INTERNATIONAL JOURNAL OF COMPUTER SCIENCE & INFORMATION
SYSTEM
Volume10 Issue01, January-2025, pg. 1-5
E-ISSN: 2536-7919
P-ISSN: 2536-7900
SJIF 2019: 4.58 2020: 5.046 2021: 5.328
2025, IJCSIS, https://scientiamreearch.org
pg. 1
Published Date: -
01-01-2025
ENHANCING CLOUD SECURITY: A LIGHTWEIGHT
HOMOMORPHIC ENCRYPTION APPROACH TO
ANOMALY DETECTION
Charlie Edwards
Computer Science & Software Engineering, School of Science, Rmit University,
Australia
Abstract: With the increasing adoption of cloud computing, ensuring data privacy and security has
become a critical challenge, particularly in the context of anomaly detection. This study proposes a
lightweight homomorphic encryption-based approach to enhance cloud security by enabling privacy-
preserving anomaly detection. Homomorphic encryption allows computations to be performed on
encrypted data without the need for decryption, ensuring that sensitive information remains
protected throughout the detection process. The paper explores the implementation of a lightweight
encryption scheme that balances both computational efficiency and strong security, making it suitable
for real-time anomaly detection in cloud environments. The proposed method is evaluated against
traditional encryption approaches, demonstrating its capability to detect anomalous behaviors
without exposing raw data to cloud service providers. Results indicate that the lightweight
homomorphic encryption method maintains high levels of accuracy in detecting anomalies while
ensuring minimal performance overhead, making it a promising solution for secure cloud-based
anomaly detection systems. This work contributes to advancing privacy-preserving techniques in cloud
security and paves the way for more secure and efficient cloud computing applications.
Keywords: Cloud security, Anomaly detection, Homomorphic encryption, Privacy-preserving,
Lightweight encryption, Data protection, Cloud computing, Secure anomaly detection.
INTRODUCTION
Cloud computing has revolutionized the way data is stored and processed, providing scalable and cost-
effective solutions for businesses and individuals. However, with the increased adoption of cloud services,
data security and privacy have become major concerns. Anomaly detection is a crucial aspect of data
security, enabling the identification of unusual patterns or behaviors that may indicate potential threats
or data breaches. Traditional anomaly detection methods often require data to be shared in unencrypted
form, exposing sensitive information to potential risks and privacy violations.
INTERNATIONAL JOURNAL OF COMPUTER SCIENCE & INFORMATION
SYSTEM
Volume10 Issue01, January-2025, pg. 1-5
E-ISSN: 2536-7919
P-ISSN: 2536-7900
SJIF 2019: 4.58 2020: 5.046 2021: 5.328
2025, IJCSIS, https://scientiamreearch.org
pg. 2
Published Date: -
01-01-2025
To address these privacy concerns, this paper proposes a novel approach that enhances privacy in cloud
anomaly detection through the use of lightweight homomorphic encryption. Homomorphic encryption is
a cryptographic technique that enables computations to be performed on encrypted data without the
need for decryption. By leveraging lightweight homomorphic encryption, cloud users can confidently
deploy anomaly detection services while ensuring the confidentiality of their data. This approach allows
for the detection of anomalies in encrypted data, preventing unauthorized access to sensitive information
and preserving the privacy of cloud users.
METHOD
The research methodology involves the following steps to evaluate the effectiveness and efficiency of the
proposed approach:
Selection of Anomaly Detection Algorithm:
A suitable anomaly detection algorithm is selected based on its compatibility with lightweight
homomorphic encryption. The chosen algorithm should be capable of processing encrypted data and
provide accurate anomaly detection results.
Implementation of Lightweight Homomorphic Encryption:
The selected anomaly detection algorithm is integrated with a lightweight homomorphic encryption
scheme. The implementation ensures that computations on encrypted data can be performed efficiently
without compromising the privacy of the cloud users.
Dataset Collection and Encryption:
A representative dataset containing both normal and anomalous data is collected. The dataset is then
encrypted using the lightweight homomorphic encryption scheme to preserve data confidentiality.
Privacy-Preserving Anomaly Detection:
The encrypted dataset is used to perform anomaly detection using the integrated algorithm and
lightweight homomorphic encryption. The process involves computations on encrypted data without
decrypting it, thereby maintaining privacy.
Performance Evaluation:
The performance of the proposed approach is evaluated in terms of accuracy, efficiency, and
computational overhead. A comparison is made with traditional anomaly detection methods that involve
data decryption to highlight the privacy benefits of the proposed approach.
Security Analysis:
INTERNATIONAL JOURNAL OF COMPUTER SCIENCE & INFORMATION
SYSTEM
Volume10 Issue01, January-2025, pg. 1-5
E-ISSN: 2536-7919
P-ISSN: 2536-7900
SJIF 2019: 4.58 2020: 5.046 2021: 5.328
2025, IJCSIS, https://scientiamreearch.org
pg. 3
Published Date: -
01-01-2025
A thorough security analysis is conducted to assess the robustness of the lightweight homomorphic
encryption scheme against potential attacks and vulnerabilities.
Experimental Validation:
The proposed approach is validated through experiments on real-world datasets to demonstrate its
capability to provide privacy-preserving anomaly detection in cloud computing environments.
By following this research methodology, the paper aims to demonstrate the potential of lightweight
homomorphic encryption in enhancing privacy in cloud anomaly detection. The proposed approach offers
cloud users a practical and secure solution to leverage anomaly detection services while safeguarding
their sensitive data from unauthorized access and privacy breaches.
RESULTS
The implementation of the proposed approach for enhancing privacy in cloud anomaly detection using
lightweight homomorphic encryption yielded promising results. The experimental evaluation
demonstrated that anomaly detection on encrypted data can be achieved efficiently and accurately
without compromising data privacy. The use of lightweight homomorphic encryption allowed for secure
computations on encrypted data, preventing unauthorized access to sensitive information.
DISCUSSION
The results highlight the effectiveness of the proposed approach in maintaining data privacy while
performing anomaly detection in cloud environments. By leveraging lightweight homomorphic
encryption, cloud users can confidently utilize anomaly detection services without exposing their sensitive
data in unencrypted form. This enhances the overall security and confidentiality of cloud-based anomaly
detection, addressing the privacy concerns associated with traditional methods.
Furthermore, the implementation of the lightweight homomorphic encryption scheme showed minimal
computational overhead, making it a practical solution for real-world cloud applications. The approach
efficiently handled the encryption and decryption processes, ensuring that anomaly detection can be
performed in a timely manner without compromising on accuracy.
The security analysis revealed that the lightweight homomorphic encryption scheme used in the proposed
approach demonstrated resilience against common cryptographic attacks. The encryption scheme
effectively protected the data and the intermediate results during the anomaly detection process,
providing an additional layer of security to prevent data breaches.
CONCLUSION
INTERNATIONAL JOURNAL OF COMPUTER SCIENCE & INFORMATION
SYSTEM
Volume10 Issue01, January-2025, pg. 1-5
E-ISSN: 2536-7919
P-ISSN: 2536-7900
SJIF 2019: 4.58 2020: 5.046 2021: 5.328
2025, IJCSIS, https://scientiamreearch.org
pg. 4
Published Date: -
01-01-2025
The research demonstrates that lightweight homomorphic encryption can be successfully utilized to
enhance privacy in cloud anomaly detection. By enabling computations on encrypted data, the proposed
approach ensures that cloud users' sensitive information remains confidential throughout the anomaly
detection process. This approach addresses the privacy concerns associated with traditional anomaly
detection methods that require data to be shared in unencrypted form.
The efficient performance of the lightweight homomorphic encryption scheme makes the proposed
approach practical and feasible for real-world cloud computing applications. It provides cloud users with
a secure and privacy-preserving solution for leveraging anomaly detection services without compromising
the confidentiality of their data.
Overall, the study contributes to the advancement of data security and privacy in cloud computing by
showcasing the potential of lightweight homomorphic encryption in anomaly detection. The proposed
approach offers a valuable tool for organizations and individuals seeking to enhance the privacy of their
data while utilizing cloud-based anomaly detection services. As cloud computing continues to evolve,
privacy-preserving techniques like lightweight homomorphic encryption will play a crucial role in ensuring
the secure and confidential processing of data in cloud environments.
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INTERNATIONAL JOURNAL OF COMPUTER SCIENCE & INFORMATION
SYSTEM
Volume10 Issue01, January-2025, pg. 1-5
E-ISSN: 2536-7919
P-ISSN: 2536-7900
SJIF 2019: 4.58 2020: 5.046 2021: 5.328
2025, IJCSIS, https://scientiamreearch.org
pg. 5
Published Date: -
01-01-2025
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