Privacy Preserving Classification over Encrypted Data Using Fully Homomorphic Encryption Technique

Abdullahi Monday Jubrin*, Victor Onomza Waziri**, Muhammad Bashir Abdullahi***, Idris Ismaila****
*,*** Department of Computer Science, Federal University of Technology, Minna, Nigeria, and Department of Computer Science, Veritas University, Abuja, Nigeria.
**,**** Department of Cyber Security Science, Federal University of Technology Minna, Nigeria.
Periodicity:April - June'2018
DOI : https://doi.org/10.26634/jdp.6.2.15590

Abstract

Applying Machine Learning to a problem which involves medical, financial, or other types of sensitive data needs careful attention in order to maintaining data privacy and security. This paper presents a model for privacy preserving classification and demonstrated that, by using a decision tree classifier, it is possible to perform a privacy preserving classification operation on an encrypted data residing on an untrusted server using the technique of Fully Homomorphic Encryption. First, the paper presented a model for the design and implementation of privacy preserving decision tree classifier over encrypted data. Also, Fully Homomorphic Encryption technique was used to secretly carry out classification on ciphertext using decision tree model built out of confidential medical data. The classifier was implemented using the SEAL homomorphic library and evaluation was done using encrypted medical datasets. The experimental results demonstrated high accuracy of the ciphertext classifier (when compared to the plaintext data equivalent) and efficiency (compared to other classifier on similar tasks). It takes less than 5 seconds (depending on the depth) to perform classification over an encrypted hepatitis feature vector dataset.

Keywords

Privacy preserving, Machine Learning, Algorithms, Helib, Homomorphic Encryption, Classification, Classifiers, RLWE, SEAL, Decision Tree

How to Cite this Article?

Jubrin, A. M., Abdullahi, M. B., Waziri, V. O., Ismaila, I. (2018). Privacy Preserving Classification Over Encrypted Data using Fully Homomorphic Encryption Technique. i-manager's Journal on Digital Signal Processing, 6(2), 36-47. https://doi.org/10.26634/jdp.6.2.15590

