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Neural Processing Letters

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10-03-2023

Secure Multi-Party Computation for Personalized Human Activity Recognition

Authors: David Melanson, Ricardo Maia, Hee-Seok Kim, Anderson Nascimento, Martine De Cock

Published in: Neural Processing Letters | Issue 3/2023

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Abstract

Calibrating Human Activity Recognition (HAR) models to end-users with Transfer Learning (TL) often yields significant accuracy improvements. Such TL is by design done based on very personal data collected by sensors worn close to the human body. To protect the users’ privacy, we therefore introduce Secure Multi-Party Computation (MPC) protocols for personalization of HAR models, and for secure activity recognition with the personalized models. Our MPC protocols do not require the end-users to reveal their sensitive data in an unencrypted manner, nor do they require the application developer to disclose their trained model parameters or any other sensitive or proprietary information with anyone in plaintext. Through experiments on HAR benchmark datasets, we demonstrate that our privacy-preserving solution yields the same accuracy gains as TL in-the-clear, i.e. when no measures to protect privacy are in place, and that our approach is fast enough for use in practice.

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Throughout this paper, we use the terms “secure” and “privacy-preserving” as synonyms.

Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359CrossRef

Yang Q, Zhang Y, Dai W, Pan SJ (2020) Transfer learning. Cambridge University Press, UKCrossRef

Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, Xiong H, He Q (2021) A comprehensive survey on transfer learning. Proc IEEE 109(1):43–76CrossRef

Hernandez N, Lundström J, Favela J, McChesney I, Arnrich B (2020) Literature review on transfer learning for human activity recognition using mobile and wearable devices with environmental technology. Springer Nature Computer Science 1(66)

Mehrang S, Pietila J, Tolonen J, Helander E, Jimison H, Pavel M, Korhonen I (2017) Human activity recognition using a single optical heart rate monitoring wristband equipped with triaxial accelerometer. In: Joint conference of the European Medical and Biological Engineering Conference (EMBEC) and the nordic-baltic conference on biomedical engineering and medical physics (NBC), pp 587–590

Shoaib M, Bosch S, Incel O, Scholten H, Havinga P (2014) Fusion of smartphone motion sensors for physical activity recognition. Sensors 14:10146–10176CrossRef

Lin C-Y, Marculescu R (2020) Model personalization for human activity recognition. In: 2020 IEEE international conference on pervasive computing and communications workshops (PerCom Workshops)

Lin Y-P, Jung T-P (2017) Improving EEG-based emotion classification using conditional transfer learning. Front Hum Neurosci 11:334CrossRef

Wu D, Xu Y, Lu B.-L (2020) Transfer learning for EEG-based brain-computer interfaces: a review of progress made since 2016. IEEE Trans Cognit Dev Syst, pp 4–19

10.

Carlini N, Liu C, Erlingsson Ú, Kos J, Song D (2019) The secret sharer: evaluating and testing unintended memorization in neural networks. In: 28th USENIX security symposium, pp 267–284

11.

Balli S, Saǧbaş EA, Peker M (2019) Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm. Meas Control 52(1–2):37–45CrossRef

12.

Cramer R, Damgard I, Nielsen J (2015) Secure multiparty computation and secret sharing. Cambridge University Press, New York

13.

Kairouz P, McMahan HB, Avent B, Bellet A, Bennis M, Bhagoji AN, Bonawitz K, Charles Z, Cormode G, Cummings R, D’Oliveira RGL, Eichner H, Rouayheb SE, Evans D, Gardner J, Garrett Z, Gascón A, Ghazi B, Gibbons PB, Gruteser M, Harchaoui Z, He C, He L, Huo Z, Hutchinson B, Hsu J, Jaggi M, Javidi T, Joshi G, Khodak M, Konecný J, Korolova A, Koushanfar F, Koyejo S, Lepoint T, Liu Y, Mittal P, Mohri M, Nock R, Özgür A, Pagh R, Qi H, Ramage D, Raskar R, Raykova M, Song D, Song W, Stich SU, Sun Z, Suresh AT, Tramèr F, Vepakomma P, Wang J, Xiong L, Xu Z, Yang Q, Yu FX, Yu H, Zhao S (2021) Advances and open problems in federated learning. Found Trends Mach Learn 14(1–2):1–210

14.

