Advertisement

PPLDEM: A Fast Anomaly Detection Algorithm with Privacy Preserving

  • Ao Yin
  • Chunkai Zhang
  • Zoe L. Jiang
  • Yulin Wu
  • Xing Zhang
  • Keli Zhang
  • Xuan Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11337)

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.

Keywords

Anomaly detection Local density Privacy preserving 

Notes

Acknowledgment

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

References

  1. 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_11CrossRefGoogle Scholar
  2. 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_3CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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_24CrossRefGoogle Scholar
  5. 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. 6.
    ElGamal, T.: A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE Trans. Inf. Theor. 31(4), 469–472 (1985)MathSciNetCrossRefGoogle Scholar
  7. 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_23CrossRefGoogle Scholar
  8. 8.
    He, Z., Xu, X., Deng, S.: Discovering cluster-based local outliers. Pattern Recogn. Lett. 24(9–10), 1641–1650 (2003)CrossRefGoogle Scholar
  9. 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_27CrossRefGoogle Scholar
  10. 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. 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. 12.
    Kriegel, H.P., S Hubert, M., Zimek, A.: Angle-based outlier detection in high-dimensional data, pp. 444–452 (2008). Dbs.ifi.lmu.deGoogle Scholar
  13. 13.
    Li, L., Huang, L., Yang, W., Yao, X., Liu, A.: Privacy-preserving LOF outlier detection. Knowl. Inf. Syst. 42(3), 579–597 (2015)CrossRefGoogle Scholar
  14. 14.
    Lin, X., Clifton, C., Zhu, M.: Privacy-preserving clustering with distributed EM mixture modeling. Knowl. Inf. Syst. 8(1), 68–81 (2005)CrossRefGoogle Scholar
  15. 15.
    Liu, F.T., Kai, M.T., Zhou, Z.H.: Isolation-based anomaly detection. ACM Trans. Knowl. Discov. Data 6(1), 1–39 (2012)CrossRefGoogle Scholar
  16. 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. 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)CrossRefGoogle Scholar
  18. 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_38CrossRefGoogle Scholar
  19. 19.
    Peter, A., Tews, E., Katzenbeisser, S.: Efficiently outsourcing multiparty computation under multiple keys. IEEE Trans. Inf. Forensics Secur. 8(12), 2046–2058 (2013)CrossRefGoogle Scholar
  20. 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. 21.
    Tang, B., He, H.: A local density-based approach for outlier detection. Neurocomputing 241, 171–180 (2017)CrossRefGoogle Scholar
  22. 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. 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. 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_2CrossRefGoogle Scholar
  25. 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_20CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyHarbin Institute of TechnologyShenzhenChina
  2. 2.National Engineering Laboratory for Big Data Collaborative Security TechnologyBeijingChina

Personalised recommendations