research-article

Cloud-enabled privacy-preserving collaborative learning for mobile sensing

Authors:

Ramesh GovindanAuthors Info & Claims

SenSys '12: Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems

Pages 57 - 70

https://doi.org/10.1145/2426656.2426663

Published: 06 November 2012 Publication History

Abstract

In this paper, we consider the design of a system in which Internet-connected mobile users contribute sensor data as training samples, and collaborate on building a model for classification tasks such as activity or context recognition. Constructing the model can naturally be performed by a service running in the cloud, but users may be more inclined to contribute training samples if the privacy of these data could be ensured. Thus, in this paper, we focus on privacy-preserving collaborative learning for the mobile setting, which addresses several competing challenges not previously considered in the literature: supporting complex classification methods like support vector machines, respecting mobile computing and communication constraints, and enabling user-determined privacy levels. Our approach, Pickle, ensures classification accuracy even in the presence of significantly perturbed training samples, is robust to methods that attempt to infer the original data or poison the model, and imposes minimal costs. We validate these claims using a user study, many real-world datasets and two different implementations of Pickle.

References

[1]

T. Abdelzaher, Y. Anokwa, P. Boda, J. Burke, D. Estrin, L. Guibas, A. Kansal, S. Madden, and J. Reich. Mobiscopes for human spaces. IEEE Pervasive Computing, 6(2), 2007.

Digital Library

[2]

D. Agrawal and C. Aggarwal. On the design and quantification of privacy preserving data mining algorithms. In Proceedings of the 20th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS '01), 2001.

Digital Library

[3]

R. Agrawal and R. Srikant. Privacy-preserving data mining. In Proceedings of the 2000 ACM Special Interest Group on Management Of Data (SIGMOD '00), 2000.

Digital Library

[4]

H. Ahmadi, N. Pham, R. Ganti, T. Abdelzaher, S. Nath, and J. Han. A framework of energy efficient mobile sensing for automatic human state recognition. In Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services (MobiSys '09), 2009.

Digital Library

[5]

H. Ahmadi, N. Pham, R. Ganti, T. Abdelzaher, S. Nath, and J. Han. Privacy-aware regression modeling of participatory sensing data. In Proceedings of the 8th ACM Conference on Embedded Network Sensor Systems (SenSys '10), 2010.

Digital Library

[6]

M. Almeida, G. Cavalheiro, A. Pereira, and A. Andrade. Investigation of age-related changes in physiological kinetic tremor. Annals of Biomedical Engineering, 38(11), 2010.

[7]

M. Azizyan, I. Constandache, and R. Choudhury. Surroundsense: mobile phone localization via ambience fingerprinting. In Proceedings of the 15th Annual International Conference on Mobile Computing and Networking (Mobicom '09), 2009.

Digital Library

[8]

X. Bao and R. Choudhury. Movi: mobile phone based video highlights via collaborative sensing. In Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services (MobiSys '10), 2010.

Digital Library

[9]

S. Bay and M. Schwabacher. Mining distance-based outliers in near linear time with randomization and a simple pruning rule. In Proceedings of the 9th ACM Special Interest Group on Knowledge Discovery and Data mining (SIGKDD '03), 2003.

Digital Library

[10]

A. Bose, X. Hu, K. Shin, and T. Park. Behavioral detection of malware on mobile handsets. In Proceedings of the 6th International Conference on Mobile Systems, Applications, and Services (MobiSys '08), 2008.

Digital Library

[11]

R. Brand. Microdata protection through noise addition. Inference Control in Statistical Databases, 2002.

Digital Library

[12]

J. Burke, D. Estrin, M. Hansen, A. Parker, N. Ramanathan, S. Reddy, and M. Srivastava. Participatory sensing. In Proceedings of the 2006 World Sensor Web Workshop, 2006.

[13]

K. Chaudhuri, C. Monteleoni, and A. Sarwate. Differentially private empirical risk minimization. Journal of Machine Learning Research, 12, 2011.

Digital Library

[14]

K. Chen and L. Liu. Privacy-preserving data classification with rotation perturbation. In Proceedings of the 5th IEEE International Conference on Data Mining (ICDM '05), 2005.

Digital Library

[15]

K. Chen and L. Liu. Towards attack-resilient geometric data perturbation. In SIAM International Conference on Data Mining (SDM '07), 2007.

