Cloud Based Secure File Sharing Using Access Control
Keywords:
File sharing, AES-256, SHA-256, Secure sharing, Software Requirement Specification.Abstract
In the current environment, data security is paramount, and any confidential files we must have with us everywhere we go raise the chance of losing such files. To prevent this inconvenient method of transporting private information, our suggested solution uses the cloud to store users' myriad of files of any size in encrypted form, encrypting them using the AES-256 version technique to ensure that your private data remains secret. The user no longer has to be concerned about security breaches when sharing his file with the intended recipient, since he may now provide permission to access his document to only the people he chooses, and it will only be accessible to him. With this approach, the saving and exchange of data is made possible. Our encryption algorithm uses the AES-256 version, in which all data is grouped into a string of bits and is subsequently converted into 128-bit blocks.
References
- Alam, M. S., Fernando, B. R., Jaoudi, Y., Yakopcic, C., Hasan, R., Taha, T. M., & Subramanyam, G. (2019, July). Memristor based autoencoder for unsupervised real-time network intrusion and anomaly detection. In Proceedings of the International Conference on Neuromorphic Systems (pp. 1-8). https://dl.acm.org/doi/pdf/10.1145/3354265.3354267
- Gunnam, V. G., Kilaru, N. B., & Cheemakurthi, S. K. M. (2022). Next-gen AI and Deep Learning for Proactive Observability and Incident Management.Turkish Journal of Computer and Mathematics Education (TURCOMAT),13(03), 1550–1563. https://doi.org/10.61841/turcomat.v13i03.14765
- Gunnam, V. G., Kilaru, N. B., & Cheemakurthi, S. K. M. (2022). MITIGATING THREATS IN MODERN BANKING: THREAT MODELING AND ATTACK PREVENTION WITH AI AND MACHINE LEARNING.Turkish Journal of Computer and Mathematics Education (TURCOMAT),13(03), 1564–1575. https://doi.org/10.61841/turcomat.v13i03.14766
- Vasa, Y., & Singirikonda, P. (2022). Proactive Cyber Threat Hunting With AI: Predictive And Preventive Strategies. International Journal of Computer Science and Mechatronics, 8(3), 30–36.
- Vasa, Y., Cheemakurthi, S. K. M., & Kilaru, N. B. (2022). Deep Learning Models For Fraud Detection In Modernized Banking Systems Cloud Computing Paradigm. International Journal of Advances in Engineering and Management, 4(6), 2774–2783. https://doi.org/10.35629/5252-040627742783
- Katikireddi, P. M. (2022). Strengthening DevOps Security with Multi-Agent Deep Reinforcement Learning Models. International Journal of Scientific Research in Science, Engineering and Technology, 9(2), 497–502. https://doi.org/https://doi.org/10.32628/IJSRSET2411159
- Mallreddy, S. R., & Vasa, Y. (2022). Autonomous Systems In Software Engineering: Reducing Human Error In Continuous Deployment Through Robotics And AI. NVEO - Natural Volatiles & Essential Oils, 9(1), 13653–13660. https://doi.org/https://doi.org/10.53555/nveo.v11i01.5765
- Belidhe, S. (2022b). Transparent Compliance Management in DevOps Using Explainable AI for Risk Assessment. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 8(2), 547–552. https://doi.org/https://doi.org/10.32628/CSEIT2541326
- Gunnam, V. G., Kilaru, N. B., & Cheemakurthi, S. K. M. . (2022). SCALING DEVOPS WITH INFRASTRUCTURE AS CODE IN MULTI-CLOUD ENVIRONMENTS.Turkish Journal of Computer and Mathematics Education (TURCOMAT),13(2), 1189–1200. https://doi.org/10.61841/turcomat.v13i2.14764
- Vasa, Y., & Mallreddy, S. R. (2022). Biotechnological Approaches To Software Health: Applying Bioinformatics And Machine Learning To Predict And Mitigate System Failures. Natural Volatiles & Essential Oils, 9(1), 13645–13652. https://doi.org/https://doi.org/10.53555/nveo.v9i2.5764
- Katikireddi, P. M., & Jaini, S. (2022). IN GENERATIVE AI: ZERO-SHOT AND FEW-SHOT. International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT) , 8(1), 391–397. https://doi.org/https://doi.org/10.32628/CSEIT2390668
- Nunnagupala, L. S. C. ., Mallreddy, S. R., & Padamati, J. R. . (2022). Achieving PCI Compliance with CRM Systems. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 13(1), 529–535.