Presotto R, Civitarese G, Bettini C (2022) FedCLAR: federated clustering for personalized sensor-based human activity recognition. In: 2022 IEEE international conference on pervasive computing and communications (PerCom), pp 227–236

15.

Chen Y, Qin X, Wang J, Yu C, Gao W (2020) FedHealth: a federated transfer learning framework for wearable healthcare. IEEE Intell Syst 35(4):83–93CrossRef

16.

Hu R, Guo Y, Li H, Pei Q, Gong Y (2020) Personalized federated learning with differential privacy. IEEE Internet Things J 7(10):9530–9539CrossRef

17.

Liu S, Wang J, Zhang W (2021) Federated personalized random forest for human activity recognition. Math Biosci Eng 19:953–971CrossRefMATH

18.

Dwork C, Roth A (2014) The algorithmic foundations of differential privacy. Found Trends Theor Comput Sci 9(3–4):211–407MathSciNetMATH

19.

Hashemian M, Razzazi F, Zarrabi H, Moin MS (2019) A privacy-preserving distributed transfer learning in activity recognition. Telecommun Syst: Model Anal Des Manag 72(1):69–79CrossRef

20.

Hashemian M, Razzazi F, Zarrabi H, Moin M (2021) Semi-supervised and unsupervised privacy-preserving distributed transfer learning approach in HAR systems. Wirel Pers Commun 117:1–18CrossRef

21.

Evans D, Kolesnikov V, Rosulek M (2018) A pragmatic introduction to secure multi-party computation. Found Trends Privacy Secur 2(2–3):70–246CrossRef

22.

Dalskov A, Escudero D, Keller M (2020) Secure evaluation of quantized neural networks. Proc Privacy Enh Technol 2020(4):355–375

23.

De Cock M, Dowsley R, Horst C, Katti R, Nascimento A, Poon W-S, Truex S (2019) Efficient and private scoring of decision trees, support vector machines and logistic regression models based on pre-computation. IEEE Trans Dependable Secure Comput 16(2):217–230CrossRef

24.

Fritchman K, Saminathan K, Dowsley R, Hughes T, De Cock M, Nascimento A, Teredesai A (2018) Privacy-preserving scoring of tree ensembles: a novel framework for AI in healthcare. In: Proceedings of 2018 IEEE BigData, pp 2412–2421

25.

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

26.

Kumar N, Rathee M, Chandran N, Gupta D, Rastogi A, Sharma R (2020) CrypTFlow: secure TensorFlow inference. In: 41st IEEE symposium on security and privacy, pp 336–353

27.

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

28.

Reich D, Todoki A, Dowsley R, De Cock M, Nascimento A (2019) Privacy-preserving classification of personal text messages with secure multi-party computation. In: Advances in neural information processing systems (NeurIPS), vol 32, pp 3752–3764

29.

Riazi S, Weinert C, Tkachenko O, Songhori E.M, Schneider T, Koushanfar F (2018) Chameleon: A hybrid secure computation framework for machine learning applications. In: Asia conference on computer and communications security, pp 707–721. ACM

30.

Rouhani B.D, Riazi M.S, Koushanfar F (2018) DeepSecure: scalable provably-secure deep learning. In: 55th Annual design automation conference (DAC)

31.

Agarwal A, Dowsley R, McKinney ND, Wu D, Lin C-T, De Cock M, Nascimento A (2019) Protecting privacy of users in brain-computer interface applications. IEEE Trans Neural Syst Rehabil Eng 27(8):1546–1555CrossRef

32.

De Cock M, Dowsley R, Nascimento ACA, Newman SC (2015) Fast, privacy preserving linear regression over distributed datasets based on pre-distributed data. In: Proceedings of the 8th ACM workshop on artificial intelligence and security, pp 3–14

33.

