Cryptographic indexing schemes for encrypted scientific databases
Shweta S. Aladakattishweta.aladakatti@manipal.eduSchool of Computer EngineeringManipal Institute of Technology BengaluruManipal Academy of Higher EducationBengaluru, Karnataka, 560064, IndiaView full profile → , Bondada Venkata Ramanaramanabondada999@gmail.comDepartment of Computer ScienceAditya Degree & PG CollegesKakinada, Andhra Pradesh, 533003, IndiaView full profile → , *Vijaysinh U. BansudeCorresponding authorvijubansude@gmail.comDepartment of Electronics & Telecommunication EngineeringS B Patil College of EngineeringIndapur, Pune, Maharashtra, 413106, IndiaView full profile → , Shwetambari Pandurang Katakeshwetambari.katake@vit.eduDepartment of Computer EngineeringVishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile → , M. Keerthanakeerthana1749@vardhaman.orgDepartment of Computer Science and Engineering (Data Science)Vardhaman College of EngineeringShamshabad, Hyderabad, Telangana, 501218, IndiaView full profile → , Subhash Chand Agrawalsubhash.agrawal@gla.ac.inDepartment of Computer Engineering & ApplicationsGLA UniversityMathura, 281406, Uttar Pradesh, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2026
- Published Online:
- 31 Oct 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-3004
- Pages:
- 3727–3738
Abstract
The spread of encrypted scientific information archives has posed an urgent need of retrieval systems that can maintain privacy of data and at the same time provide a pragmatic query efficiency. Traditional encryption algorithms ensure privacy, but come with extremely high-performance costs-making even large scientific databases computationally infeasible. This paper proposes a new Cryptographic Indexing Scheme (CIS) that is specifically designed to work with encrypted scientific database, it combines searchable symmetric encryption (SSE), secure hash-based multi-level indexing and lightweight partial homomorphic operations to facilitate complex structured and key-word querying. It has a hierarchical, leakage-resistant index, which is resistant to both access-pattern and inference attacks with a sub-linear search complexity. Assessment of large scale simulated genomic, climate and astrophysical data sets reveals that the proposed CIS is able to deliver 79.9 percent reduction in query response time, 73.1 percent less storage overhead, 97.3 percent retrieval precision and a security robustness score of 9.35/10 in comparison to current state of the art techniques. These findings support the idea that the proposed framework can be implemented in a real world secure scientific information management system.
Keywords
Subject Classifications
References
[1] E. Shmueli, R. Waisenberg, Y. Elovici, and E. Gudes, “Designing secure indexes for encrypted databases,” in Data and Applications Security,” Lecture Notes in Computer Science, vol. 3654, S. Jajodia and D. Wijesekera, Eds. Berlin, Heidelberg: Springer, pp. 54–68 (2005), doi: 10.1007/11535706_5.
[2] B. Lin, H. Du, P. Jin, and X. Fang, “PreIndex: A simple but efficient tree index for encrypted databases,” in Proc. 2nd Int. Conf. Sensing, Measurement, Communication and Internet of Things Technologies (SMC-IoT), Changsha, China, pp. 35–39 (2023), doi: 10.1109/SMC-IoT62253.2023.00014.
[3] M. Canim, M. Kantarcioglu, and B. Malin, “Secure management of biomedical data with cryptographic hardware,” IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 1, pp. 166–175 (Jan. 2012), doi: 10.1109/TITB.2011.2171701.
[4] F. Ren, Z. Zhang, K. Jiang, P. Zhang, and T. Yang, “Reversible data hiding and authentication scheme for encrypted image based on prediction error compression,” Scientific Reports, vol. 15, Art. no. 11636 (2025), doi: 10.1038/s41598-025-95433-9.
[5] M. Trabucco, M. Andreolini, and L. Ferretti, “Practical and secure history-independent indexing for queryable-encrypted databases,” Journal of Information Security and Applications, vol. 99, Art. no. 104439 (2026), doi: 10.1016/j.jisa.2026.104439.
[6] R. C. Poonia, T. Singh, C. K. Rajashri, D. Mondal, and S. M. Patil, “Optimizing communication system security through comprehensive testing and intelligent network configurations,” Journal of Information and Optimization Sciences, vol. 47, no. 5-A, pp. 1709–1717 (2026), doi: 10.47974/JIOS-2261.
[7] A K. Sharma, N. Purohit, S. Joshi, I. U. Lakkewar, and P. Khobragade, “Securing IoT environments: Deep learning-based intrusion detection,” in Deep Learning for Intrusion Detection, F. S. Masoodi and A. Bamhdi, Eds. Wiley (2026), doi: 10.1002/9781394285198.ch12.
[8] H. S. Sharma and A. Sharma, “Enhancing information retrieval through advanced query expansion techniques and semantic analysis,” Journal of Information and Optimization Sciences, vol. 46, no. 5, pp. 1587–1603 (2025), doi: 10.47974/JIOS-1839.
[9] V. Yesin, M. Karpinski, M. Yesina, V. Vilihura, R. Kozak, and R. Shevchuk, “Technique for searching data in a cryptographically protected SQL database,” Applied Sciences, vol. 13, no. 20, Art. no. 11525 (2023), doi: 10.3390/app132011525.
[10] M. Zhang, F. Shao, R. Zheng, M. Liu, and Z. Ji, “An efficient encryption scheme with fully hidden access policy for medical data,” Electronics, vol. 12, no. 13, Art. no. 2930 (2023), doi: 10.3390/electronics12132930.
[11] A. Patange, A. Gupta, G. M. Vaidya, M. Gulhane, V. M. Thorat, and S. Kumar, “Designing wearable AI systems for detecting stress and anxiety,” AIP Conference Proceedings, vol. 3426, no. 1, Art. no. 030109 (Jun. 2026).
[12] N. R. Nangare, M. Gulhane, M. Jujar Komal, N. Rakesh, M. B. Mane, and A. Gupta, “Predictive maintenance of biomedical devices using AI,” AIP Conference Proceedings, vol. 3386, no. 1, Art. no. 020114 (Mar. 2026).
