Abstract
Data mining has become very popular with the arrival of big data era, but it also raises privacy issues. Negative database (NDB) is a new type of data representation which stores the negative image of data and can protect privacy while supporting some basic data mining operations such as classification and clustering. However, the existing clustering algorithm upon NDBs is based on Hamming distance, when facing datasets which have many categories for each attribute, the encoded data will become very long and resulting in low computational efficiency. In this paper, we propose a privacy-preserving k-means clustering algorithm based on Euclidean distance upon NDBs. The main step of k-means algorithm is to calculate the distance between each record and cluster centers, in order to solve the problem of privacy disclosure in this step, we transform each record in database into an NDB and propose a method to estimate Euclidean distance from a binary string and an NDB. Our work opens up new ideas for data mining upon negative database.
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References
Bao, Y., Luo, W., Zhang, X.: Estimating positive surveys from negative surveys. Stat. Prob. Lett. 83(2), 551–558 (2013)
Bringer, J., Chabanne, H.: Negative databases for biometric data. In: Proceedings of the 12th ACM Workshop on Multimedia and Security, pp. 55–62. ACM (2010)
Chen, K., Liu, L.: Geometric data perturbation for privacy preserving outsourced data mining. Knowl. Inf. Syst. 29(3), 657–695 (2011)
Dasgupta, D., Azeem, R.: An investigation of negative authentication systems. In: Proceedings of 3rd International Conference on Information Warfare and Security, pp. 117–126 (2008)
Dasgupta, D., Roy, A., Nag, A.: Negative authentication systems. Advances in User Authentication. ISFS, pp. 85–145. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58808-7_3
Dasgupta, D., Saha, S.: A biologically inspired password authentication system. In: Proceedings of the 5th Annual Workshop on Cyber Security and Information Intelligence Research: Cyber Security and Information Intelligence Challenges and Strategies, p. 41. ACM (2009)
Dasgupta, D., Saha, S.: Password security through negative filtering. In: 2010 International Conference on Emerging Security Technologies (EST), pp. 83–89. IEEE (2010)
Dheeru, D., Karra Taniskidou, E.: UCI machine learning repository (2017). http://archive.ics.uci.edu/ml. Accessed 27 Aug 2018
Dhiraj, S.S., Khan, A.M.A., Khan, W., Challagalla, A.: Privacy preservation in k-means clustering by cluster rotation. In: TENCON 2009–2009 IEEE Region 10 Conference, pp. 1–7. IEEE (2009)
Esponda, F.: Everything that is not important: negative databases [research frontier]. IEEE Comput. Intell. Mag. 3(2), 60–63 (2008)
Esponda, F.: Negative surveys. arXiv preprint. arXiv: math/0608176 (2006)
Esponda, F.: Hiding a needle in a haystack using negative databases. In: Solanki, K., Sullivan, K., Madhow, U. (eds.) IH 2008. LNCS, vol. 5284, pp. 15–29. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88961-8_2
Esponda, F., Ackley, E.S., Helman, P., Jia, H., Forrest, S.: Protecting data privacy through hard-to-reverse negative databases. Int. J. Inf. Secur. 6(6), 403–415 (2007)
Esponda, F., Forrest, S., Helman, P.: Enhancing privacy through negative representations of data. Technical report, Department of Computer Science, University of New Mexico (2004)
Esponda, F., Trias, E.D., Ackley, E.S., Forrest, S.: A relational algebra for negative databases. University of New Mexico, Technical report (2007)
Ferris, B., Froehlich, J.: WalkSAT as an informed heuristic to DPLL in sat solving. Technical report, CSE 573: Artificial Intelligence (2004)
Hartigan, J.A., Wong, M.A.: Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 100–108 (1979)
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, pp. 593–599. ACM (2005)
Jia, H., Moore, C., Strain, D.: Generating hard satisfiable formulas by hiding solutions deceptiveily. In: National Conference on Artificial Intelligence, pp. 384–389 (2005)
Lin, K.P.: Privacy-preserving kernel k-means clustering outsourcing with random transformation. Knowl. Inf. Syst. 49(3), 885–908 (2016)
Liu, R., Luo, W., Wang, X.: A hybrid of the prefix algorithm and the q-hidden algorithm for generating single negative databases. In: 2011 IEEE Symposium on Computational Intelligence in Cyber Security (CICS), pp. 31–38. IEEE (2011)
Liu, R., Luo, W., Yue, L.: Classifying and clustering in negative databases. Front. Comput. Sci. 7(6), 864–874 (2013)
Liu, R., Luo, W., Yue, L.: The p-hidden algorithm: hiding single databases more deeply. Immune Comput. 2(1), 43–55 (2014)
Mahajan, Y.S., Fu, Z., Malik, S.: Zchaff2004: an efficient SAT solver. In: Hoos, H.H., Mitchell, D.G. (eds.) SAT 2004. LNCS, vol. 3542, pp. 360–375. Springer, Heidelberg (2005). https://doi.org/10.1007/11527695_27
Oliveira, S., Zaiane, O.: Data perturbation by rotation for privacy-preserving clustering. Technical report TR04-17 (2004)
Patel, S., Patel, V., Jinwala, D.: Privacy preserving distributed k-means clustering in malicious model using zero knowledge proof. In: Hota, C., Srimani, P.K. (eds.) ICDCIT 2013. LNCS, vol. 7753, pp. 420–431. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36071-8_33
Pipatsrisawat, K., Darwiche, A.: On the power of clause-learning SAT solvers with restarts. In: Gent, I.P. (ed.) CP 2009. LNCS, vol. 5732, pp. 654–668. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04244-7_51
Selman, B., Kautz, H.A., Cohen, B.: Noise strategies for improving local search. In: AAAI, vol. 94, pp. 337–343 (1994)
Vaidya, J., Clifton, C.: Privacy-preserving k-means clustering over vertically partitioned data. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 206–215. ACM (2003)
Zhao, D., Luo, W., Liu, R., Yue, L.: A fine-grained algorithm for generating hard-toreverse negative databases. In: 2015 International Workshop on Artificial Immune Systems (AIS), pp. 1–8 (2015)
Zhao, D., Luo, W., Liu, R., Yue, L.: Negative iris recognition. IEEE Trans. Dependable Secure Comput. 15(1), 112–125 (2018)
Acknowledgement
This work was partially supported by the National Natural Science Foundation of China (Grant No. 61806151, 61672398, 61702387), the Hubei Provincial Natural Science Foundation of China (Grant No. 2017CFA012, 2017CFB302), the Key Technical Innovation Project of Hubei (Grant No. 2017AAA122), Provincial Science & Technology International Cooperation Program of Hubei (Grant No. 2017AHB048), the Applied Fundamental Research of Wuhan (Grant No. 20160101010004), and the Open Fund of Hubei Key Lab. of Transportation of IoT (Grant No. 2017III28-004).
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Hu, X. et al. (2018). Privacy-Preserving K-Means Clustering Upon Negative Databases. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11304. Springer, Cham. https://doi.org/10.1007/978-3-030-04212-7_17
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