YHayashi TWang LMoriai S(2018)Privacy-Preserving Deep Learning via Additively Homomorphic EncryptionIEEE Transactions on Information Forensics and Security10.5555/3196160.319624513:5(1333-1345)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.5555/3196160.3196245
AONO YHAYASHI TPHONG LWANG L(2018)Efficient Homomorphic Encryption with Key Rotation and Security UpdateIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences10.1587/transfun.E101.A.39E101.A:1(39-50)Online publication date: 2018
https://doi.org/10.1587/transfun.E101.A.39
Phong LAono YHayashi TWang LMoriai S(2018)Privacy-Preserving Deep Learning via Additively Homomorphic EncryptionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2017.278798713:5(1333-1345)Online publication date: May-2018
https://doi.org/10.1109/TIFS.2017.2787987
Phong LAono YHayashi TWang LMoriai S(2017)Privacy-Preserving Deep Learning: Revisited and EnhancedApplications and Techniques in Information Security10.1007/978-981-10-5421-1_9(100-110)Online publication date: 23-Jun-2017
https://doi.org/10.1007/978-981-10-5421-1_9
Wang LHayashi TAono YPhong L(2017)A Generic yet Efficient Method for Secure Inner ProductNetwork and System Security10.1007/978-3-319-64701-2_16(217-232)Online publication date: 26-Jul-2017
https://doi.org/10.1007/978-3-319-64701-2_16

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