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Neural Computing and Applications

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21-07-2021 | Original Article

Privacy protected user identification using deep learning for smartphone-based participatory sensing applications

Published in: Neural Computing and Applications | Issue 24/2021

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Abstract

In smartphone-based crowd/participatory sensing systems, it is necessary to identify the actual sensor data provider. In this context, this paper attempts to recognize the users’ identity based on their gait patterns (i.e. unique walking patterns). More specifically, a deep convolution neural network (CNN) model is proposed for the user identification with accelerometer data generated from users smartphone sensors. The proposed model is evaluated based on the real-world benchmark dataset (accelerometer biometric competition data) having a total of 387 users accelerometer sensor readings (60 million data samples). The performance of the proposed CNN-based approach is also compared with five baseline methods namely Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbours (KNN). It is observed that the proposed model achieves better results (accuracy = 98.8%, precision = 0.94, recall = 0.97, and F1-score = 0.95) as compared to the baseline methods.

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Capponi A, Fiandrino C, Kantarci B, Foschini L, Kliazovich D, Bouvry P (2019) A survey on mobile crowdsensing systems: challenges, solutions, and opportunities. IEEE Commun Surv Tutor 21(3):2419–2465CrossRef

Middya AI, Roy S, Dutta J, Das R (2020) Jusense: a unified framework for participatory-based urban sensing system. Mobile Netw Appl 25:1249–1274CrossRef

Patra S, Middya AI, Roy S (2021) PotSpot: participatory sensing based monitoring system for pothole detection using deep learning. Multimed Tools Appl 80(16):25171–25195. https://doi.org/10.1007/s11042-021-10874-4CrossRef

Middya AI, Roy S (2021) Spatial interpolation techniques on participatory sensing data. ACM Trans Spat Algorithms Syst 7(3):1–32CrossRef

Nath P, Saha P, Middya AI, Roy S (2021) Long-term time-series pollution forecast using statistical and deep learning methods. Neural Comput Appl. https://doi.org/10.1007/s00521-021-05901-2CrossRef

Das R, Middya AI, Roy S (2021) High granular and short term time series forecasting of pm 2.5 air pollutant - a comparative review. Artif Intell Rev. https://doi.org/10.1007/s10462-021-09991-1CrossRef

Kar D, Middya AI, Roy S (2019) An approach to detect travel patterns using smartphone sensing. In: 2019 IEEE international conference on advanced networks and telecommunications systems (ANTS). IEEE, pp 1–6. https://doi.org/10.1109/ants47819.2019.9118073CrossRef

Katsini C, Belk M, Fidas C, Avouris N, Samaras G (2016) Security and usability in knowledge-based user authentication: a review. In: Proceedings of the 20th pan-hellenic conference on informatics. pp 1–6. https://doi.org/10.1145/3003733.3003764

Spolaor R, Li Q, Monaro M, Conti M, Gamberini L, Sartori G (2016) Biometric authentication methods on smartphones: a survey. PsychNology J 14(2–3):87–98.

10.

Gafurov D (2007) A survey of biometric gait recognition: approaches, security and challenges. In: Annual Norwegian Computer Science Conference Norway, pp 19–21

11.

Nambiar A, Bernardino A, Nascimento JC (2019) Gait-based person re-identification: a survey. ACM Comput Surv (CSUR) 52(2):1–34CrossRef

12.

Benegui C, Ionescu RT (2020) Convolutional neural networks for user identification based on motion sensors represented as images. IEEE Access 8(61):255–261–266

13.

Angrisano A, Bernardi ML, Cimitile M, Gaglione S, Vultaggio M (2020) Identification of walker identity using smartphone sensors: an experiment using ensemble learning. IEEE Access 8:27435–27447CrossRef

14.

Neverova N, Wolf C, Lacey G, Fridman L, Chandra D, Barbello B, Taylor G (2016) Learning human identity from motion patterns. IEEE Access 4:1810–1820CrossRef

15.

Buriro A, Crispo B, Delfrari F, Wrona K (2016) Hold and sign: a novel behavioral biometrics for smartphone user authentication. In: IEEE security and privacy workshops (SPW). IEEE, pp 276–285. https://doi.org/10.1109/spw.2016.20CrossRef

16.

Shi W, Yang J, Jiang Y, Yang F, Xiong Y (2011) Senguard: passive user identification on smartphones using multiple sensors. In: 2011 IEEE 7th international conference on wireless and mobile computing, networking and communications (WiMob). IEEE, pp 141–148. https://doi.org/10.1109/wimob.2011.6085412CrossRef

17.

