Top

Published in:

2018 | OriginalPaper | Chapter

PPLDEM: A Fast Anomaly Detection Algorithm with Privacy Preserving

Authors : Ao Yin, Chunkai Zhang, Zoe L. Jiang, Yulin Wu, Xing Zhang, Keli Zhang, Xuan Wang

Published in: Algorithms and Architectures for Parallel Processing

Publisher: Springer International Publishing

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

search-config

AI-assisted search

Off

Abstract

In this paper, we first propose a fast anomaly detection algorithm LDEM. The key insight of LDEM is a fast local density estimator, which estimates the local density of instances by the average density of all features. The local density of each feature can be estimated by the defined mapping function. Furthermore, we propose an efficient scheme PPLDEM to detect anomaly instances with considering privacy protection in the case of multi-party participation, based on the proposed scheme and homomorphic encryption. Compare with existing schemes with privacy preserving, our scheme needs less communication cost and less calculation. From security analysis, it can prove that our scheme will not leak any privacy information of participants. And experiments results show that our proposed scheme PPLDEM can detect anomaly instances effectively and efficiently.

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 VISU: A Simple and Efficient Cache Coherence Protocol Based on Self-updating

next chapter Towards Secure Cloud Data Similarity Retrieval: Privacy Preserving Near-Duplicate Image Data Detection

https://en.wikipedia.org/wiki/Receiver_operating_characteristic.

http://archive.ics.uci.edu/ml/datasets.html.

Bendlin, R., Damgård, I., Orlandi, C., Zakarias, S.: Semi-homomorphic encryption and multiparty computation. In: Paterson, K.G. (ed.) EUROCRYPT 2011. LNCS, vol. 6632, pp. 169–188. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20465-4_11CrossRef

Bresson, E., Catalano, D., Pointcheval, D.: A simple public-key cryptosystem with a double trapdoor decryption mechanism and its applications. In: Laih, C.-S. (ed.) ASIACRYPT 2003. LNCS, vol. 2894, pp. 37–54. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-40061-5_3CrossRef

Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers, vol. 29, no. 2, pp. 93–104 (2000)CrossRef

Chen, Z., Fu, A.W.-C., Tang, J.: On complementarity of cluster and outlier detection schemes. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds.) DaWaK 2003. LNCS, vol. 2737, pp. 234–243. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-45228-7_24CrossRef

Duan, L., Xiong, D., Lee, J., Guo, F.: A local density based spatial clustering algorithm with noise. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 978–986 (2007)

ElGamal, T.: A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE Trans. Inf. Theor. 31(4), 469–472 (1985)MathSciNetCrossRef

Gao, J., Hu, W., Zhang, Z.M., Zhang, X., Wu, O.: RKOF: robust kernel-based local outlier detection. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011. LNCS (LNAI), vol. 6635, pp. 270–283. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20847-8_23CrossRef

He, Z., Xu, X., Deng, S.: Discovering cluster-based local outliers. Pattern Recogn. Lett. 24(9–10), 1641–1650 (2003)CrossRef

Kantarcıoǧlu, M., Clifton, C.: Privately computing a distributed \(k\)-nn classifier. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 279–290. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30116-5_27CrossRef

10.

Keller, F., Muller, E., Bohm, K.: HiCS: high contrast subspaces for density-based outlier ranking. In: IEEE International Conference on Data Engineering, pp. 1037–1048 (2012)

11.

Knorr, E.M., Ng, R.T.: Algorithms for mining distance-based outliers in large datasets. In: International Conference on Very Large Data Bases, pp. 392–403 (1998)

12.

Kriegel, H.P., S Hubert, M., Zimek, A.: Angle-based outlier detection in high-dimensional data, pp. 444–452 (2008). Dbs.ifi.lmu.de

13.

Li, L., Huang, L., Yang, W., Yao, X., Liu, A.: Privacy-preserving LOF outlier detection. Knowl. Inf. Syst. 42(3), 579–597 (2015)CrossRef

14.

Lin, X., Clifton, C., Zhu, M.: Privacy-preserving clustering with distributed EM mixture modeling. Knowl. Inf. Syst. 8(1), 68–81 (2005)CrossRef

15.

Liu, F.T., Kai, M.T., Zhou, Z.H.: Isolation-based anomaly detection. ACM Trans. Knowl. Discov. Data 6(1), 1–39 (2012)CrossRef

16.

Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 413–422. IEEE (2008)

17.

Liu, X., Deng, R.H., Choo, K.K.R., Weng, J.: An efficient privacy-preserving outsourced calculation toolkit with multiple keys. IEEE Trans. Inf. Forensics Secur. 11(11), 2401–2414 (2016)CrossRef

18.

Damgård, I., Pastro, V., Smart, N., Zakarias, S.: Multiparty computation from somewhat homomorphic encryption. In: Safavi-Naini, R., Canetti, R. (eds.) CRYPTO 2012. LNCS, vol. 7417, pp. 643–662. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32009-5_38CrossRef

19.

Peter, A., Tews, E., Katzenbeisser, S.: Efficiently outsourcing multiparty computation under multiple keys. IEEE Trans. Inf. Forensics Secur. 8(12), 2046–2058 (2013)CrossRef

20.

Sugiyama, M., Borgwardt, K.M.: Rapid distance-based outlier detection via sampling. In: Advances in Neural Information Processing Systems, pp. 467–475 (2013)

21.

Tang, B., He, H.: A local density-based approach for outlier detection. Neurocomputing 241, 171–180 (2017)CrossRef

22.

Wang, X., Wang, X.L., Wilkes, M.: A fast distance-based outlier detection technique. In: Poster and Workshop Proceedings of Industrial Conference Advances in Data Mining, ICDM 2008, Leipzig, Germany, 2008 July, pp. 25–44 (2008)

23.

Wu, K., Zhang, K., Fan, W., Edwards, A., Yu, P.S.: RS-forest: a rapid density estimator for streaming anomaly detection 2014, pp. 600–609 (2014)

24.

Zhang, C., Liu, H., Yin, A.: Research of detection algorithm for time series abnormal subsequence. In: Zou, B., Li, M., Wang, H., Song, X., Xie, W., Lu, Z. (eds.) ICPCSEE 2017. CCIS, vol. 727, pp. 12–26. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-6385-5_2CrossRef

25.

Zhang, C., Yin, A., Deng, Y., Tian, P., Wang, X., Dong, L.: A novel anomaly detection algorithm based on trident tree. In: Luo, M., Zhang, L.-J. (eds.) CLOUD 2018. LNCS, vol. 10967, pp. 295–306. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94295-7_20CrossRef

Title: PPLDEM: A Fast Anomaly Detection Algorithm with Privacy Preserving
Authors: Ao Yin
Chunkai Zhang
Zoe L. Jiang
Yulin Wu
Xing Zhang
Keli Zhang
Xuan Wang
Publisher: Springer International Publishing
Book: Algorithms and Architectures for Parallel Processing
Print ISBN: 978-3-030-05062-7

Electronic ISBN: 978-3-030-05063-4

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-05063-4_28

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