Skip to main content
SpringerLink
Log in

Pros and Cons of X-Based PPDL

  • Chapter
  • First Online:
Privacy-Preserving Deep Learning

Part of the book series: SpringerBriefs on Cyber Security Systems and Networks ((BRIEFSCSSN))

  • 732 Accesses

Abstract

This chapter discusses the comparison of all of privacy-preserving deep learning methods, highlighting the pros and cons of each method based on privacy parameters, used specific neural network and dataset type from the point of performance. We also provide our analysis about the weakness of each privacy-preserving deep learning method and our feasible solution to address their weakness.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Aono Y, Hayashi T, Wang L, Moriai S et al (2017) Privacy-preserving deep learning via additively homomorphic encryption. IEEE Trans Inf Forensics Secur 13(5):1333–1345

    Google Scholar 

  • Bourse F, Minelli M, Minihold M, Paillier P (2018) Fast homomorphic evaluation of deep discretized neural networks. In: Annual international cryptology conference. Springer, pp 483–512

    Google Scholar 

  • Bu Z, Dong J, Long Q, Su WJ (2019) Deep learning with gaussian differential privacy. arXiv:1911.11607

  • Chabanne H, de Wargny A, Milgram J, Morel C, Prouff E (2017) Privacy-preserving classification on deep neural network. IACR Cryptol ePrint Arch 2017:35

    Google Scholar 

  • Chase M, Gilad-Bachrach R, Laine K, Lauter KE, Rindal P (2017) Private collaborative neural network learning. IACR Cryptol ePrint Arch 2017:762

    Google Scholar 

  • Cheon JH, Han K, Kim A, Kim M, Song Y (2018) Bootstrapping for approximate homomorphic encryption. In: Annual international conference on the theory and applications of cryptographic techniques. Springer, pp 360–384

    Google Scholar 

  • Chillotti I, Gama N, Georgieva M, Izabachene M (2016) Faster fully homomorphic encryption: bootstrapping in less than 0.1 s. In: International conference on the theory and application of cryptology and information security. Springer, pp 3–33

    Google Scholar 

  • Chou E, Beal J, Levy D, Yeung S, Haque A, Fei-Fei L (2018) Faster cryptonets: leveraging sparsity for real-world encrypted inference. arXiv:1811.09953

  • Dean J, Corrado G, Monga R, Chen K, Devin M, Mao M, Ranzato M, Senior A, Tucker P, Yang K et al (2012) Large scale distributed deep networks. In: Advances in neural information processing systems, pp 1223–1231

    Google Scholar 

  • Ducas L, Micciancio D (2015) FHEW: bootstrapping homomorphic encryption in less than a second. In: Annual international conference on the theory and applications of cryptographic techniques. Springer, pp 617–640

    Google Scholar 

  • Gilad-Bachrach R, Dowlin N, Laine K, Lauter K, Naehrig M, Wernsing J (2016) Cryptonets: applying neural networks to encrypted data with high throughput and accuracy. In: International conference on machine learning, pp 201–210

    Google Scholar 

  • Graepel T, Lauter K, Naehrig M (2012) Ml confidential: machine learning on encrypted data. In: International conference on information security and cryptology. Springer, pp 1–21

    Google Scholar 

  • Hamm J, Champion AC, Chen G, Belkin M, Xuan D (2015) Crowd-ml: a privacy-preserving learning framework for a crowd of smart devices. In: 2015 IEEE 35th international conference on distributed computing systems. IEEE, pp 11–20

    Google Scholar 

  • Hardy S, Henecka W, Ivey-Law H, Nock R, Patrini G, Smith G, Thorne B (2017) Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. arXiv:1711.10677

  • Hesamifard E, Takabi H, Ghasemi M (2017) Cryptodl: deep neural networks over encrypted data. arXiv:1711.05189

  • Hunt T, Song C, Shokri R, Shmatikov V, Witchel E (2018) Chiron: privacy-preserving machine learning as a service. arXiv:1803.05961

  • Jiang X, Kim M, Lauter K, Song Y (2018) Secure outsourced matrix computation and application to neural networks. In: Proceedings of the 2018 ACM SIGSAC conference on computer and communications security, pp 1209–1222

    Google Scholar 

  • Jiang L, Tan R, Lou X, Lin G (2019) On lightweight privacy-preserving collaborative learning for internet-of-things objects. In: Proceedings of the international conference on internet of things design and implementation, pp 70–81

    Google Scholar 

  • Juvekar C, Vaikuntanathan V, Chandrakasan A (2018) {GAZELLE}: a low latency framework for secure neural network inference. In: 27th USENIX security symposium (USENIX) security 18), pp 1651–1669

    Google Scholar 

  • Kumar N, Rathee M, Chandran N, Gupta D, Rastogi A, Sharma R (2019) Cryptflow: secure tensorflow inference. arXiv:1909.07814

  • Liu J, Juuti M, Lu Y, Asokan N (2017) Oblivious neural network predictions via minionn transformations. In: Proceedings of the 2017 ACM SIGSAC conference on computer and communications security, pp 619–631