- Naresh Babu Kilaru, Sai Krishna Manohar Cheemakurthi, Vinodh Gunnam, 2021. "SOAR Solutions in PCI Compliance: Orchestrating Incident Response for Regulatory Security"ESP Journal of Engineering & Technology Advancements 1(2): 78-84.
- Jangampeta, S., Mallreddy, S.R., & Padamati, J.R. (2021). Anomaly Detection for Data Security in SIEM: Identifying Malicious Activity in Security Logs and User Sessions. 10(12), 295-298
- Vasa, Y. (2021). Robustness and adversarial attacks on generative models. International Journal for Research Publication and Seminar, 12(3), 462–471. https://doi.org/10.36676/jrps.v12.i3.1537
- Kilaru, N. B., Cheemakurthi, S. K. M., & Gunnam, V. (n.d.). Advanced Anomaly Detection In Banking: Detecting Emerging Threats Using Siem. International Journal of Computer Science and Mechatronics, 7(4), 28–33.
- Naresh Babu Kilaru. (2021). AUTOMATE DATA SCIENCE WORKFLOWS USING DATA ENGINEERING TECHNIQUES. International Journal for Research Publication and Seminar, 12(3), 521–530. https://doi.org/10.36676/jrps.v12.i3.1543
- Gunnam, V., & Kilaru, N. B. (2021). Securing Pci Data: Cloud Security Best Practices And Innovations. Nveo, 8(3), 418–424. https://doi.org/https://doi.org/10.53555/nveo.v8i3.5760
- Katikireddi, P. M., Singirikonda, P., & Vasa, Y. (2021). Revolutionizing DEVOPS with Quantum Computing: Accelerating CI/CD pipelines through Advanced Computational Techniques. Innovative Research Thoughts, 7(2), 97–103. https://doi.org/10.36676/irt.v7.i2.1482
- Vasa, Y. (2021). Quantum Information Technologies in cybersecurity: Developing unbreakable encryption for continuous integration environments. International Journal for Research Publication and Seminar, 12(2), 482–490. https://doi.org/10.36676/jrps.v12.i2.1539
- Jangampeta, S., Mallreddy, S. R., & Padamati, J. R. (2021). Data Security: Safeguarding the Digital Lifeline in an Era of Growing Threats. International Journal for Innovative Engineering and Management Research, 10(4), 630-632.
- Singirikonda, P., Jaini, S., & Vasa, Y. (2021). Develop Solutions To Detect And Mitigate Data Quality Issues In ML Models. NVEO - Natural Volatiles & Essential Oils, 8(4), 16968–16973. https://doi.org/https://doi.org/10.53555/nveo.v8i4.5771
- Vasa, Y., Jaini, S., & Singirikonda, P. (2021). Design Scalable Data Pipelines For Ai Applications. NVEO - Natural Volatiles & Essential Oils, 8(1), 215–221. https://doi.org/https://doi.org/10.53555/nveo.v8i1.5772
- Sukender Reddy Mallreddy(2020).Cloud Data Security: Identifying Challenges and Implementing Solutions.JournalforEducators,TeachersandTrainers,Vol.11(1).96 -102.
- Vasa, Y. (2021). Develop Explainable AI (XAI) Solutions For Data Engineers. NVEO - Natural Volatiles & Essential Oils, 8(3), 425–432. https://doi.org/https://doi.org/10.53555/nveo.v8i3.5769
- Singirikonda, P., Katikireddi, P. M., & Jaini, S. (2021). Cybersecurity In Devops: Integrating Data Privacy And Ai-Powered Threat Detection For Continuous Delivery. NVEO - Natural Volatiles & Essential Oils, 8(2), 215–216. https://doi.org/https://doi.org/10.53555/nveo.v8i2.5770
- Kilaru, N. B., & Cheemakurthi, S. K. M. (2021). Techniques For Feature Engineering To Improve Ml Model Accuracy. NVEO-NATURAL VOLATILES & ESSENTIAL OILS Journal| NVEO, 194-200.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRCSEIT

This work is licensed under a Creative Commons Attribution 4.0 International License.