[16]

C. Cortes and V. Vapnik. Support-vector network. Machine Learning, 20(3):273--297, 1995.

Digital Library

[17]

S. Eisenman, E. Miluzzo, N. Lane, R. Peterson, G. Ahn, and A. Campbell. The bikenet mobile sensing system for cyclist experience mapping. In Proceedings of the 5th ACM Conference on Embedded Network Sensor Systems (SenSys '07), 2007.

Digital Library

[18]

D. Ellis. PLP and RASTA (and MFCC, and inversion) in Matlab, 2005. online web resource.

[19]

S. Ganta, S. Kasiviswanathan, and A. Smith. Composition attacks and auxiliary information in data privacy. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD '08), 2008.

Digital Library

[20]

J. Garofolo, L. Lamel, W. Fisher, J. Fiscus, D. Pallett, and N. Dahlgren. Darpa timit acoustic phonetic continuous speech corpus cdrom, 1993.

[21]

Y. Hu, F. Liang, and G. He. Privacy-preserving svm classification on vertically partitioned data without secure multi-party computation. In Proceedings of the 5th International Conference on Natural Computation (ICNC '09), 2009.

Digital Library

[22]

Z. Huang, W. Du, and B. Chen. Deriving private information from randomized data. In Proceedings of the 2005 ACM Special Interest Group on Management Of Data (SIGMOD '05), 2005.

Digital Library

[23]

B. Hull, V. Bychkovsky, Y. Zhang, K. Chen, M. Goraczko, A. Miu, E. Shih, H. Balakrishnan, and S. Madden. Cartel: a distributed mobile sensor computing system. In Proceedings of the 4th ACM Conference on Embedded Network Sensor Systems (SenSys '06), 2006.

Digital Library

[24]

G. Jagannathan and R. Wright. Privacy-preserving distributed k-means clustering over arbitrarily partitioned data. In Proceedings of the 11th ACM Special Interest Group on Knowledge Discovery and Data mining (SIGKDD '05), 2005.

Digital Library

[25]

W. Johnson and J. Lindenstrauss. Extensions of lipshitz mapping into hilbert space. Contemp. Maths., 26, 1984.

[26]

S. Kargupta, S. Datta, Q. Wang, and K. Sivakumar. On the privacy preserving properties of random data perturbation techniques. In Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM '03), 2003.

Digital Library

[27]

D. Kim, M. Azim, and J. Park. Privacy preserving support vector machines in wireless sensor networks. In Proceedings of the 3rd International Conference on Availability, Reliability and Security (ARES '08), 2008.

Digital Library

[28]

S. Laur, H. Lipmaa, and T. Mielikainen. Cryptographically private support vector machines. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD '06), 2006.

Digital Library

[29]

K. Lin and M. Chen. Releasing the svm classifier with privacy-preservation. In Prodeedings of the 8th IEEE International Conference on Data Mining (ICDM '08), 2008.

Digital Library

[30]

K. Lin and M. Chen. Privacy-preserving outsourcing support vector machines with random transformation. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD '10), 2010.

Digital Library

[31]

K. Lin and M. Chen. On the design and analysis of the privacy-preserving svm classifier. IEEE Transactions on Knowledge and Data Engineering, 23(11), 2011.

Digital Library

[32]

K. Liu, H. Kargupta, and J. Ryan. Random projection-based multi-plicative data perturbation for privacy preserving distributed data mining. IEEE Trans. on Knowledge and Data Engineering, 18(1), 2006.

Digital Library

[33]

L. Liu, J. Wang, and J. Zhang. Wavelet-based data perturbation for simultaneous privacy-preserving and statistics-preserving. In Proceedings of the 8th IEEE International Conference on Data Mining Workshops, 2008.

Digital Library

[34]

H. Lu, B. Brush, B. Priyantha, A. Karlson, and J. Liu. Speakersense: Energy efficient unobtrusive speaker identification on mobile phones. In Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services (MobiSys '11), 2011.

[35]

H. Lu, W. Pan, N. Lane, T. Choudhury, and A. Campbell. Soundsense: scalable sound sensing for people-centric applications on mobile phones. In Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services (MobiSys '09), 2009.

Digital Library

[36]

T. Maekawa, Y. Yanagisawa, Y. Kishino, K. Ishiguro, K. Kamei, Y. Sakurai, and T. Okadome. Object-based activity recognition with heterogeneous sensors on wrist. 2010.

[37]

E. Magkos, M. Maragoudakis, V. Chrissikopoulos, and S. Gritzalis. Accurate and large-scale privacy-preserving data mining using the election paradigm. Data and Knowledge Engineering, 68(11), 2009.

Digital Library

[38]

O. Mangasarian and E. Wild. Privacy-preserving classification of horizontally partitioned data via random kernels. In Prodeedings of the 8th IEEE International Conference on Data Mining (ICDM '08), 2008.

[39]

O. Mangasarian, E. Wild, and G. Fung. Privacy-preserving classification of vertically partitioned data via random kernels. ACM Transactions on Knowledge Discovery from Data, 2(3), 2008.

Digital Library

[40]

S. Mathur, W. Trappe, N. Mandayam, C. Ye, and A. Reznik. Radio-telepathy: extracting a secret key from an unauthenticated wireless channel. In Proceedings of the 14th ACM International Conference on Mobile Computing and Networking (Mobicom '08), 2008.

Digital Library

[41]

B. Milner and X. Shao. Clean speech reconstruction from MFCC vectors and fundamental frequency using an integrated front-end. Speech Communication, 48(6), 2006.

[42]

E. Miluzzo, C. Cornelius, A. Ramaswamy, T. Choudhury, Z. Liu, and A. Campbell. Darwin phones: the evolution of sensing and inference on mobile phones. In Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services (MobiSys '10), 2010.

Digital Library

[43]

E. Miluzzo, N. Lane, K. Fodor, R. Peterson, H. Lu, M. Musolesi, S. Eisenman, X. Zheng, and A. Campbell. Sensing meets mobile social networks: the design, implementation and evaluation of the cenceme application. In Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems (SenSys '08), 2008.

Digital Library

[44]

P. Mohan, V. Padmanabhan, and R. Ramjee. Nericell: Rich monitoring of road and traffic conditions using mobile smartphones. In Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems (SenSys '08), 2008.

Digital Library

[45]

A. Navia-Vazquez, D. Gutierrez-Gonzalez, E. Parrado-Hernandez, and J. Navarro-Abellan. Distributed support vector machines. IEEE Transactions on Neural Networks, 2006.

Digital Library

[46]

N. Pham, R. Ganti, Y. Uddin, S. Nath, and T. Abdelzaher. Privacy-preserving reconstruction of multidimensional data maps in vehicular participatory sensing. In Wireless Sensor Networks, 2011.

Digital Library

[47]

J. Qiang, B. Yang, Q. Li, and L. Jing. Privacy-preserving svm of horizontally partitioned data for linear classification. In Prodeedings of the 4th International Congress on Image and Signal Processing (CISP '11), 2011.

[48]

B. Rubinstein, P. Bartlett, L. Huang, and N. Taft. Learning in a large function space: Privacy-preserving mechanisms for svm learning. Arxiv Preprint arXiv:0911.5708, 2009.

[49]

V. Tan and S. Ng. Privacy-preserving sharing of horizontally-distributed private data for constructing accurate classifiers. In Proceedings of the 1st ACM SIGKDD International Conference on Privacy, Security, and Trust in KDD, 2007.

Digital Library

[50]

J. Vaidya and C. Clifton. Privacy-preserving k-means clustering over vertically partitioned data. In Proceedings of the 9th ACM Special Interest Group on Knowledge Discovery and Data mining (SIGKDD '03), 2003.

Digital Library

[51]

J. Vaidya, H. Yu, and X. Jiang. Privacy preserving svm classification. Knowledge and Information Systems, 14(2), 2008.

Digital Library

[52]

J. Wang and J. Zhang. Nnmf-based factorization techniques for high-accuracy privacy protection on non-negative-valued datasets. In Proceedings of the 6th IEEE International Conference on Data Mining Workshops, 2006.

Digital Library

[53]

K. Weinberger and L. Saul. Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research, 10(2), 2009.

Digital Library

[54]

P. Weinzaepfel, H. Jegou, and P. Perez. Reconstructing an image from its local descriptors. In Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '11), 2011.

Digital Library

[55]

S. Xu and S. Lai. Fast fourier transform based data perturbation method for privacy protection. In IEEE Intelligence and Security Informatics, 2007.

[56]

S. Xu, J. Zhang, D. Han, and J. Wang. Singular value decomposition based data distortion strategy for privacy protection. Knowledge and Information Systems, 10(3), 2006.

Digital Library

[57]

H. Yu, J. Vaidya, and X. Jiang. Privacy-preserving svm classification on vertically partitioned data. In Proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD '06), 2006.

Digital Library

[58]

H. Yu, J. Vaidya, and X. Jiang. Privacy-preserving svm using nonlinear kernels on horizontally partitioned data. In Proceedings of the 21st Annual ACM Symposium on Applied Computing (SAC '06), 2006.

Digital Library

[59]

J. Zhan, L. Chang, and S. Matwin. Privacy-preserving support vector machines learning. In Proceedings of the 2005 International Conference on Electronic Business (ICEB '05), 2005.

Cited By

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Al MYagli SKung S(2023)Privacy Enhancing Machine Learning via Removal of Unwanted DependenciesIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.311083134:6(3019-3033)Online publication date: Jun-2023
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Cloud-enabled privacy-preserving collaborative learning for mobile sensing

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cover image ACM Conferences

SenSys '12: Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems

November 2012

404 pages

ISBN:9781450311694

DOI:10.1145/2426656

General Chair:
Rasit Eskicioglu
University of Manitoba
,
Program Chairs:
Andrew Campbell
Dartmouth College
,
Koen Langendoen
Delft University of Technology

Copyright © 2012 ACM.

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Published: 06 November 2012

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SenSys '12

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SenSys '12: The 10th ACM Conference on Embedded Network Sensor Systems

November 6 - 9, 2012

Ontario, Toronto, Canada

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Overall Acceptance Rate 198 of 990 submissions, 20%

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Tran ALuong THuynh V(2024)A comprehensive survey and taxonomy on privacy-preserving deep learningNeurocomputing10.1016/j.neucom.2024.127345576(127345)Online publication date: Apr-2024
https://doi.org/10.1016/j.neucom.2024.127345
Yang YHu PShen JCheng HAn ZLiu X(2024)Privacy-preserving human activity sensing: A surveyHigh-Confidence Computing10.1016/j.hcc.2024.1002044:1(100204)Online publication date: Mar-2024
https://doi.org/10.1016/j.hcc.2024.100204
Al MYagli SKung S(2023)Privacy Enhancing Machine Learning via Removal of Unwanted DependenciesIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.311083134:6(3019-3033)Online publication date: Jun-2023
https://doi.org/10.1109/TNNLS.2021.3110831
Mirabi MNikiel RBinnig C(2023)SafeML: A Privacy-Preserving Byzantine-Robust Framework for Distributed Machine Learning Training2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00033(207-216)Online publication date: 4-Dec-2023
https://doi.org/10.1109/ICDMW60847.2023.00033
Jiang LSong QTan RLi MGummeson JLee SGao JXing G(2022)PriMaskProceedings of the 20th ACM Conference on Embedded Networked Sensor Systems10.1145/3560905.3568531(164-178)Online publication date: 6-Nov-2022
https://dl.acm.org/doi/10.1145/3560905.3568531
Yogi MChakravarthy A(2022)Application of Novel AI Mechanism for Minimizing Private Data Release in Cyber‐Physical SystemsA Roadmap for Enabling Industry 4.0 by Artificial Intelligence10.1002/9781119905141.ch8(127-139)Online publication date: 30-Nov-2022
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Civitarese GYe JZampatti MBettini C(2021)Collaborative activity recognition with heterogeneous activity sets and privacy preferencesJournal of Ambient Intelligence and Smart Environments10.3233/AIS-21001813:6(433-452)Online publication date: 1-Jan-2021
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Meurisch CMühlhäuser M(2021)Data Protection in AI ServicesACM Computing Surveys10.1145/344075454:2(1-38)Online publication date: 5-Mar-2021
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Yang YChen XTan RXiao Y(2021)Lightweight Privacy‐Preserving Learning Schemes*Intelligent IoT for the Digital World10.1002/9781119593584.ch5(197-237)Online publication date: 16-Apr-2021
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