Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Peer-to-Peer Networking and Applications
Erschienen in: Peer-to-Peer Networking and Applications 5/2016

01.09.2016

A semi-supervised privacy-preserving clustering algorithm for healthcare

verfasst von: Meiyu Huang, Yiqiang Chen, Bo-Wei Chen, Junfa Liu, Seungmin Rho, Wen Ji

Erschienen in: Peer-to-Peer Networking and Applications | Ausgabe 5/2016

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

With the proliferation of healthcare data, the cloud mining technology for E-health services and applications has become a hot research topic. While on the other hand, these rapidly evolving cloud mining technologies and their deployment in healthcare systems also pose potential threats to patient’s data privacy. In order to solve the privacy problem in the cloud mining technique, this paper proposes a semi-supervised privacy-preserving clustering algorithm. By employing a small amount of supervised information, the method first learns a Large Margin Nearest Cluster metric using convex optimization. Then according to the trained metric, the method imposes multiplicative perturbation on the original data, which can change the distribution shape of the original data and thus protect the privacy information as well as ensuring high data usability. The experimental results on the brain fiber dataset provided by the 2009 PBC demonstrated that the proposed method could not only protect data privacy towards secure attacks, but improve the clustering purity.
Vorheriger Artikel Efficient data integrity auditing for storage security in mobile health cloud
Nächster Artikel Providing security and fault tolerance in P2P connections between clouds for mHealth services

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Wang L, Alexander CA (2014) Telemedicine based on mobile devices and mobile cloud computing. Int J Cloud Comput Serv Sci 3(1):26–36
2.
Sultan N (2014) Making use of cloud computing for healthcare provision: opportunities and challenges. Int J Inf Manag 34(2):177–184CrossRef
3.
Uniyal D, Raychoudhury V (2014) Pervasive healthcare-a comprehensive survey of tools and techniques. Clin Orthop Relat Res
4.
Jeong S, Kim Y-W, Youn C-H (2014) Personalized healthcare system for chronic disease care in cloud environment. J Electron Telecommunications Res Inst 36(5):730–740
5.
Meyer J, Boll S (2014) Digital health devices for everyone! IEEE Pervasive Comput 13(2):10–13CrossRef
6.
Min J-K, Doryab A, Wiese J, Amini S, Zimmerman J, Hong JI (2014) Toss’n’turn: smartphone as sleep and sleep quality detector. In Proceedings of the 32nd annual ACM conference on human factors in computing systems. ACM 477–486
7.
Banu PN, Andrews S (2015) Performance analysis of hard and soft clustering approaches for gene expression data. Int J Rough Sets Data Anal 2(1):58–69CrossRef
8.
Yuan B, Herbert J (2014) Context-aware hybrid reasoning framework for pervasive health- care. Pers Ubiquit Comput 18(4):865–881CrossRef
9.
Theoharidou M, Tsalis N, Gritzalis D (2014) Smart home solutions for healthcare: privacy in ubiquitous computing infrastructures. Handbook of smart homes, health care and well-being
10.
Avancha S, Baxi A, Kotz D (2012) Privacy in mobile technology for personal healthcare. ACM Comput Surv 45(1):3CrossRef
11.
Verykios VS, Bertino E, Fovino IN, Provenza LP, Saygin Y, Theodoridis Y (2004) State-of-the-art in privacy preserving data mining. ACM Sigmod Rec 33(1):50–57CrossRef
12.
Chhinkaniwala H, Garg S (2014) Privacy preserving data mining-issues & techniques: preserving privacy of data streams and large data sets while mining. Scholars Press
13.
Wang B, Yang J (2011) The state of the art and tendency of privacy preserving data mining. In International Conference on E-Business and E-Government. IEEE 1–3
14.
Keyvanpour MR, Moradi SS (2014) A perturbation method based on singular value decomposition and feature selection for privacy preserving data mining. Int J Data Warehouse Min 10(1):55–76CrossRef
15.
Liu K, Kargupta H, Ryan J (2006) Random projection-based multiplicative data pertur- bation for privacy preserving distributed data mining. IEEE Trans Knowl Data Eng 18(1):92–106CrossRef
16.
Saygin Y, Verykios VS, Elmagarmid AK (2002) Privacy preserving association rule mining. In Proceedings of twelfth international workshop on research issues in data engineering: engineering e-commerce/e-business systems. IEEE 151–158
17.
Fienberg SE, McIntyre J (2005) Data swapping: variations on a theme by dalenius and reiss. J Off Stat 21(2):309
18.
Oliveira SR, Za¨ıane OR (2004) Achieving privacy preservation when sharing data for clustering. In secure data management. Springer 67–82
19.
20.
Han J, Kamber M (2006) Data mining, Southeast Asia edition: concepts and techniques. Morgan kaufmann
21.
Tomar D, Agarwal S (2013) A survey on data mining approaches for healthcare. Int J Bio Sci Bio Technol 5(5):241–266CrossRef
22.
Kumar V, Park H, Basole RC, Braunstein M, Kahng M, Chau DH, Tamersoy A, Hirsh DA, Serban N, Bost J et al (2014) Exploring clinical care processes using visual and data analytics: challenges and opportunities. In Proceedings of the 20th ACM SIGKDD conference on knowledge discovery and data mining workshop on data science for social good
23.
Hartigan JA, Wong MA (1979) Algorithm as 136: a k-means clustering algorithm. Appl Stat 100–108
24.
Huang Z (1998) Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min Knowl Disc 2(3):283–304CrossRef
25.
Ball GH, Hall DJ (1965) Isodata, a novel method of data analysis and pattern classification. DTIC document. Technol Rep
26.
Kaushik K, Kapoor D, Varadharajan V, Nallusamy R (2014) Disease management: clustering–based disease prediction. Int J Collab Enterp 4(1):69–82CrossRef
27.
Vesanto J, Alhoniemi E (2000) Clustering of the self-organizing map. IEEE Trans Neural Netw 11(3):586–600CrossRef
28.
Hajihashemi Z, Yefimova M, Popescu M (2014) Detecting daily routines of older adults using sensor time series clustering. In Proceedings of the 36th annual IEEE international conference on engineering in medicine and biology society. IEEE 5912–5915
29.
Fahad LG, Tahir SF, Rajarajan M (2014) Activity recognition in smart homes using clustering based classification. In Proceedings of the 22nd international conference on pattern recognition. IEEE 1348–1353
30.
31.
Haraty RA, Dimishkieh M, Masud M (2015) An enhanced k-means clustering algorithm for pattern discovery in healthcare data. Int J Distrib Sens Netw
32.
Wang X, Chen M, Kwon TT, Yang L, Leung V (2013) Ames-cloud: a framework of adaptive mobile video streaming and efficient social video sharing in the clouds. IEEE Trans Multimed 15(4):811–820CrossRef
33.
Wan J, Ullah S, Lai C-F, Zhou M, Wang X et al (2013) Cloud-enabled wireless body area networks for pervasive healthcare. IEEE Netw 27(5):56–61CrossRef
34.
Raij A, Ghosh A, Kumar S, Srivastava M (2011) Privacy risks emerging from the adoption of innocuous wearable sensors in the mobile environment. In Proceedings of the SIGCHI conference on human factors in computing systems, ser. CHI ’11. New York, NY, USA. ACM 11–20
35.
Du W, Zhan Z (2003) Using randomized response techniques for privacy-preserving data mining. In Proceedings of the ninth ACM SIGKDD international conference on knowl- edge discovery and data mining. ACM 505–510
36.
Kalaivani R, Chidambaram S (2014) Additive gaussian noise based data perturbation in multi-level trust privacy preserving data mining. Int J Data Min Knowl Manag Process 4(3):21–29CrossRef
37.
Wieland SC, Cassa CA, Mandl KD, Berger B (2008) Revealing the spatial distribution of a disease while preserving privacy. Proc Natl Acad Sci 105(46):17608–17613CrossRef
38.
Elmisery AM, Fu H (2010) Privacy preserving distributed learning clustering of healthcare data using cryptography protocols. In Proceeedings of the 34th Annual IEEE Conference on Computer Software and Applications Workshops. IEEE 140–145
39.
Williams J (2010) Social networking applications in health care: threats to the privacy and security of health information. In Proceedings of the International Conference on Software Engeneering Workshop on Software Engineering in Health Care. ACM 39–49
40.
Allab K, Benabdeslem K (2011) Constraint selection for semi-supervised topological clustering. In machine learning and knowledge discovery in databases. Springer 28–43
41.
Lange T, Law MH, Jain AK, Buhmann JM (2005) Learning with constrained and unlabelled data. Proc IEEE Comput Soc Conf Comput Vis Pattern Recogn 1:731–738
42.
Bekkerman R, Sahami M (2006) Semi-supervised clustering using combinatorial mrfs. In Proceedings of IEEE international conference of machine learning workshop on learn- ing in structured output spaces
43.
Yang L, Jin R (2006) Distance metric learning: a comprehensive survey. Michigan State Univ 2
44.
Guillaumin M, Verbeek J, Schmid C (2010) Multiple instance metric learning from auto- matically labeled bags of faces. In Europeon Conference on Computer Vision. Springer 634–647
45.
Klein D, Kamvar SD, Manning CD (2002) From instance-level constraints to space- level constraints: making the most of prior knowledge in data clustering. In Proceedings of the Nineteenth International Conference on Machine Learning. Morgan Kaufmann Publishers Inc. 307–314
46.
Cohn D, Caruana R, McCallum A (2003) Semi-supervised clustering with user feedback. Constrained Cluster Adv AlgorithmsTheory Appl 4(1):17–32MathSciNetMATH
47.
Wu L, Hoi SC, Jin R, Zhu J, Yu N (2012) Learning bregman distance functions for semi-supervised clustering. IEEE Trans Knowl Data Eng 24(3):478–491CrossRef
48.
Domeniconi C, Peng J, Yan B (2011) Composite kernels for semi-supervised clustering. Knowl Inf Syst 28(1):99–116CrossRef
49.
Chen Y, Rege M, Dong M, Hua J (2007) Incorporating user provided constraints into document clustering. In Proceedings of the Seventh IEEE International Conference on Data Mining. IEEE 103–112
50.
Baghshah MS, Shouraki SB (2010) Kernel-based metric learning for semi-supervised clustering. Neurocomputing 73(7):1352–1361CrossRefMATH
51.
Hoi SC, Jin R, Lyu MR (2007) Learning nonparametric kernel matrices from pairwise constraints. In Proceedings of the 24th International Conference on Machine Learning. ACM 361–368
52.
Bar-Hillel A, Hertz T, Shental N, Weinshall D (2003) Learning distance functions using equivalence relations. Proc Tenth Int Conf Mach Learn 3:11–18MATH
53.
Xing EP, Jordan MI, Russell S, Ng AY (2002) Distance metric learning with appli- cation to clustering with side-information. In advances in neural information processing systems. 505–512
54.
Weinberger KQ, Blitzer J, Saul LK (2005) Distance metric learning for large margin nearest neighbor classification. In Advances in neural information processing systems. 1473–1480
55.
56.
57.
Golub GH, Van Loan CF (2012) Matrix computations. JHU Press 3
Metadaten
Titel
A semi-supervised privacy-preserving clustering algorithm for healthcare
verfasst von
Meiyu Huang
Yiqiang Chen
Bo-Wei Chen
Junfa Liu
Seungmin Rho
Wen Ji
Publikationsdatum
01.09.2016
Verlag
Springer US
Erschienen in
Peer-to-Peer Networking and Applications / Ausgabe 5/2016
Print ISSN: 1936-6442
Elektronische ISSN: 1936-6450
DOI
https://doi.org/10.1007/s12083-015-0356-9

Weitere Artikel der Ausgabe 5/2016

Peer-to-Peer Networking and Applications 5/2016 Zur Ausgabe

OriginalPaper

Providing security and fault tolerance in P2P connections between clouds for mHealth services

EditorialNotes

Guest editorial: Secure cloud computing for mobile health services

OriginalPaper

Underlaid-D2D-assisted cooperative multicast based on social networks

OriginalPaper

Device-to-device resource allocation in LTE-advanced networks by hybrid particle swarm optimization and genetic algorithm

OriginalPaper

Novel power control and collision resolution schemes for device-to-device discovery

OriginalPaper

Cryptanalysis and improvement of ‘a secure authentication scheme for telecare medical information system’ with nonce verification