    Google Scholar 

  • Liu W, Pan F, Wang XA, Cao Y, Tang D (2018) Privacy-preserving all convolutional net based on homomorphic encryption. In: International conference on network-based information systems. Springer, pp 752–762

    Google Scholar 

  • McMahan HB, Moore E, Ramage D, Hampson S et al (2016) Communication-efficient learning of deep networks from decentralized data. arXiv:1602.05629

  • Mohassel P, Rindal P (2018) Aby3: a mixed protocol framework for machine learning. In: Proceedings of the 2018 ACM SIGSAC conference on computer and communications security, pp 35–52

    Google Scholar 

  • Mohassel P, Zhang Y (2017) Secureml: a system for scalable privacy-preserving machine learning. In: IEEE symposium on security and privacy (SP). IEEE, 19–38

    Google Scholar 

  • Mugunthan V, Peraire-Bueno A, Kagal L (2020) Privacyfl: a simulator for privacy-preserving and secure federated learning. arXiv:2002.08423

  • Ohrimenko O, Schuster F, Fournet C, Mehta A, Nowozin S, Vaswani K, Costa M (2016) Oblivious multi-party machine learning on trusted processors. 25th {USENIX} security symposium

    Google Scholar 

  • Papernot N, Abadi M, Erlingsson U, Goodfellow I, Talwar K (2016) Semi-supervised knowledge transfer for deep learning from private training data. arXiv:1610.05755

  • Riazi MS, Weinert C, Tkachenko O, Songhori EM, Schneider T, Koushanfar F (2018) Chameleon: a hybrid secure computation framework for machine learning applications. In: Proceedings of the 2018 on Asia conference on computer and communications security, pp 707–721

    Google Scholar 

  • Rouhani BD, Riazi MS, Koushanfar F (2018) Deepsecure: scalable provably-secure deep learning. In: Proceedings of the 55th annual design automation conference, pp 1–6

    Google Scholar 

  • Ryffel T, Trask A, Dahl M, Wagner B, Mancuso J, Rueckert D, Passerat-Palmbach J (2018) A generic framework for privacy preserving deep learning. arXiv:1811.04017

  • Sanyal A, Kusner MJ, Gascon A, Kanade V (2018) Tapas: tricks to accelerate (encrypted) prediction as a service. arXiv:1806.03461

  • So J, Guler B, Avestimehr AS, Mohassel P (2019) Codedprivateml: a fast and privacy-preserving framework for distributed machine learning. arXiv:1902.00641

  • Song G, Chai W (2018) Collaborative learning for deep neural networks. In: Advances in neural information processing systems, pp 1832–1841

    Google Scholar 

  • Sun Z, Kairouz P, Suresh AT, McMahan HB (2019) Can you really backdoor federated learning? arXiv:1911.07963

  • Tramer F, Boneh D (2018) Slalom: fast, verifiable and private execution of neural networks in trusted hardware. arXiv:1806.03287

  • Truex S, Baracaldo N, Anwar A, Steinke T, Ludwig H, Zhang R, Zhou Y (2019) A hybrid approach to privacy-preserving federated learning. In: Proceedings of the 12th ACM workshop on artificial intelligence and security, pp 1–11

    Google Scholar 

  • Vergara-Laurens IJ, Jaimes LG, Labrador MA (2016) Privacy-preserving mechanisms for crowdsensing: survey and research challenges. IEEE Internet Things J 4(4):855–869

    Article  Google Scholar 

  • Wagh S, Gupta D, Chandran N (2019) Securenn: 3-party secure computation for neural network training. Proc Priv Enhanc Technol 3:26–49

    Google Scholar 

  • Wang Z, Song M, Zhang Z, Song Y, Wang Q, Qi H (2019) Beyond inferring class representatives: User-level privacy leakage from federated learning. In: IEEE INFOCOM 2019-IEEE conference on computer communications. IEEE, pp 2512–2520

    Google Scholar 

  • Xue H, Huang Z, Lian H, Qiu W, Guo J, Wang S, Gong Z (2018) Distributed large scale privacy-preserving deep mining. In: 2018 IEEE third international conference on data science in cyberspace (DSC). IEEE, pp 418–422

    Google Scholar 

  • Xu R, Joshi JB, Li C (2019) Cryptonn: training neural networks over encrypted data. In: 2019 IEEE 39th international conference on distributed computing systems (ICDCS). IEEE, pp 1199–1209

    Google Scholar 

  • Zhang Q, Yang LT, Castiglione A, Chen Z, Li P (2019) Secure weighted possibilistic c-means algorithm on cloud for clustering big data. Inf Sci 479:515–525

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computing, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea (Republic of)

    Kwangjo Kim & Harry Chandra Tanuwidjaja

Authors
  1. Kwangjo Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Harry Chandra Tanuwidjaja
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kwangjo Kim .

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kim, K., Tanuwidjaja, H.C. (2021). Pros and Cons of X-Based PPDL. In: Privacy-Preserving Deep Learning. SpringerBriefs on Cyber Security Systems and Networks. Springer, Singapore. https://doi.org/10.1007/978-981-16-3764-3_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-3764-3_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-3763-6

  • Online ISBN: 978-981-16-3764-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation