Top

Published in:

2019 | OriginalPaper | Chapter

Privacy Preserving Collaborative Agglomerative Hierarchical Clustering Construction

Authors : Mina Sheikhalishahi, Mona Hamidi, Fabio Martinelli

Published in: Information Systems Security and Privacy

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Sharing information brought by governments, companies, and individuals, has created fabulous opportunities for knowledge-based decision making. However, the main challenge in collaborative data analysis returns back to the privacy of sensitive data. In current study, we propose a general framework which can be exploited as a secure tool for constructing any agglomerative hierarchical clustering algorithm over partitioned data. We assume that data is distributed between two (or more) parties either horizontally or vertically, such that for mutual benefits the participated parties are interested in obtaining the clusters’ structure on whole data, but for privacy concerns, they are not willing to share the original datasets. To this end, in this study, we propose general algorithms based on secure scalar product and secure hamming distance to securely compute the desired criteria for shaping the clusters’ scheme. Our proposed approach covers the private construction of all possible agglomerative hierarchical clustering algorithms on distributed datasets, including both numerical and categorical data.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now

previous chapter Survey and Guidelines for the Design and Deployment of a Cyber Security Label for SMEs

next chapter Insights into Unsupervised Holiday Detection from Low-Resolution Smart Metering Data

http://hdl.handle.net/10071/4097.

Artoisenet, C., Roland, M., Closon, M.: Health networks: actors, professional relationships, and controversies. In: Collaborative Patient Centred eHealth, vol. 141. IOSPress (2013)

Berkhin, P.: A survey of clustering data mining techniques. In: Kogan, J., Nicholas, C., Teboulle, M. (eds.) Grouping Multidimensional Data, pp. 25–71. Springer, Berlin Heidelberg (2006). https://doi.org/10.1007/3-540-28349-8_2CrossRef

Bogan, E., English, J.: Benchmarking for Best Practices: Winning Through Innovative Adaptation. McGraw-Hill, New York (1994)

Bringer, J., Chabanne, H., Favre, M., Patey, A., Schneider, T., Zohner, M.: GSHADE: faster privacy-preserving distance computation and biometric identification. In: Proceedings of the 2nd ACM Workshop on Information Hiding and Multimedia Security, New York, NY, USA, pp. 187–198 (2014)

Bringer, J., Chabanne, H., Patey, A.: SHADE: secure hamming distance computation from oblivious transfer. In: Adams, A.A., Brenner, M., Smith, M. (eds.) FC 2013. LNCS, vol. 7862, pp. 164–176. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41320-9_11CrossRef

Bunn, P., Ostrovsky, R.: Secure two-party k-means clustering. In: Proceedings of the 14th ACM Conference on Computer and Communications Security, CCS 2007, pp. 486–497. ACM, NY, USA (2007)

Day, W.H.E., Edelsbrunner, H.: Efficient algorithms for agglomerative hierarchical clustering methods. J. Classif. 1(1), 7–24 (1984)CrossRef

De, I., Tripathy, A.: A secure two party hierarchical clustering approach for vertically partitioned data set with accuracy measure. In: Thampi, S., Abraham, A., Pal, S., Rodriguez, J. (eds.) Recent Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, vol. 235. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-01778-5_16CrossRef

Erkin, Z., Franz, M., Guajardo, J., Katzenbeisser, S., Lagendijk, I., Toft, T.: Privacy-preserving face recognition. In: Goldberg, I., Atallah, M.J. (eds.) PETS 2009. LNCS, vol. 5672, pp. 235–253. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03168-7_14CrossRef

10.

Gan, G., Ma, C., Wu, J.: Data Clustering: Theory, Algorithms, and Applications. ASA-SIAM Series on Statistics and Applied Probability. Society for Industrial and Applied Mathematics, Philadelphia (2007)CrossRef

11.

Hamidi, M., Sheikhalishahi, M., Martinelli, F.: Secure two-party agglomerative hierarchical clustering construction. In: Proceedings of the 4th International Conference on Information Systems Security and Privacy, ICISSP 2018, Funchal, Madeira, Portugal, 22–24 January 2018, pp. 432–437 (2018)

12.

Hamidi, M., Sheikhalishahi, M., Martinelli, F.: Secure two-party agglomerative hierarchical clustering construction. In: the 4th International Conference on Information Systems Security and Privacy (ICISSP). SciTePress (2018)

13.

Jagannathan, G., Pillaipakkamnatt, K., Wright, R.N.: A new privacy-preserving distributed k-clustering algorithm. In: SDM, pp. 494–498. SIAM (2006)

14.

Jagannathan, G., Wright, R.N.: Privacy-preserving distributed k-means clustering over arbitrarily partitioned data. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, KDD 2005, pp. 593–599. ACM, New York, NY, USA (2005)

15.

Jha, S., Kruger, L., McDaniel, P.: Privacy preserving clustering. In: di Vimercati, S.C., Syverson, P., Gollmann, D. (eds.) ESORICS 2005. LNCS, vol. 3679, pp. 397–417. Springer, Heidelberg (2005). https://doi.org/10.1007/11555827_23CrossRef

16.

Martinelli, F., Saracino, A., Sheikhalishahi, M.: Modeling privacy aware information sharing systems: a formal and general approach. In: 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (2016)

17.

Sheikhalishahi, M., Martinelli, F.: Privacy preserving hierarchical clustering over multi-party data distribution. In: Wang, G., Atiquzzaman, M., Yan, Z., Choo, K.-K.R. (eds.) SpaCCS 2017. LNCS, vol. 10656, pp. 530–544. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-72389-1_42CrossRef

18.

Mohammed, N., Chen, R., Fung, B.C., Yu, P.S.: Differentially private data release for data mining. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011, pp. 493–501, ACM, New York, NY, USA (2011)

19.

Murtagh, F., Contreras, P.: Algorithms for hierarchical clustering: an overview. Wiley Interdisc. Rew. Data Min. Knowl. Discov. 2(1), 86–97 (2012)CrossRef

20.

Nateghizad, M., Erkin, Z., Lagendijk, R.L.: Efficient and secure equality tests. In: 2016 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6 (2016)

21.

Nateghizad, M., Erkin, Z., Lagendijk, R.L.: An efficient privacy-preserving comparison protocol in smart metering systems. EURASIP J. Inf. Secur. 2016(1), 11 (2016)CrossRef

22.

Oliveira, S.R.M., Zaïane, O.R.: Privacy preserving frequent itemset mining. In: Proceedings of the IEEE International Conference on Privacy, Security and Data Mining, CRPIT 2014, vol. 14, pp. 43–54 (2002)

23.

Oliveira, S.R.M., Zaiane, O.R.: A privacy-preserving clustering approach toward secure and effective data analysis for business collaboration. Comput. Secur. 26(1), 81–93 (2007)CrossRef

24.

Sheikhalishahi, M., Martinelli, F.: Privacy preserving clustering over horizontal and vertical partitioned data. In: 2017 IEEE Symposium on Computers and Communications, ISCC 2017, Heraklion, Greece, 3–6 July 2017, pp. 1237–1244 (2017)

25.

Sheikhalishahi, M., Martinelli, F.: Privacy-utility feature selection as a privacy mechanism in collaborative data classification. In: The 26th IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises, Poznan, Poland (2017)

26.

Sheikhalishahi, M., Mejri, M., Tawbi, N., Martinelli, F.: Privacy-aware data sharing in a tree-based categorical clustering algorithm. In: Cuppens, F., Wang, L., Cuppens-Boulahia, N., Tawbi, N., Garcia-Alfaro, J. (eds.) FPS 2016. LNCS, vol. 10128, pp. 161–178. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51966-1_11CrossRef

27.

Su, C., Zhou, J., Bao, F., Takagi, T., Sakurai, K.: Two-party privacy-preserving agglomerative document clustering. In: Dawson, E., Wong, D.S. (eds.) ISPEC 2007. LNCS, vol. 4464, pp. 193–208. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72163-5_16CrossRef

28.

Tan, S.C., San Lau, J.P.: Time series clustering: A superior alternative for market basket analysis. In: Herawan, T., Deris, M., Abawajy, J. (eds.) DaEng-2013. LNEE, vol. 285, pp. 241–248. Springer, Singapore (2014). https://doi.org/10.1007/978-981-4585-18-7_28CrossRef

29.

Vaidya, J., Clifton, C.: Privacy preserving association rule mining in vertically partitioned data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 639–644. ACM, New York, NY, USA (2002)

Title: Privacy Preserving Collaborative Agglomerative Hierarchical Clustering Construction
Authors: Mina Sheikhalishahi
Mona Hamidi
Fabio Martinelli
Publisher: Springer International Publishing
Book: Information Systems Security and Privacy
Print ISBN: 978-3-030-25108-6

Electronic ISBN: 978-3-030-25109-3

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-25109-3_14

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner