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PPLDEM: A Fast Anomaly Detection Algorithm with Privacy Preserving

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Algorithms and Architectures for Parallel Processing (ICA3PP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11337))

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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.

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Notes

  1. 1.

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

  2. 2.

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

References

  1. 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_11

    Chapter  Google Scholar 

  2. 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_3

    Chapter  Google Scholar 

  3. 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)

    Article  Google Scholar 

  4. 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_24

    Chapter  Google Scholar 

  5. 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)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  7. 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_23

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  9. 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_27

    Chapter  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

  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_38

    Chapter  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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_2

    Chapter  Google Scholar 

  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_20

    Chapter  Google Scholar 

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Acknowledgment

This study was supported by the Shenzhen Research Council (Grant No. JSGG20170822160842949, JCYJ20170307151518535).

Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China

    Ao Yin, Chunkai Zhang, Zoe L. Jiang, Yulin Wu & Xuan Wang

  2. National Engineering Laboratory for Big Data Collaborative Security Technology, Beijing, China

    Xing Zhang & Keli Zhang

Authors
  1. Ao Yin
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  2. Chunkai Zhang
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  3. Zoe L. Jiang
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  4. Yulin Wu
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  5. Xing Zhang
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  6. Keli Zhang
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  7. Xuan Wang
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Corresponding authors

Correspondence to Chunkai Zhang or Zoe L. Jiang .

Editor information

Editors and Affiliations

  1. Rutgers University, Newark, NJ, USA

    Jaideep Vaidya

  2. Guangzhou University, Guangzhou, China

    Jin Li

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Yin, A. et al. (2018). PPLDEM: A Fast Anomaly Detection Algorithm with Privacy Preserving. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11337. Springer, Cham. https://doi.org/10.1007/978-3-030-05063-4_28

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  • DOI: https://doi.org/10.1007/978-3-030-05063-4_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05062-7

  • Online ISBN: 978-3-030-05063-4

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