Differential Privacy-Aware Data Sanitization for Multi-Level Security

Year : 2025 | Volume : 03 | Issue : 01 | Page : 42 52
    By

    Manas Kumar Yogi,

  1. Assistant Professor, Department of Computer Science and Engineering, Pragati Engineering College (A), Surampalem, Andhra Pradesh, India

Abstract

Multi-level security (MLS) models are fundamental for enforcing mandatory access control in
high-security environments such as government, military, healthcare, and finance. However, traditional MLS frameworks, including the Bell-LaPadula and Biba models, often create rigid data silos, preventing efficient data utilization. Differential privacy (DP) presents a novel solution by enabling controlled information leakage while preserving confidentiality. By injecting statistical noise into query results, DP allows lower-clearance users to access sanitized versions of higher-classified data
without violating security policies. This paper explores advancements in privacy-preserving MLS, emphasizing hybrid models that integrate DP with homomorphic encryption for enhanced security. Additionally, machine learning–driven adaptive noise mechanisms are discussed as a means to dynamically adjust privacy protections based on query sensitivity and user behavior. These innovations not only strengthen data security but also improve operational efficiency in multi-tiered access systems. We examine policy implications, advocating for DP-aware MLS frameworks in national security and intelligence-sharing contexts. Ethical concerns such as privacy-utility trade-offs, bias in DP mechanisms, and adversarial risks are also addressed. By balancing security and controlled data access, DP-enhanced MLS models offer a future-ready approach to secure data-sharing architectures, fostering both confidentiality and collaboration.

Keywords: Security, Bell-LaPadula model, differential privacy, encryption, malware

[This article belongs to International Journal of Computer Science Languages ]

How to cite this article:
Manas Kumar Yogi. Differential Privacy-Aware Data Sanitization for Multi-Level Security. International Journal of Computer Science Languages. 2025; 03(01):42-52.
How to cite this URL:
Manas Kumar Yogi. Differential Privacy-Aware Data Sanitization for Multi-Level Security. International Journal of Computer Science Languages. 2025; 03(01):42-52. Available from: https://journals.stmjournals.com/ijcsl/article=2025/view=203393


References

  1. Ghazi B, Golowich N, Kumar R, Manurangsi P, Zhang C. Deep learning with label differential privacy. Adv Neural Inform Process Syst. 2021; 34: 27131–27145.
  2. Wu X, Zhang Y, Shi M, Li P, Li R, Xiong NN. An adaptive federated learning scheme with differential privacy preserving. Future Generation Computer Syst. 2022; 127: 362–372.
  3. Jiang B, Li J, Yue G, Song H. Differential privacy for industrial internet of things: opportunities, applications, and challenges. IEEE Internet of Things J. 2021; 8 (13): 10430–10451.
  4. Zhao Y, Chen J. A survey on differential privacy for unstructured data content. ACM Comput Surveys. 2022; 54 (10s): 1–28.
  5. Yousefpour A, Shilov I, Sablayrolles A, Testuggine D, Prasad K, Malek M, Nguyen J, Ghosh S, Bharadwaj A, Zhao J, Cormode G. Opacus: user-friendly differential privacy library in PyTorch. arXiv preprint. arXiv:2109.12298. September 25, 2021.
  6. Li Z, Wang B, Li J, Hua Y, Zhang S. Local differential privacy protection for wearable device data. PLoS One. 2022; 17 (8): e0272766.
  7. Wang J, Han H, Li H, He S, Sharma PK, Chen L. Multiple strategies differential privacy on sparse tensor factorization for network traffic analysis in 5G. IEEE Trans Indus Informatics. 2021; 18 (3): 1939–1948.
  8. Jia B, Zhang X, Liu J, Zhang Y, Huang K, Liang Y. Blockchain-enabled federated learning data protection aggregation scheme with differential privacy and homomorphic encryption in IIoT. IEEE Trans Indus Informatics. 2021; 18 (6): 4049–4058.
  9. Ziller A, Usynin D, Braren R, Makowski M, Rueckert D, Kaissis G. Medical imaging deep learning with differential privacy. Sci Rep. 2021; 11 (1): 13524.
  10. El Ouadrhiri A, Abdelhadi A. Differential privacy for deep and federated learning: a survey. IEEE Access. 2022; 10: 22359–22380.
  11. Demelius L, Kern R, Trügler A. Recent advances of differential privacy in centralized deep learning: a systematic survey. ACM Comput Surveys. 2025; 57 (6): 1–28.
  12. Ponomareva N, Hazimeh H, Kurakin A, Xu Z, Denison C, McMahan HB, Vassilvitskii S, Chien S, Thakurta AG. How to dp-fy ML: a practical guide to machine learning with differential privacy. J Artif Intell Res. 2023; 77: 1113–1201.
  13. Adnan M, Kalra S, Cresswell JC, Taylor GW, Tizhoosh HR. Federated learning and differential privacy for medical image analysis. Sci Rep. 2022; 12 (1): 1953.
  14. Zhang W, Li X. Federated transfer learning for intelligent fault diagnostics using deep adversarial networks with data privacy. IEEE/ASME Trans Mechatron. 2021; 27 (1): 430–439.
  15. Jiang H, Pei J, Yu D, Yu J, Gong B, Cheng X. Applications of differential privacy in social network analysis: a survey. IEEE Trans Knowledge Data Eng. 2021; 35 (1): 108–127.
  16. Wylde V, Rawindaran N, Lawrence J, Balasubramanian R, Prakash E, Jayal A, Khan I, Hewage C, Platts J. Cybersecurity, data privacy and blockchain: a review. SN Computer Sci. 2022; 3 (2): 127.
Regular Issue Subscription Review Article
Volume 03
Issue 01
Received 26/02/2025
Accepted 28/02/2025
Published 10/03/2025
Publication Time 12 Days
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