Abstract
We propose an efficient and secure frequent pattern mining protocol with fully homomorphic encryption (FHE). Nowadays, secure outsourcing of mining tasks to the cloud with FHE is gaining attentions. However, FHE execution leads to significant time and space complexities. P3CC, the first proposed secure protocol with FHE for frequent pattern mining, has these particular problems. It generates ciphertexts for each component in item-transaction data matrix, and executes numerous operations over the encrypted components. To address this issue, we propose efficient frequent pattern mining with ciphertext packing. By adopting the packing method, our scheme will require fewer ciphertexts and associated operations than P3CC, thus reducing both encryption and calculation times. We have also optimized its implementation by reusing previously produced results so as not to repeat calculations. Our experimental evaluation shows that the proposed scheme runs 430 times faster than P3CC, and uses 94.7 % less memory with 10,000 transactions data.
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References
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD record, vol. 22, pp. 207–216. ACM (1993)
Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of the 20th international conference on very large data bases, VLDB, vol. 1215, pp. 487–499 (1994)
Atzori, M., Bonchi, F., Giannotti, F., Pedreschi, D.: Anonymity preserving pattern discovery. VLDB J. 17(4), 703–727 (2008). Springer
Bhaskar, R., Laxman, S., Smith, A., Thakurta, A.: Discovering frequent patterns in sensitive data. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 503–512. ACM (2010)
Brakerski, Z., Gentry, C., Vaikuntanathan, V.: (leveled) fully homomorphic encryption without bootstrapping. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, pp. 309–325. ACM (2012)
Evfimievski, A., Gehrke, J., Srikant, R.: Limiting privacy breaches in privacy preserving data mining. In: Proceedings of the 22th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 211–222. ACM (2003)
Gellman, R.: Privacy in the clouds: risks to privacy and confidentiality from cloud computing. In: Proceedings of the World Privacy Forum, 23 February 2012
Gentry, C.: Fully homomorphic encryption using ideal lattices. In: Proceedings of the 41th Annual ACM Symposium on Theory of Computing, pp. 169–178. ACM (2009)
Graepel, T., Lauter, K., Naehrig, M.: ML Confidential: machine learning on encrypted data. In: Kwon, T., Lee, M.-K., Kwon, D. (eds.) ICISC 2012. LNCS, vol. 7839, pp. 1–21. Springer, Heidelberg (2013)
Halevi, S., Shoup, V.: Algorithms in HElib. In: Garay, J.A., Gennaro, R. (eds.) CRYPTO 2014. LNCS, vol. 8616, pp. 554–571. Springer, Heidelberg (2014). doi:10.1007/978-3-662-44371-2_31
Kaosar, M.G., Paulet, R., Yi, X.: Fully homomorphic encryption based two-party association rule mining, vol. 76, pp. 1–15. Elsevier (2012)
Kapoor, V., Poncelet, P., Trousset, F., Teisseire, M.: Privacy preserving sequential pattern mining in distributed databases. In: Proceedings of the 15th ACM international conference on Information and knowledge management, pp. 758–767. ACM (2006)
Khedr, A., Gulak, G., Vaikuntanathan, V.: Shield: Scalable homomorphic implementation of encrypted data-classifiers. In: IEEE Transactions on Computers. IEEE (2015)
Liu, J., Li, J., Xu, S., Fung, B.C.M.: Secure outsourced frequent pattern mining by fully homomorphic encryption. In: Madria, S., Hara, T. (eds.) DaWaK 2015. LNCS, vol. 9263, pp. 70–81. Springer, Heidelberg (2015). doi:10.1007/978-3-319-22729-0_6
Naehrig, M., Lauter, K., Vaikuntanathan, V.: Can homomorphic encryption be practical? In: Proceedings of the 3rd ACM Workshop on Cloud Computing Security Workshop, pp. 113–124. ACM (2011)
Qiu, L., Li, Y., Wu, X.: Protecting business intelligence and customer privacy while outsourcing data mining tasks. Knowl. Inf. Syst. 17(1), 99–120 (2009). Springer
Smart, N.P., Vercauteren, F.: Fully homomorphic encryption with relatively small key and ciphertext sizes. In: Nguyen, P.Q., Pointcheval, D. (eds.) PKC 2010. LNCS, vol. 6056, pp. 420–443. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13013-7_25
Smart, N.P., Vercauteren, F.: Fully homomorphic simd operations. Des. Codes Crypt. 71, 57–81 (2014). Springer
Tai, C.H., Yu, P.S., Chen, M.S.: k-support anonymity based on pseudo taxonomy for outsourcing of frequent itemset mining. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 473–482. ACM (2010)
Dijk, M., Gentry, C., Halevi, S., Vaikuntanathan, V.: Fully homomorphic encryption over the integers. In: Gilbert, H. (ed.) EUROCRYPT 2010. LNCS, vol. 6110, pp. 24–43. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13190-5_2
Wang, Y., Wu, X.: Approximate inverse frequent itemset mining: Privacy, complexity, and approximation. In: 5th IEEE International Conference on Data Mining (ICDM), pp. 482–489. IEEE (2005)
Acknowledgements
This work was supported by the CREST program of the Japan Science and Technology Agency. We would like to thank Mr. Takumi Takahashi, who implemented our scheme experimentally.
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Imabayashi, H., Ishimaki, Y., Umayabara, A., Sato, H., Yamana, H. (2016). Secure Frequent Pattern Mining by Fully Homomorphic Encryption with Ciphertext Packing. In: Livraga, G., Torra, V., Aldini, A., Martinelli, F., Suri, N. (eds) Data Privacy Management and Security Assurance. DPM QASA 2016 2016. Lecture Notes in Computer Science(), vol 9963. Springer, Cham. https://doi.org/10.1007/978-3-319-47072-6_12
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DOI: https://doi.org/10.1007/978-3-319-47072-6_12
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