Nikolaenko V, Weinsberg U, Ioannidis S, Joye M, Boneh D, Taft N (2013) Privacy-preserving ridge regression on hundreds of millions of records. In: 2013 IEEE symposium on security and privacy (SP), pp 334–348

34.

Abspoel M, Escudero D, Volgushev N (2021) Secure training of decision trees with continuous attributes. Proc Privacy Enh Technol 2021(1):167–187

35.

de Hoogh S, Schoenmakers B, Chen P, op den Akker H (2014) Practical secure decision tree learning in a teletreatment application. In: International conference on financial cryptography and data security, pp 179–194

36.

Lindell Y, Pinkas B (2000) Privacy preserving data mining. In: Annual international cryptology conference, pp 36–54

37.

Adams S, Choudhary C, De Cock M, Dowsley R, Melanson D, Nascimento AC, Railsback D, Shen J (2022) Privacy-preserving training of tree ensembles over continuous data. Proc Privacy Enh Technol 2:205–226

38.

Agrawal N, Shahin Shamsabadi A, Kusner M.J, Gascón A (2019) QUOTIENT: two-party secure neural network training and prediction. In: ACM SIGSAC conference on computer and communications security, pp 1231–1247

39.

De Cock M, Dowsley R, Nascimento ACA, Railsback D, Shen J, Todoki A (2021) High performance logistic regression for privacy-preserving genome analysis. BMC Med Genomics 14(1):23CrossRef

40.

Guo C, Hannun A, Knott B, van der Maaten L, Tygert M, Zhu R (2021) Secure multiparty computations in floating-point arithmetic. Inf Inference: J IMA 11(1):103–135MathSciNetCrossRefMATH

41.

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

42.

Wagh S, Gupta D, Chandran N (2019) SecureNN: 3-party secure computation for neural network training. Proc Privacy Enh Technol 2019(3):26–49

43.

Beaver D (1997) Commodity-based cryptography (extended abstract). In: Proceedings of the twenty-ninth annual ACM symposium on theory of computing. STOC ’97, pp 446–455

44.

Rabin MO (2008) How to exchange secrets with oblivious transfer. IACR Cryptol. ePrint Arch 2005(187)

45.

Araki T, Furukawa J, Lindell Y, Nof A, Ohara K (2016) High-throughput semi-honest secure three-party computation with an honest majority. In: ACM SIGSAC Conference on computer and communications security, pp 805–817

46.

Keller M (2020) MP-SPDZ: a versatile framework for multi-party computation. In: Proceedings of the 2020 ACM SIGSAC conference on computer and communications security, pp 1575–1590

47.

Mishra P, Lehmkuhl R, Srinivasan A, Zheng W, Popa RA (2020) Delphi: a cryptographic inference service for neural networks. In: 29th USENIX security symposium, pp 2505–2522

48.

Adams S, Melanson D, De Cock M (2021) Private text classification with convolutional neural networks. In: Proceedings of the third workshop on privacy in natural language processing (NAACL Workshops), pp 53–58

49.

Escudero D, Ghosh S, Keller M, Rachuri R, Scholl P (2020) Improved primitives for MPC over mixed arithmetic-binary circuits. In: Annual international cryptology conference, pp 823–852

50.

Barshan B, Yüksek MC (2014) Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units. Comput J 57(11):1649–1667CrossRef

51.

Zhang A, Lipton ZC, Li M, Smola AJ (2022) Dive into deep learning, https://d2l.ai

52.

Chollet F, et al.: Keras. https://github.com/fchollet/keras

Title: Secure Multi-Party Computation for Personalized Human Activity Recognition
Authors: David Melanson
Ricardo Maia
Hee-Seok Kim
Anderson Nascimento
Martine De Cock
Publication date: 10-03-2023
Publisher: Springer US
Published in: Neural Processing Letters / Issue 3/2023
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-023-11182-8

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