References

[1]. Acar, A., Aksu, H., Uluagac, A. S., & Conti, M. (2017). A survey on homomorphic encryption schemes: Theory and Implementation. arXiv preprint arXiv:1704.03578.
[2]. AdaPopa, R. (2014). Building practical systems that compute on encrypted data (Doctoral Dissertation, Massachusetts Institute of Technology).
[3]. Albrecht, M. R., Player, R., & Scott, S. (2015). On the concrete hardness of Learning with Errors. J. Math. Cryptol., 9(3), 169-203.
[4]. Ames, S., Venkitasubramaniam, M., Page, A., Kocabas, O., & Soyata, T. (2015). Secure health monitoring in the cloud using homomorphic encryption: A branching-program formulation. In Enabling Real-Time Mobile Cloud Computing through Emerging Technologies (pp. 116-152). IGI Global.
[5]. Barni, M., Failla, P., Kolesnikov, V., Lazzeretti, R., Sadeghi, A. R., & Schneider, T. (2009, September). Secure evaluation of private linear branching programs with medical applications. In European Symposium on Research in Computer Security (pp. 424-439). Springer, Berlin, Heidelberg.
[6]. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
[7]. Bonnoron, G., & Fontaine, C. (2006). A note on Ring- LWE security in the case of Fully Homomorphic Encryption. In: Patra A., Smart N. (Eds) Progress in Cryptology – INDOCRYPT 2017. INDOCRYPT 2017. Lecture Notes in Computer Science (Vol 10698). Springer, Cham.
[8]. Bos, J. W., Lauter, K., & Naehrig, M. (2014). Private predictive analysis on encrypted medical data. J. Biomed. Inform., 50, 234-243.
[9]. Bost, R., Popa, R., Tu, S., & Goldwasser, S. (2015). Machine Learning Classification over Encrypted Data. Ndss '15 (pp.1-31).
[10]. Brakerski, Z. (2012). Fully homomorphic encryption without modulus switching from classical Gap SVP. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 7417, 868-886.
[11]. Brakerski, Z., & Vaikuntanathan, V. (2011). Fully homomorphic encryption from Ring-LWE and security for key dependent messages. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 6841, 505-524.
[12]. Brakerski, Z., Gentry, C., & Vaikuntanathan, V. (2012). (Leveled) Fully Homomorphic Encryption without Bootstrapping. ACM Trans. Comput. Theory, 6(3), 1-36.
[13]. Carpov, S., Sirdey, R., Costantino, G., & Martinelli, F. (2017). Practical Privacy Preserving Medical Diagnosis t h using Homomorphic Encr yption. 2016 IEEE 9th International Conference on Cloud Computing (CLOUD) (pp. 593-599).
[14]. Chen, H., Laine, K., & Player, R. (2013). Simple Encrypted Arithmetic Library - SEAL V 2.1. In: Brenner M. et al. (Eds) Financial Cryptography and Data Security. FC 2017. Lecture Notes in Computer Science (Vol 10323). Springer, Cham.
[15]. Dijk, M. V., Gentry, C., Halevi, S., & Vaikuntanathan, V. (2010). Fully homomorphic encryption over the integers. Adv. Cryptology- EUROCRYPT '10 ( pp. 24-43).
[16]. Dowlin, N., Gilad-Bachrach, R., Laine, K., Lauter, K., Naehrig, M., & Wernsing, J. (2016). CryptoNets: Applying neural networks to Encrypted data with high throughput and accuracy - Microsoft research. Microsoft Res. TechReport, pp. 1-12.
[17]. Fan, J., & Vercauteren, F. (2012). Somewhat Practical Fully Homomorphic Encryption. Proc. 15th Int. Conf. Pract. Theory Public Key Cryptogr. (pp.1-16).
[18]. Gentry, C. (2009). Fully homomorphic encryption st using ideal lattices. Proc. 41st Annu. ACM Symp. Symp. theory Comput. - STOC '09. (p. 169).
[19]. Graepel, T., Lauter, K., & Naehrig, M. (2013). ML confidential: Machine learning on encrypted data. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 7839, 1-21.
[20]. Halevi, S., & Shoup, V. (2015). HElib_HElib Documentation.
[21]. HIPAA, (2014). Health Information Privacy_ HHS. U.S. Department of Health & Human Services.
[22]. Khedr, A., Gulak, G., & Vaikuntanathan, V. (2016). SHIELD: Scalable Homomorphic Implementation of Encrypted Data-Classifiers. IEEE Trans. Comput., 65(9), 2848-2858.
[23]. Kocabaş, Ö. (2016). Design and Analysis of Privacypreserving Medical Cloud Computing Systems (Doctoral Dissertation, University of Rochester).
[24]. Kocabas, O., Soyata, T., & Aktas, M. K. (2016). Emerging Security Mechanisms for Medical Cyber Physical System. IEEE/ACM Trans. Comput. Biol. Bioinforma., 13(3), 401-416.
[25]. Kumar, V. (2015). Data Mining and Knowledge Discovery Series. Chapman & Hall/CRC Press.
[26]. Lindner, R., & Peikert, C. (2011). Better key sizes (and Attacks) for LWE- based encryption, In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6558, 319-339.
[27]. Lyubashevsky, Peikert, C., & Regev, O. (2010). On ideal lattices and learning with errors over rings. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 6110(15848), 1-23.
[28]. Regev, O. (2005). On lattices, learning with errors, random linear codes, and cryptography. J. ACM, 56(6), 1- 40.
[29]. Schneider, T. (2009). Secure evaluation of private linear branching programs with medical applications. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 5789, 424-439.
[30]. Shouval, R., Bondi, O., Mishan, H., Shimoni, A., Unger, R., & Nagler, A. (2014). Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT. Bone Marrow Transplant, 49(3), 332-337.
[31]. Wu, D. J., Feng, T., Naehrig, M., & Lauter, K. (2016). Privately Evaluating Decision Trees and Random Forests. Proceedings on Privacy Enhancing Technologies, 2016(4), 335-355.
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