banner

Securing large-scale data processing: Integrating lightweight cryptography in MapReduce

Marwa Khadji, Samira Khoulji, Mohamed Larbi Kerkeb, Inass Khadji

Abstract


In today’s rapidly evolving digital landscape, the imperative of data security stands paramount. With the proliferation of sensitive information being stored and transmitted online, the necessity for robust encryption algorithms has grown exponentially. However, the suitability of traditional encryption methods in resource-constrained settings, like mobile devices and cloud computing, remains a concern due to their computational intensity. To address this, researchers have introduced a novel category of encryption algorithms known as lightweight cryptography algorithms. These cryptographic solutions are designed to offer robust security while minimizing computational demands, thus striking a harmonious balance between security and efficiency. While lightweight cryptography algorithms present a promising solution, their adequacy for applications demanding exceptionally high security, particularly within Big Data environments, warrants careful consideration. In this study, we presented a novel approach involving the utilization of lightweight cryptography algorithms within the MapReduce framework. By subjecting these algorithms to rigorous experimentation, we assessed their performance using software-oriented metrics from various dimensions.


Keywords


big data; Hadoop; stream ciphers; block ciphers; data security; MapReduce; lightweight cryptography algorithms

Full Text:

PDF

References


1. Marr B. How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read. Forbes, 2018.

2. Sharma, PP. Securing Big Data Hadoop: A Review of Security Issues, Threats and Solution. International Journal of Computer Science and Information Technologies (IJCSIT). 5, 2126-2131.

3. Khadji M, Kholji S, Bourekkadi S, et al. Sustainable MapReduce: Optimizing Security and Efficiency in Hadoop Clusters with Lightweight Cryptography-based Key Management. Bourekkadi S, Kerkeb ML, El Imrani O, et al., eds. E3S Web of Conferences. 2023, 412: 01065. doi: 10.1051/e3sconf/202341201065

4. Khadji M, Khoulji S and Larbi kerkeb M. Efficient Big Data Security: Evaluating The Performance Of A Proposed Hybrid Key Management Algorithm Using Lightweight Cryptography. Journal of Theoretical and Applied Information Technology. 2023, 101(13).

5. Zissis D, Lekkas D. Addressing cloud computing security issues. Future Generation Computer Systems. 2012, 28(3): 583-592. doi: 10.1016/j.future.2010.12.006

6. Liu L and Ma W. A Secure Hadoop Distributed File System Based on Hierarchical Encryption. IEEE Transactions on Services Computing. 2016, 9(3): 383-392.

7. Bhat R and Thakare AP. Big Data: A Comprehensive Survey. International Journal of Computer Applications. 2016, 139(11): 8-14.

8. Vavilapalli VK, Murthy AC, Douglas C, et al. Apache Hadoop YARN. Proceedings of the 4th annual Symposium on Cloud Computing. Published online October 2013. doi: 10.1145/2523616.2523633

9. Hadoop K, et al. Security at Scale with Apache Ranger. In: Proceedings of the ACM Symposium on Cloud Computing. 2014. pp. 13:1-13:16.

10. Baraani-Dastjerdi M and Buyya R. Securing Big Data in Cloud Computing Environment. Journal of Cloud Computing: Advances, Systems and Applications. 2017, 6(1): 6.

11. Stefanov E, et al. Seal: A Scalable, Elastic, and Adaptive Framework for Secure Cloud Computations. IEEE Symposium on Security and Privacy. 2012. pp. 455-470.

12. Dean J, Ghemawat S. MapReduce. Communications of the ACM. 2008, 51(1): 107-113. doi: 10.1145/1327452.1327492

13. Malladi S and Parthasarathy R. Securing Big Data Platforms. IEEE Security & Privacy. 2015. 13(4): 42-49.

14. Fonseca E, et al. Kerberos-Based Authentication for Hadoop. In: Proceedings of the IEEE International Conference on Cloud Computing. 2014. pp. 382-389.

15. Ahmed T, et al. Hadoop: A Comprehensive Security Review. Journal of Computer and System Sciences. 2017. 86: 78-91.

16. Hadoop K, et al. Security at Scale with Apache Ranger. In: Proceedings of the ACM Symposium on Cloud Computing. 2014. pp. 13:1-13:16.

17. Luk M, et al. Towards a Fine-Grained, Multi-Dimensional, and Dynamic Data Auditing Framework for Hadoop. IEEE Transactions on Services Computing. 2018, 11(5): 782-795.

18. Wilson B and Sathaye J. Security Design Considerations for Hadoop Deployments. In: Proceedings of the IEEE International Conference on Cloud Computing. 2012. pp. 50-57.

19. Mahajan D, et al. Big Data Security: A Survey and Research Directions. Journal of Big Data. 2019, 6(1): 28.

20. Stefanov E, et al. Seal: A Scalable, Elastic, and Adaptive Framework for Secure Cloud Computations. IEEE Symposium on Security and Privacy. 2012. pp. 455-470.

21. Ahmed T, et al. Hadoop Security at Rest: A Practical Guide to HDFS Encryption. Proceedings of the ACM Symposium on Cloud Computing. 2015. pp. 1-6.

22. Zissis D and Lekkas D. Addressing Cloud Computing Security Issues. Future Generation Computer Systems. 2012. 28(3): 583-592.

23. Apache HBase Reference Guide. Cell Level Security Encryption. Available online: https://hbase.apache.org/book.html#cell_security (accessed on 5 August 2022).

24. Khalil MJ, et al. Data Masking Security Challenges and Techniques: A Review. Computers & Security. 2020, 97: 101980.

25. Apache Project Rhino. Project Rhino - Secure Hadoop. Available online: https://cwiki.apache.org/confluence/display/RHINO/Project+Rhino (accessed on 5 August 2022).

26. Rancher RD, et al. Project Rhino: Adding New Security Features to the Hadoop Distributed File System. Proceedings of the IEEE Symposium on Security and Privacy Workshops. 2013. pp. 137-141.

27. Daemen J, Rijmen V. The Advanced Encryption Standard Process. The Design of Rijndael. Published online 2002: 1-8. doi: 10.1007/978-3-662-04722-4_1

28. Apache Project Rhino. Project Rhino-Secure Hadoop. Available online: https://cwiki.apache.org/confluence/display/RHINO/Project+Rhino. (accessed on 5 August 2022).

29. Ahmed T, et al. Hadoop Security at Rest: A Practical Guide to HDFS Encryption. Proceedings of the ACM Symposium on Cloud Computing. 2015. pp. 1-6.

30. Rancher RD, et al. Project Rhino: Adding New Security Features to the Hadoop Distributed File System. Proceedings of the IEEE Symposium on Security and Privacy Workshops. 2013. pp. 137-141.

31. Kadre V and Chaturvedi S. Secure Data Encryption Using Parallel Processing in HDFS. International Journal of Computer Applications. 2015, 124(6): 20-26.

32. Kumar KN and Rao MS. Efficient File Encryption and Decryption Using Hadoop Framework. International Journal of Advanced Research in Computer and Communication Engineering. 2014, 3(9): 7243-7249.

33. Lin HY, et al. Data Confidentiality for HDFS: Integrating AES with RSA and Pairing-Based Encryption. The Scientific World Journal. 2014, 2014: 1-9.

34. Liu H and Ge Y. Data Confidentiality and Integrity in HDFS. Journal of Convergence Information Technology. 2012, 7(15): 214-220.

35. Park S and Lee Y. A Secure Hadoop Architecture by Applying Encryption and Decryption Functions to the HDFS. International Journal of Distributed Sensor Networks. 2014, 10(6): 154320.

36. Song Y, et al. Secure HDFS Data Encryption Scheme with ARIA Algorithm. The Journal of Supercomputing. 2017. 73(1): 24-36.

37. Mahmoud H, Hegazy A, Khafagy MH. An approach for big data security based on Hadoop distributed file system. 2018 International Conference on Innovative Trends in Computer Engineering (ITCE). Published online February 2018. doi: 10.1109/itce.2018.8316608

38. Wang X, Zhang X, Yang Z. Performance evaluation of stream ciphers on large data sets. Journal of Information Security. 2015, 6(02): 43-49.

39. Shen J, Jiang W, Liu Y, et al. Performance evaluation of stream ciphers on real-time big data stream processing. Security and Communication Networks. 2017.

40. Kaushal S, Sondhi S. Performance evaluation of chacha20 encryption algorithm. International Journal of Computer Sciences and Engineering. 2019, 7(6), 429-433.

41. Sharma A, Jaiswal R, Srivastava N. Performance analysis of symmetric cryptography algorithms in IoT and cloud computing environment. Journal of Information Security and Applications. 2020, 50, 102421.

42. Zhang H, Li Y, Chen X, Hu X. A Comparative Analysis of Symmetric Cryptographic Algorithms. In Proceedings of the International Conference on Computational Science and Computational Intelligence. IEEE. 2016; pp. 575-579.

43. Liu M, Zhu Y, Li Y. Comparative Analysis of Symmetric Cryptography Algorithms in Cloud Storage. Journal of Physics: Conference Series. IOP Publishing. 2018, 1117(3), 032101.

44. Wang Y, Wang X, Liu Y. An Analysis of Encryption Algorithms for Big Data Security. In Proceedings of the 3rd International Conference on E-Business and Internet. ACM. 2019, pp. 196-200.

45. Chen J, Guo Z, Gao J, et al. A Comparative Study on the Performance of Symmetric Encryption Algorithms in Cloud Storage. Journal of Physics: Conference Series. IOP Publishing. 1662(3), 032021.




DOI: https://doi.org/10.32629/jai.v7i4.1390

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Marwa Khadji, Samira Khoulji, Mohamed Larbi Kerkeb, Inass Khadji

License URL: https://creativecommons.org/licenses/by-nc/4.0/