Kwapisz JR, Weiss GM, Moore SA (2010) Cell phone-based biometric identification. In: 2010 Fourth IEEE international conference on biometrics: theory, applications and systems (BTAS). IEEE, pp 1–7. https://doi.org/10.1109/btas.2010.5634532CrossRef

18.

Yam C, Nixon MS, Carter JN (2004) Automated person recognition by walking and running via model-based approaches. Pattern Recognit 37(5):1057–1072CrossRef

19.

Tan W, Hsu J, Pinn F (2001) Method and system for token-based authentication.US Patent App. 09/792785

20.

Thavalengal S, Corcoran P (2016) User authentication on smartphones: focusing on iris biometrics. IEEE Consum Electron Mag 5(2):87–93CrossRef

21.

Subban R, Mankame DP (2013) A study of biometric approach using fingerprint recognition. Lect Notes Softw Eng 1(2):209CrossRef

22.

Hwang Y, Mun H-J, Lee J-W (2015) Face recognition system technologies for authentication system—a survey. J Converg Soc SMB 5(3):9–13

23.

Tao S, Zhang X, Cai H, Lv Z, Hu C, Xie H (2018) Gait based biometric personal authentication by using mems inertial sensors. J Ambient Intell Human Comput 9(5):1705–1712CrossRef

24.

Chai Y, Ren J, Zhao R, Jia J (2006) Automatic gait recognition using dynamic variance features. In: 7th International conference on automatic face and gesture recognition (FGR06). IEEE, pp 475–480. https://doi.org/10.1109/fgr.2006.24CrossRef

25.

Ahad MAR, Ngo TT, Antar AD, Ahmed M, Hossain T, Muramatsu D, Makihara Y, Inoue S, Yagi Y (2020) Wearable sensor-based gait analysis for age and gender estimation. Sensors 20(8):2424CrossRef

26.

Muaaz M, Mayrhofer R (2017) Smartphone-based gait recognition: from authentication to imitation. IEEE Trans Mobile Comput 16(11):3209–3221CrossRef

27.

Middleton L, Buss AA, Bazin A, Nixon MS (2005) A floor sensor system for gait recognition. In: Fourth IEEE workshop on automatic identification advanced technologies (AutoID’05). IEEE, pp 171–176. https://doi.org/10.1109/autoid.2005.2CrossRef

28.

Accelerometer biometric competition dataset. https://www.kaggle.com/c/accelerometer-biometric-competition/overview, accessed Feb 02 2021

29.

Dargan S, Kumar M, Ayyagari MR, Kumar G (2019) A survey of deep learning and its applications: a new paradigm to machine learning. Arch Comput Methods Eng 27(4):1071–1092. https://doi.org/10.1007/s11831-019-09344-wMathSciNetCrossRef

30.

Chen B, Deng W, Du J (2017) Noisy softmax: improving the generalization ability of DCNN via postponing the early softmax saturation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5372–5381. https://doi.org/10.1109/cvpr.2017.428

31.

Adam optimizer. https://keras.io/api/optimizers/adam/, accessed Feb 02 2021

32.

Feurer M, Hutter F (2019) Hyperparameter optimization. Automated machine learning. Springer, Cham, pp 3–33CrossRef

33.

Python. https://www.python.org/downloads/release/python-370/, accessed Feb 02 2021

34.

Nickel C, Brandt H, Busch C (2011) Benchmarking the performance of SVMS and HMMS for accelerometer-based biometric gait recognition. In: 2011 IEEE international symposium on signal processing and information technology (ISSPIT). IEEE, pp 281–286. https://doi.org/10.1109/isspit.2011.6151574CrossRef

35.

Kececi A, Yildirak A, Ozyazici K, Ayluctarhan G, Agbulut O, Zincir I (2020) Implementation of machine learning algorithms for gait recognition. Eng Sci Technol Int J 23(4):931–937

36.

Shi L-F, Qiu C-X, Xin D-J, Liu G-X (2020) Gait recognition via random forests based on wearable inertial measurement unit. J Ambient Intell Human Comput 11(11):5329–5340. https://doi.org/10.1007/s12652-020-01870-xCrossRef

37.

Gasso G (2019) Logistic regression [Online], Available: https://bit.ly/3xOOTij

38.

Choi S, Youn I-H, LeMay R, Burns S, Youn J-H (2014) Biometric gait recognition based on wireless acceleration sensor using k-nearest neighbor classification. In: 2014 International conference on computing, networking and communications (ICNC). IEEE, pp 1091–1095. https://doi.org/10.1109/iccnc.2014.6785491CrossRef

Title: Privacy protected user identification using deep learning for smartphone-based participatory sensing applications
Publication date: 21-07-2021
Published in: Neural Computing and Applications / Issue 24/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-021-06319-6

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