Articles
| Open Access |
ENHANCING PRIVACY IN CLOUD ANOMALY DETECTION WITH LIGHTWEIGHT HOMOMORPHIC ENCRYPTION
Khalil Ahamed , Computer Science & Software Engineering, School of Science, Rmit University, AustraliaAbstract
Anomaly detection is a critical component in ensuring the security and integrity of data in cloud computing environments. However, traditional anomaly detection methods often require data to be shared in unencrypted form, raising privacy concerns for cloud users. To address this challenge, this paper proposes a novel approach that enhances privacy in cloud anomaly detection using lightweight homomorphic encryption. The proposed method allows for the detection of anomalies in encrypted data without the need to decrypt it, thereby preserving the confidentiality of sensitive information. The study evaluates the effectiveness and efficiency of the proposed approach through experimentation and demonstrates its potential to provide privacy-preserving anomaly detection capabilities in cloud environments. By leveraging lightweight homomorphic encryption, cloud users can confidently utilize anomaly detection services while maintaining the confidentiality of their data.
Keywords
Anomaly detection, cloud computing, lightweight homomorphic encryption
References
C.C. Aggarwal, Data Mining – The Textbook, Springer, 2015.
K. Li, H. Jiang, L.T. Yang, A. Cuzzocrea (Eds.), Big Data – Algorithms, Analytics, and Applications, Chapman and Hall/CRC, 2015, http://www.crcnetbase.com/isbn/978-1-4822-4056-6.
R. Agrawal, A. Kadadi, X. Dai, F. Andrès, Challenges and opportunities with big data visualization, in: Proceedings of the 7th International Conference on Management of Computational and Collective IntElligence in Digital EcoSystems, Caraguatatuba, Brazil, October 25–29, 2015, 2015, pp. 169–173.
I.A.T. Hashem, I. Yaqoob, N.B. Anuar, S. Mokhtar, A. Gani, S.U. Khan, The rise of “big data” on cloud computing: review and open research issues, Inf.Syst. 47 (2015) 98–115, http://dx.doi.org/10.1016/j.is.2014.07.006.
Gvero, Cloud computing concepts, technology and architecture by Thomas Erl, Zaigham Mahmood and Ricardo Puttini, ACM SIGSOFT Softw. Eng.Notes 39 (4) (2014) 37–38, http://dx.doi.org/10.1145/2632434.2632462.
S. Subashini, V. Kavitha, A survey on security issues in service delivery models of cloud computing, J. Netw. Comput. Appl. 34 (1) (2011) 1–11, http://dx.doi.org/10.1016/j.jnca.2010.07.006.
D. Zissis, D. Lekkas, Addressing cloud computing security issues, Future Gener. Comput. Syst. 28 (3) (2012) 583–592, http://dx.doi.org/10.1016/j.future.2010.12.006.
K.W. Hamlen, M. Kantarcioglu, L. Khan, B.M. Thuraisingham, Security issues for cloud computing, Int. J. Inf. Secur. Priv. 4 (2) (2010) 36–48, http://dx.doi.org/10.4018/jisp.2010040103.
H. Kumarage, I. Khalil, Z. Tari, A.Y. Zomaya, Distributed anomaly detection for industrial wireless sensor networks based on fuzzy data modelling, J. Parallel Distrib. Comput. 73 (6) (2013) 790–806, http://dx.doi.org/10.1016/j.jpdc.2013.02.004.
J. Domingo-Ferrer, A provably secure additive and multiplicative privacy homomorphism, in: Information Security, 5th International Conference, ISC 2002 Sao Paulo, Brazil, September 30–October 2, 2002, Proceedings, 2002, pp. 471–483.
Article Statistics
Downloads
Copyright License
Copyright (c) 2023 Khalil Ahamed
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright and Ethics:
- Authors are responsible for obtaining permission to use any copyrighted materials included in their manuscript.
- Authors are also responsible for ensuring that their research was conducted in an ethical manner and in compliance with institutional and national guidelines for the care and use of animals or human subjects.
- By submitting a manuscript to International Journal of Computer Science & Information System (IJCSIS), authors agree to transfer copyright to the journal if the manuscript is accepted for publication.